Search Results (156)

Precise, spatially explicit sub-provincial GDP estimates are essential for regional planning, especially in mountainous areas where official economic data remain spatially coarse and unevenly distributed. This study develops a multisource county-level GDP spatialization framework for Sichuan Province, China, integrating corrected NPP/VIIRS nighttime-light (NTL) data with Points of Interest (POIs), land-use structure indicators (proportion of farmland (PFL); proportion of construction land (PCL)), elevation, precipitation, accessibility and population density within a unified indicator system. Two regression approaches—Ordinary Least Squares (OLS) as a global benchmark and Geographically Weighted Regression (GWR) as the spatially adaptive primary model—are calibrated on county-level cross-sectional data for 2020 (n = 183) and evaluated using R², adjusted R², AICc and residual spatial diagnostics. The multisource GWR model achieves R² = 0.882 (adjusted R² = 0.872, AICc = 5712.26), substantially outperforming both the global OLS benchmark (R² = 0.801) and NTL-only GWR baseline (R² = 0.662), confirming that spatial nonstationarity is an intrinsic feature of the GDP–proxy relationship and that integrating complementary geospatial proxies is the primary pathway to improved estimation accuracy in topographically heterogeneous regions. The GWR-based GDP surface exhibits a pronounced basin–plateau contrast: high-value clusters concentrate along the Chengdu Plain and adjacent city corridors, while extensive low-value zones prevail across the western highlands (global Moran’s I = 0.33, Z = 14.26, p < 0.001). Spatially varying GWR coefficients reveal that elevation and precipitation constrain GDP most strongly in high-altitude counties, construction land exerts a consistently positive but spatially graded effect, and the influences of accessibility and population density are context-dependent and locally differentiated. These findings support differentiated territorial development policies: plateau counties require accessibility-first strategies; hill counties benefit from targeted small-city industrialization; and basin cores need managed growth to balance agglomeration advantages against congestion pressures. The framework relies exclusively on globally or nationally available data and is portable to other mountainous regions, though cross-regional validation and extension to multi-year panels using geographically weighted panel regression remain important directions for future work. Full article

This study compared the kinematic and neuromuscular characteristics of the tennis forehand drive between high-performance (HP) and intermediate (INT) players. Eighteen right-handed male players (HP: n = 9; INT: n = 9) performed cross-court forehands while three-dimensional motion capture and surface electromyography (EMG) were recorded from the dominant upper limb and trunk. Kinematic and EMG data were time-normalized to the forward swing. One-dimensional statistical parametric mapping two-sample t-tests were used to compare joint angles, angular and linear velocities, and EMG amplitude waveforms between groups. Bonferroni-corrected significance levels were set at α = 0.0017 for kinematic variables and α = 0.0063 for EMG data. HP players exhibited greater racket linear velocity during the final part of the forward swing, accompanied by higher shoulder, elbow and wrist linear velocities, whereas hip linear velocity did not differ between groups. Joint angles were broadly similar, with SPM revealing only slightly greater early knee flexion in HP players. In contrast, HP players showed higher hip and knee angular velocities and greater wrist angular velocities in both flexion/extension and radial/ulnar deviation towards impact. EMG patterns were generally comparable, but HP players displayed higher biceps brachii activation in two significant clusters during the mid-to-late forward swing and greater triceps brachii activation in the late forward swing. No significant differences were observed for deltoid, pectoralis major, latissimus dorsi, flexor carpi radialis or extensor carpi radialis. These findings indicate that superior forehand performance in HP players is associated primarily with refined segmental coordination, greater lower-limb and distal segment velocities, and locally increased elbow muscle activation, rather than with widespread increases in upper-limb or trunk muscle activity. Full article

