Search Results (966)

Metasurfaces, as emerging functional optical coatings, enable precise wavefront manipulation at the subwavelength scale. In this work, we propose a polarization-encoded switchable structured light generator based on a single-layer holographic metasurface composed of silicon nanopillars. By combining Fresnel holography technology and the Pancharatnam–Berry phase modulation principle, two sets of holographic phase distributions for perfect vortex beams corresponding to orthogonal circularly polarized states are superimposed onto a single metasurface. The optimized nanopillar achieves a transmittance of approximately 85% and a polarization conversion efficiency of around 98% at the wavelength of 632.8 nm. By simply adjusting the incident polarization state, the metasurface generates a perfect vortex beam under right-handed circularly polarized light illumination and a cylindrical vector beam under horizontal linearly polarized light illumination, enabling on-demand switching between the two structured light modes. Furthermore, we design and validate a spatially multiplexed perfect vortex beam generator, in which the radius, topological charge, and focal plane position of each channel can be independently controlled. This flexible thin-film metasurface platform offers a promising route toward compact, multifunctional photonic devices for advanced optical integration. Full article

To address class imbalance in transmission line fault traveling-wave samples, the strong non-stationarity of transient traveling-wave features, and the limited identification capability of single-representation methods, this paper proposes a fault type identification method that integrates Polar Lights Optimizer (PLO)-based feature selection with a Gramian Angular Field (GAF) attention fusion network. First, the Borderline Synthetic Minority Over-sampling Technique (Borderline-SMOTE) is applied to balance six fault categories in the training set, and time domain, frequency domain, time–frequency domain, and waveform-edge features are extracted from traveling-wave signals acquired by online monitoring devices. Then, PLO is used to select key explicit features, while the preprocessed traveling-wave sequences are encoded into dual-branch images using the Gramian Angular Summation Field (GASF) and the Gramian Angular Difference Field (GADF). Finally, a Gramian Angular Field–Parallel Convolutional Neural Network–Attention (GAF-PCNN-AT) model is constructed to fuse deep image features with selected explicit features for fault identification. Validation on the independent real test set under a representative stratified 8:2 split shows that the proposed method achieves an accuracy of 95.40% and an average area under the curve (AUC) of 0.9900 in the six-class fault identification task. The results indicate that the proposed method can effectively integrate deep image features of traveling-wave signals with PLO-selected explicit features, thereby providing high identification accuracy and good overall classification performance. Full article

Type IV composite hydrogen storage vessels must ensure structural safety under high internal pressure, and determining the optimal laminate configuration typically requires repetitive finite element analysis (FEA), leading to significant computational cost during the early design stage. To address this limitation, this study proposes a machine learning (ML)-based design-assistance framework for predicting the minimum laminate configuration required to satisfy structural safety based on the Tsai–Wu failure criterion. Three geometric design variables—liner radius, liner length, and polar hole radius—were used as inputs, and a dataset was generated using ANSYS Workbench-based FEA. Five ML models—SVR, GPR, RF, XGBoost, and MLP—were applied and evaluated using leave-one-out cross-validation (LOOCV) and an independent test set. All models achieved high predictive accuracy, with LOOCV R² values ranging from 0.9801 to 0.9907 and test-set R² values from 0.9866 to 0.9949, while maintaining MAPE below 6%. The consistent performance between LOOCV and the independent test set indicates stable predictive behavior. Learning curve analysis demonstrated stable convergence and a small performance gap between training and validation, suggesting stable predictive behavior within the considered design space. Feature importance analysis using the RF model identified the liner radius as the dominant parameter, while the influence of liner length was relatively minor. These results demonstrate that the proposed ML-based surrogate model can serve as an efficient tool for rapid design decision-making, reducing reliance on repetitive FEA in the early design stage. Full article

Purpose: To describe the morphological and functional features of two patients phenotypically and genotypically diagnosed with Best disease and autosomal dominant drusen (BD-ADD) using multimodal imaging. It is hypothesized that the concurrent presence of pathogenic mutations in both BEST1 and EFEMP1 genes is associated with a phenotype that may exhibit characteristic features of both diseases potentially revealing further additive effects on retinal function or structure. The data are compared with those from a patient with genetically confirmed ADD. Methods: The patients received a full ophthalmological investigation, including fundus autofluorescence (FAF) imaging, spectral-domain optical coherence tomography (SD-OCT) and polarization-sensitive optical coherence tomography (PS-OCT). Genetic analysis of DNA samples was performed by targeted whole-exome sequencing. Results: Macular SD-OCT demonstrated a thickened retinal pigment epithelium (RPE)-Bruch’s membrane complex corresponding to macular drusen-like deposits in both ADD and BD-ADD cases. In contrast to isolated ADD, BD-ADD showed small hyperautofluorescent dots originating in hyperreflective photoreceptor debris at or above the RPE on FAF imaging. FAF further showed large hyperautofluorescent spots corresponding to sub-RPE drusen in both ADD and BD-ADD. The tissue-specific contrast of PS-OCT imaging allowed identification of the RPE within the macular lesion, structural changes of the subretinal material and incipient scar formation. Genetic analysis identified pathogenic mutations in both the BEST1 and the EFEMP1 gene in both BD-ADD cases. Conclusions: The characteristic morphological and functional features of both diseases are evident in the patients with the BD-ADD dual genotype. The coexistence of two independent autosomal dominant disorders provides an illustrative opportunity to inform our understanding of BEST1- and EFEMP1-mediated retinal disease. Full article