Quantitative microstructure evaluation of sedimentary rock thin sections is essential for revealing reservoir flow mechanisms and assessing reservoir quality. However, traditional manual identification is inefficient and prone to subjectivity. Although current deep learning approaches have improved efficiency, most remain confined to single tasks and lack a pathway to translate image recognition into quantifiable geological parameters. Moreover, these methods struggle with cross-scale feature extraction and accurate grain boundary localization in complex textures. To overcome these limitations, this study proposes a three-stage automated analysis framework integrating intelligent lithology identification, sandstone grain segmentation, and quantitative analysis of fabric parameters. To address scale discrepancies in lithology discrimination, Rock-PLionNet integrates a Partial-to-Whole Context Fusion (PWC-Fusion) module and the Lion optimizer, which mitigates cross-scale feature inconsistencies and enables accurate screening of target sandstone samples. Subsequently, to correct boundary deviations caused by low contrast and grain adhesion, the PetroSAM-CRF strategy integrates polarization-aware enhancement with dense conditional random field (DenseCRF)-based probabilistic refinement to extract precise grain contours. Based on these outputs, the framework automatically calculates key fabric parameters, including grain size and roundness. Experiments on 3290 original multi-source thin-section images show that Rock-PLionNet achieves a classification accuracy of 96.57% on the test set. Furthermore, PetroSAM-CRF reduces segmentation bias observed in general-purpose models under complex texture conditions, enabling accurate parameter estimation with a roundness error of 2.83%. Overall, this study presents an intelligent workflow linking microscopic image recognition with quantitative analysis of geological fabric parameters, providing a practical pathway for digital petrographic evaluation in hydrocarbon exploration. Full article

A spatially resolved investigation of bacterial inactivation using a radiofrequency (13.56 MHz) capacitively coupled plasma (RF CCP) discharge operating in ambient air at 4.0 mbar is presented. The plasma was generated in a parallel-plate reactor without external gas precursors and characterized using Langmuir probe diagnostics and optical emission spectroscopy (OES). Electron densities on the order of 109 cm³ were measured near the powered electrode, exhibiting pronounced axial and radial gradients across the discharge volume. OES revealed strong excitation of oxygen- and nitrogen-containing emitters, including O I (777 nm), N₂ s positive system (337–380 nm), and N₂⁺ first negative system features, with emission intensities increasing monotonically with applied RF power. The bactericidal performance was evaluated using Escherichia coli American Type Culture Collection (ATCC) 11775 exposed at different axial and radial positions within the reactor. At a fixed exposure time of 60 s, the log₁₀ reduction increased nonlinearly with RF power, rising from 0.29 at 20 W to 0.81 at 40 W, followed by a sharp transition to the assay reporting ceiling (≥2.95-log₁₀ under the adopted half-count correction) at 50 W and above. Time-resolved measurements at 50 W demonstrated rapid inactivation kinetics, with measurable reductions occurring within 5–10 s and reaching the reporting ceiling within 60 s. In contrast, samples positioned at the chamber periphery or approximately 20 cm from the discharge center exhibited negligible inactivation, confirming strong spatial localization of the biocidal effect. These results identify a threshold-like operating regime in which increased discharge intensity produces rapid inactivation in the plasma core while remaining strongly position dependent. The findings establish medium pressure, air-based RF CCP as an efficient, gas-free, and spatially controllable platform for localized surface decontamination under non-thermal conditions. Full article

Lung cancer is one of the most common causes of death from cancer around the world, and catching it early through computed tomography (CT) scans can drastically improve survival. However, automated classification of pulmonary nodule candidates is hard because images do not all have the same intensity across scanners and protocols, resulting in inconsistent performance, more false positives (FP), and a ceiling on how much deep learning models work in an average clinic. In this work, we tackle this by introducing a preprocessing step that corrects intensity differences before feeding images into classification models. We use Contrast-Limited Adaptive Histogram Equalization (CLAHE), but with its key parameters tuned automatically via a modified version of the Covariance Matrix Adaptation Evolution Strategy (CMA-ES). This helps to boost local contrast adaptively, keeps important anatomical details intact, and cuts down on noise. We tested the approach on the public LUNA16 dataset, first checking image quality (Peak Signal-to-Noise Ratio (PSNR) around 53 dB and Structural Similarity Index (SSIM) of 0.9, better than standard methods), then training three popular deep models—namely, ResNet-50, EfficientNet-B0, and InceptionV3—with CutMix augmentation for better generalization. On the enhanced images, ResNet-50 achieved up to 99.0% classification accuracy with substantially less FP than when using the raw scans. Taken together, these results demonstrate that intelligent and optimized preprocessing can effectively mitigate intensity variations via deep learning for lung nodule detection, thus coming closer to realizing the practical toolbox of computer-aided diagnosis in routine clinical practice. Full article

Background/Objectives: Understanding the relationship between reactogenicity and immunogenicity after repeated BNT162b2 vaccination is critical for optimizing vaccination strategies. This study quantified their progressive dissociation across four vaccine doses. Methods: We conducted a prospective longitudinal cohort study among Croatian healthcare workers vaccinated with BNT162b2 from January 2021 to January 2024. Anti-SARS-CoV-2 IgG antibodies were measured at 16 timepoints using chemiluminescent immunoassay. Local (pain, erythema, swelling) and systemic (fever, fatigue, headache, myalgia, arthralgia, nausea) reactions were recorded for 7 days using FDA toxicity scale. Correlations were analyzed with Spearman’s method and Bonferroni correction. Fourth-dose responses were predicted by exponential modeling. Results: Of 631 participants, 524 completed primary immunization, 418 received a third dose (173 with complete data), and 56 received a fourth dose (22 with complete paired data). Local reactions declined from 82.4% after the first dose to 42.9% after the fourth (p < 0.001). Systemic reactions peaked at 44.8% after the second dose, then decreased to 26.0% after the third and 19.6% after the fourth. In contrast, median antibody levels rose from 9910 AU/mL after the primary series to 29,002 AU/mL after the third and 38,274 AU/mL after the fourth. Correlations between reactions and antibody titer progressively weakened: r = 0.37 (95% CI 0.29–0.44, p < 0.001) after the primary series, r = 0.08 (95% CI −0.07 to 0.23, p = 0.30) after the third, and r = 0.04 (95% CI −0.39 to 0.45, p = 0.86) after the fourth dose. Conclusions: Progressive dissociation between reactogenicity and immunogenicity was observed across four BNT162b2 doses. Booster doses maintain robust antibody responses despite reduced reactogenicity, reinforcing that minimal side effects are consistent with sustained humoral responses. Full article

Traditional image feature matching methods often fail to achieve satisfactory performance on multimodal remote sensing images (MRSIs), mainly due to significant nonlinear radiometric distortion (NRD) and complex geometric deformation caused by different imaging mechanisms. The key to successful MRSI matching lies in preserving high-frequency edge structures that are robust to geometric deformation, while overcoming nonlinear intensity mappings induced by NRD. To address these challenges, this paper proposes a novel high-precision matching framework, termed structure-aware feature descriptor with multi-scale side window filtering (SA-SWF). The proposed framework consists of three stages: (1) an anisotropic morphological scale space is constructed based on multi-scale side window filtering to strictly preserve geometric edges, and feature points are extracted using a multi-scale adaptive structure tensor with sub-pixel refinement to ensure high localization precision; (2) a structure-aware feature descriptor is constructed by integrating gradient reversal invariance and entropy-weighted attention mechanisms, rendering the multi-modal description highly robust against contrast inversion and noise; and (3) a coarse-to-fine robust matching strategy is established to progressively refine correspondences from descriptor-space matching to strict sub-pixel geometric verification, thereby minimizing alignment errors. Experiments on 60 multimodal image pairs from six categories, including infrared-infrared, optical–optical, infrared–optical, depth–optical, map–optical, and SAR–optical datasets, demonstrate that SA-SWF consistently outperforms seven state-of-the-art competitors. Across all six dataset categories, SA-SWF achieves a 100% success rate, the highest average number of correct matches (356.8), and the lowest average root mean square error (1.57 pixels). These results confirm the superior robustness, stability, and geometric accuracy of SA-SWF under severe radiometric and geometric distortions. Full article

Underwater imaging is generally subject to complex degradation issues such as color distortion, contrast degradation, and detail blurring due to the selective absorption and scattering of light wavelengths by water. Existing deep learning methods have limitations in the collaborative optimization of local details and global color. To address this issue, this paper proposes a multi-scale enhancement network based on global and local feature fusion. By integrating the advantages of CNN and Transformer, it achieves joint optimization of global color correction and local detail enhancement. Specifically, MGLFNet extracts global and local features of the image through the global and local feature fusion block in the core component of the multi-scale convolution–Transformer block and performs dynamic fusion. Meanwhile, to extract features at different scales to enhance performance, we design a multi-scale convolution feed-forward network. Through the action of the fusion module and the feed-forward network, a color-rich and detail-clear enhanced image is obtained. A large number of experimental results show that MGLF-Net outperforms comparison methods in both qualitative and quantitative evaluations of visual quality, with PSNR and SSIM values of 25.37 and 0.918 on the UIEB dataset, respectively, as well as low memory usage and computational resource requirements. In addition, detailed ablation experiments prove the effectiveness of the core components of the model. Full article