Background/Objectives: Metabolic dysfunction-associated alcoholic liver disease (MetALD) is a serious worldwide health concern, exhibiting metabolic dysfunction-associated lipid accumulation, alcohol-associated oxidative damage, and endotoxin-induced inflammation. Rb1-enriched red ginseng saponin fraction (RGSF) has been known to exhibit anti-inflammatory and anti-oxidative properties, but its role in MetALD remains to be fully elucidated. This study aims to investigate the specific mechanism of RGSF in the MetALD mouse model. Methods: The MetALD mouse model was administered with or without Rb1-RGSF for 7 weeks. Histopathological and molecular analyses, along with primary cell isolation, were conducted for in vivo and ex vivo investigations. M1 macrophage polarization was assessed by analyzing pro-inflammatory cytokine expression. NF-kB/p65 and TLR4 protein expression were measured before being visualized using immunofluorescence assays and confocal microscopy. Results: Histopathological examination revealed that RGSF treatment markedly reduced hepatic steatosis and attenuated inflammatory lesions in MetALD independent of oxidative stress. Notably, RGSF administration suppressed the LPS-induced internalization of surface TLR4. During the early inflammatory phase, RGSF prevented the LPS-mediated loss of the 130 kDa TLR4 form at the cell membrane, thereby limiting the generation of its 110 kDa cytoplasmic form. LPS-binding assay confirmed the direct interactions between TLR4 and RGSF. Conclusions: Collectively, these findings demonstrate that RGSF regulates TLR4 expression and trafficking, leading to the suppression of M1 macrophage polarization by inhibiting LPS–TLR4 surface interactions, thus exhibiting hepatoprotective effects. Full article

Hafnium-zirconium oxide (HZO)-based ferroelectric field-effect transistors (FeFETs) are expected to become core devices for new embedded memory and compute-in-memory systems. However, existing simulations rely on finite-element-based TCAD tools, which are computationally intensive and time-consuming, and they struggle to account for the dynamic flipping of ferroelectric domains. This paper utilizes a time-domain simulation framework based on the nucleation-limited switching (NLS) model coupled with the surface potential of a MOSFET, enabling a self-consistent solution for polarization and electrical characteristics; a Monte Carlo method is employed to simulate device variability, and Shmoo plots are used to identify optimal programming and erasure process windows; an integrated solution is proposed for 22 nm FDSOI devices, addressing geometric scaling, modification of the Landau–Khalatnikov (L-K) dynamic model for ultrathin ferroelectric layers, and suppression of short-channel effects. Model validation is limited to selected operating metrics, and predictive accuracy outside the calibrated cases requires additional independent datasets. This method enables end-to-end simulation of FeFETs, from material polarization and device electrical characteristics to performance optimization, thereby providing model-based analytical and design support for the development of advanced, ultra-low-power FeFETs. Full article

As Artificial Intelligence (AI) evolves from passive tools into proactive actors within socio-technical systems, traditional social network theories face fundamental limitations in explaining AI’s structural power. Drawing on the Network Capabilities framework, this study investigates the mechanism of AI power generation within homogeneous communities from a structural hole perspective. This study analyzes a COVID-19 vaccine interaction network (N = 9314) on X via social network analysis, Propensity Score Matching (PSM), counterfactual simulations, and weighted Independent Cascade Model (ICM) dynamics. The results reveal that bot-like agents do not rely on traditional brokerage to acquire power; instead, they execute a Tight Integration strategy by filling micro-structural holes. After isolating the confounding effects of connection scale via rigorous Propensity Score Matching, it creates an anomalous high-density, high-constraint configuration, with these algorithmic agents exhibiting significantly higher network constraint (0.514) than comparable human users (0.453). Counterfactual removal experiments demonstrate a profound structural dependence of the social system on AI: their removal triggers a systemic cascade collapse, decreasing the largest connected component (LCC) size by a factor of 82.9 and topologically isolating 79.7% of human users. Furthermore, transitioning from static structural analysis to dynamic simulations, ICM simulations confirm AI’s topological redundancy translates into substantial information diffusion dominance (Cohen’s d = 1.081). Revealing AI’s power generation mechanism provides essential governance insights and strategic approaches for mitigating AI-driven information cocoons and group polarization. Full article