Timber price dynamics are most often analysed using trends, seasonality, and classical measures of volatility, which describe the magnitude of fluctuations but only to a limited extent capture the temporal structure of the price-generating process. The aim of this study is to identify the structural complexity and long-term memory of quarterly prices of WC0 pine timber in the regional timber market in Poland. The analysis is based on nominal net prices (PLN/m³) from 16 forest districts of the Regional Directorate of State Forests in Kraków over the period 2005–2024, with reference to nationally averaged timber prices. Long-term dependence is assessed using the Hurst exponent estimated by detrended fluctuation analysis (DFA) applied to log returns, while the geometric complexity of price trajectories is characterised by the fractal dimension and additionally validated using the Higuchi estimator. Cross-sectional results reveal substantial spatial heterogeneity in scaling properties, indicating the coexistence of persistent (trend-following) and corrective (anti-persistent) dynamics across forest districts. Rolling-window analysis (40 quarters) demonstrates temporal variability in price dynamics, with particularly pronounced shifts observed in 2019–2021. Cluster analysis based on time-varying Hurst exponent values identifies two groups of forest districts with distinct persistence trajectories, corresponding to more trend-dominated and corrective price dynamics. In contrast, national-level prices generally exhibit higher persistence than local prices, reflecting the effects of price aggregation. Overall, the results show that fractal analysis uncovers persistent spatial and temporal differences in timber price structures that remain invisible when relying solely on variance-based measures, with direct implications for the choice of planning horizons and timber sale strategies in regional markets. Full article

The easily defanged myth that baby rattlesnakes (genera Crotalus and Sistrurus) are more dangerous than adults has persisted in North America despite all evidence to the contrary. The most often cited reason for the babies-more-dangerous (BMD) myth is the venom-dump (VD) hypothesis: babies, in contrast to adults, cannot control how much venom they expend, and therefore inject all of it when biting. We undertook three approaches to explore the origin, transmission, and prevalence of this myth and its most frequent explanation. First, we examined historical newspaper accounts. From 130 newspaper stories mentioning the relative danger of baby rattlesnakes, we identified a timeline in which (1) most stories prior to 1969 were factually correct; (2) the BMD myth and VD hypothesis likely originated in the mid-to-late 1960s and became entrenched in California, especially, from 1970 to 1999; (3) factually incorrect statements subsequently prevailed throughout North America from 2000 to 2014; and (4) factually correct stories regained prominence with apparent effective messaging success from 2015 onward. We further learned that general information stories about rattlesnakes, more often citing subject experts like university professors, were much more likely to provide accurate information than local snakebite stories, which more often cited health professionals (e.g., physicians, veterinarians, pharmacists) and emergency responders (e.g., police and fire officers) who frequently supplied misinformation. Second, we surveyed familiarity with the BMD myth and VD hypothesis among 53 university classrooms (including one high school) representing 3751 students across 29 states within the United States. Consistent with the California media’s outsized influence on misinformation transmission, familiarity with the myth was greatest in the southwestern states (52.6%) and declined moving north and east, with the least familiarity in the northeastern states (16.4%). Third, a small survey of 75 emergency responders and health professionals from Southern California revealed that a whopping 73.3% actually believed the BMD myth. Numerous organizations generally regarded as authoritative further amplified the misinformation, especially on the internet, where some content persists to this day. Unfortunately, belief in the BMD myth and VD hypothesis can lead to negative consequences, including misinformed risk-taking by those encountering snakes, unwarranted fear among snakebite victims, and inappropriate care delivered by misinformed or patient/family-pressured medical professionals. Our findings target health professionals and emergency responders as priority audiences for education. Full article

Background: Hepatocellular carcinoma (HCC) stands as a prevalent global health issue with increasing incidence and mortality rates. Hepatocellular carcinoma (HCC) exhibits profound molecular and clinical heterogeneity, which limits the effectiveness of current therapeutic strategies. Circadian rhythm disruption has been implicated in metabolic reprogramming, proliferation, and immune modulation in cancer, but its role in shaping HCC heterogeneity remains poorly defined. Methods: Four public HCC transcriptomic cohorts (TCGA-LIHC, CHCC, LIRI, LICA) were integrated using RMA normalization and ComBat for batch correction. Consensus clustering based on 31 core circadian clock genes (CCGs) identified robust molecular subtypes. Multi-omics characterization—including genomic alterations, pathway activity (GSEA/GSVA), immune microenvironment profiling (CIBERSORT, EPIC, MCP-counter, xCell), and drug-sensitivity prediction (pRRophetic/oncoPredict)—was performed to delineate subtype-specific biological properties. A nine-gene CCG-based RiskScore model was constructed using LASSO Cox regression to internally validate subtype robustness and intra-subtype risk stratification. Results: Using consensus clustering of 31 core CCGs in TCGA-LIHC and three independent validation cohorts (CHCC, LIRI, LICA), we identified three reproducible subtypes—Cluster-1 (metabolic–quiescent), Cluster-2 (transition–intermediate), and Cluster-3 (proliferation–inflammatory)—which were recapitulated across cohorts and showed distinct overall survival (Cluster-3 worst; log-rank p values significant across datasets). Multi-omic characterization revealed that Cluster-3 exhibits the highest tumor mutational burden and CNV burden with enrichment of TP53/AXIN1/TERT alterations, strong activation of cell-cycle, E2F, and G2M programs, and an immune-hot yet immunosuppressed microenvironment enriched for TAMs, Tregs and MDSCs. By contrast, Cluster-1 shows relative genomic stability, dominant hepatic metabolic signatures (fatty-acid oxidation, bile-acid and xenobiotic metabolism) and an immune-cold phenotype. Single-cell mapping linked ALAS1 expression to malignant hepatocytes predominating in Cluster-1, whereas NONO and CSNK1D localized to stromal (CAFs/TECs) and both malignant/immune compartments respectively in Cluster-3, providing a cellular mechanism for subtype-specific metabolism, angiogenesis and immune modulation. Finally, a nine-gene CCG-based RiskScore validated prognostic stratification and drug-sensitivity predictions indicated subtype-specific therapeutic vulnerabilities (notably increased predicted TKI sensitivity in Cluster-3). Conclusion: In conclusion, this study proposes a robust circadian rhythm-based molecular classification of hepatocellular carcinoma, revealing three biologically and clinically distinct subtypes characterized by divergent genomic alterations, metabolic programs, immune microenvironment states, and prognostic patterns. By integrating bulk and single-cell transcriptomic data, we identify subtype-specific roles of key circadian regulators—including ALAS1, NONO, and CSNK1D—in shaping tumor metabolism, proliferation, stromal remodeling, and immune suppression. These findings highlight circadian dysregulation as a potential upstream factor associated with HCC heterogeneity and provide a conceptual framework for developing subtype-tailored mechanistic studies and circadian-informed therapeutic strategies. Full article

The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) employs a 532 nm laser with strong water-penetration capability, making it well suited for satellite-derived bathymetry in shallow waters; however, the effective denoising of photon-counting data remains essential due to strong solar background and intrinsic instrument noise. To address this challenge, this study proposes a novel photon denoising method, termed the Directional Nearest Neighbor Distance-based Algorithm (DNNDA), for robust extraction of signal photons from shallow-water ICESat-2 data. Unlike existing methods that rely heavily on density or terrain features and often degrade under high-noise conditions, DNNDA systematically exploits both scale-corrected spatial relationships and directional distribution characteristics of photons. By quantitatively characterizing the directional features of photon distributions and embedding this information into a density representation, DNNDA amplifies the density contrast between signal and noise photons, rendering the seafloor signal photons more distinct and easier to extract. An evaluation index was further designed to automate optimal parameter determination. Validation using multiple global ICESat-2 datasets demonstrates that DNNDA achieves superior seafloor photon extraction performance, with F1-scores exceeding 95%. Further regression analysis against high-precision CUDEM data in the Puerto Rico region yields root-mean-square errors below 0.57 m. By jointly correcting scale anisotropy and incorporating directional information, DNNDA enables reliable and adaptive signal photon extraction across local and global scales, providing a robust solution for shallow-water bathymetry in complex, high-noise environments. Full article

Transverse shear deformation plays a non-negligible role in lightweight periodic-core structures and motivates the use of shear-corrected reduced-order plate and beam models. However, the shear correction factor $k_s$ is often treated as a constant despite its strong dependence on cross-sectional heterogeneity and geometry. This work quantifies the global sensitivity of $k_s$ in corrugated paperboard by combining an energy-consistent pixel-based identification of the effective shear stiffness $\left(GA{)}_{\mathrm{e}\mathrm{f}\mathrm{f}}\right.$ with a space-filling exploration of the parameter domain. Representative three-ply (single-wall) and five-ply (double-wall) configurations are generated directly in the pixel domain using sinusoidal fluting descriptions and non-overlapping liner bands. The effective shear stiffness is obtained from a heterogeneous shear-energy equivalence, where a normalized two-dimensional shear-stress shape function is computed from pixel-based sectional descriptors and integrated with spatially varying shear moduli. Latin Hypercube Sampling is employed to explore wide ranges of flute period, height, and thickness, liner thicknesses, and liner–flute shear-modulus contrasts. Global sensitivity is reported using unit-free normalized indices, including log-elasticities (based on the slope of $\ln k_s$ versus $\ln x$) and partial rank correlation coefficients. The results demonstrate that flute geometry is the primary driver of $k_s$ variability, while material contrast significantly modulates shear-energy localization, particularly in double-wall boards with two distinct flutings. The proposed framework enables high-throughput shear correction assessment and supports robust parameterized reduced-order models for corrugated structures. Full article

Soil heterogeneity and acidity are major constraints to Coffea arabica production in the Amazonian soils of Peru. This study developed a spatial predictive framework that integrates a weighted Soil Quality Index (SQIw) and geostatistical modelling (Regression–Kriging and Ordinary Kriging) to estimate lime requirements (LRs) and delineate management zones. A total of 69 coffee-cultivated soil samples were analysed, and spectral information (NDVI) was incorporated to estimate relative yield (RR). Multivariate analysis defined a Minimum Data Set (MDS) composed of exchangeable Na, available P, pH and silt percentage; the highest weights were assigned to P (Wi = 0.292) and pH (Wi = 0.276). SQIw exhibited wide variability (0.01–0.87; CV = 51.8%) and was grouped into five classes, with low (43.5%)- and very low (21.7%)-quality classes predominating. SQIw showed a strong relationship with RR (r = 0.64). Geostatistical models performed differently between localities: in Nuevo Huancabamba, Regression–Kriging improved prediction accuracy (SQIw: R² = 0.58; LR: R² = 0.396), whereas in San José de Sisa, Ordinary Kriging provided better fits only for LRs (R² = 0.32). Nuevo Huancabamba is dominated by moderate-to-high-quality soils (87.29%; SQIw > 0.6) and low lime requirements (74.94%; <0.84 t ha⁻¹), in contrast with San José de Sisa, where low-quality soils prevail (89.45%; SQIw < 0.4) alongside high LRs (75.26%; 2.54–7.13 t ha⁻¹). The resulting maps enable targeted interventions—precision liming and focused P fertilisation—to correct acidity and phosphorus deficiency, thereby improving input-use efficiency and enhancing the sustainability of Amazonian coffee systems. Full article

This study proposes a clinically driven hybrid segmentation framework for dense breast tissue analysis in mammographic images, addressing persistent challenges associated with intensity inhomogeneity, low-contrast, and complex tumor morphology. The framework integrates Multiplicative Intrinsic Component Optimization (MICO_2D) for bias field correction, followed by a distance-regularized multiphase Vese–Chan level-set model for coarse global tumor segmentation. To achieve precise boundary delineation, a localized refinement stage is employed using Localized Active Contours (LAC) with Local Image Fitting (LIF) energy, supported by Gaussian regularization to ensure smooth and coherent boundaries in regions with ambiguous tissue transitions. Building upon the refined semantic tumor mask, the framework further incorporates a panoptic-style tumor instance segmentation stage, enabling the decomposition of connected tumor regions into distinct anatomical instances, which were evaluated on both MIAS and INBreast mammography datasets to demonstrate generalizability. This extension facilitates detailed structural analysis of tumor multiplicity and spatial organization, enhancing interpretability beyond conventional pixel wise segmentation. Experiments conducted on Cranio-Caudal (CC) and Medio-Lateral Oblique (MLO) mammographic views demonstrate competitive performance relative to baseline U-Net and advanced deep learning fusion architectures, including multi-scale and multi-view networks, while offering improved interpretability and robustness. Quantitative evaluation using overlap-related metrics shows strong spatial agreement between predicted and reference segmentations, with per-image Dice Similarity Coefficient (DSC) and Intersection over Union (IoU) distributions reported to ensure reproducibility. Descriptive per-image analysis, supported by bootstrap-based confidence intervals and paired comparisons, indicates consistent performance improvements across images. Robustness analysis under realistic perturbations, including noise, contrast degradation, blur, and rotation, demonstrates stable performance across varying imaging conditions. Furthermore, feature space visualizations using t-SNE and UMAP reveal clear separability between cancerous and non-cancerous tissue regions, highlighting the discriminative capability of the proposed framework. Overall, the results demonstrate the effectiveness, robustness, and clinical motivation of this hybrid panoptic framework for comprehensive dense breast tumor analysis in mammography, while emphasizing reproducibility and conservative statistical assessment. Full article