The Warburg effect is better understood as regulated metabolic plasticity rather than mitochondrial failure. Many malignant cells retain functional mitochondria while increasing aerobic glycolysis, lactate production, and redox remodeling to support growth, immune escape, and adaptation to microenvironmental stress. Within the context of the cancer microenvironment, this review examines xylitol as a hypothetical metabolic modifier within a broader host-microbe-mitochondria framework. Xylitol, a five-carbon sugar alcohol, is derived endogenously through the pentose phosphate pathway (PPP) and the glucuronate–xylulose pathway, and is metabolized efficiently in humans, rats, and pigs through xylitol dehydrogenase (XDH) in hepatic mitochondria and the cytosol; whereas, it is less tolerated by obligate carnivores who lack this enzyme. Preclinical studies show that partial substitution of glucose with xylitol can reduce proliferation and glycolytic markers in oral squamous carcinoma models, and preliminary studies link xylitol to glutathione depletion, endoplasmic reticulum (ER) stress, autophagy-associated death, and altered tumor metabolomics. On the other hand, oral pathogens such as Fusobacterium nucleatum and Porphyromonas gingivalis promote tumor stemness, extracellular vesicle signaling, metastasis, and immune evasion. In addition, Streptococcus mutans, the primary cariogenic pathogen, contributes to systemic bacteremia and epithelial–mesenchymal transition. Oral and gut microbiomes modulate macrophage polarization, T cell activity, and the senescence-associated secretory phenotype (SASP), possibly promoting cancer immune evasion. The anti-adhesive properties of xylitol may limit pathogen attachment to immune cell receptors, reducing the generation of pro-tumorigenic senescent immune cells. Xylitol also offers metabolic benefits, a low glycemic index, partial insulin-independent metabolism, and potential diabetes-prevention activity that are relevant, considering the established link between metabolic disease and cancer risk. A recent study reported that higher levels of endogenous xylitol were associated with adverse cardiovascular events, but confirmation of this requires large scale prospective studies. The evolutionary dietary context of MIS 6, during which hominin populations in sub-Saharan Africa depended on polyol-rich underground storage organs, provides a biological basis for human tolerance of xylitol. As a result, we hypothesize that xylitol may be a context-dependent metabolic modifier within an integrated host–microbe–mitochondria–cancer stem cell network. Full article

Automated detection of lunar impact craters from digital elevation model (DEM) data is important for lunar geological analysis, landing-site selection, and crater catalog updating. However, this task remains challenging because lunar craters exhibit large scale variations, weak or degraded rims, ambiguous boundaries, and complex topographic backgrounds. In addition, large-scale lunar remote sensing applications require detection models to achieve a reasonable balance among accuracy, model complexity, and inference efficiency. To address these challenges, this study proposes FPW-YOLO11n, a frequency-perception crater detection method developed based on YOLO11n. First, a Frequency-Directional Attention Module (FDA-Module) is introduced into the shallow stage of the backbone. This module combines frequency-aware channel attention and direction-aware spatial attention to enhance the representation of crater rim structures, elevation variations, and directional topographic cues in DEM data. Second, a C2PSA-LRSA module is designed by embedding Local Region Self-Attention into the C2PSA framework, thereby improving local contextual feature interaction while reducing the excessive cost associated with global self-attention. Third, Inner-WIoU is adopted to replace the original CIoU loss in YOLO11n. By combining the auxiliary-box mechanism of Inner-IoU with the sample-quality-aware weighting strategy of WIoU, Inner-WIoU provides a more flexible bounding-box regression objective for craters with weak rims, scale variations, and uncertain boundaries. A DEM-based lunar crater dataset was constructed from the Moon LRO LOLA–SELENE Kaguya TC DEM Merge 60N60S 59m product and the Robbins lunar crater catalog, covering the non-polar region from 60° S to 60° N and containing 4760 image tiles. Under the random data-splitting strategy, FPW-YOLO11n achieves 78.3% Precision, 66.2% Recall, 75.1% mAP@0.5, and 50.2% mAP@0.5:0.95, outperforming the YOLO11n baseline by 1.2, 2.0, 1.6, and 4.0 percentage points, respectively. Additional experiments based on geographically disjoint data splitting further show that the proposed method consistently performs better than YOLO11n on DEM data, indicating that the proposed structural improvements remain effective under a more rigorous spatially independent evaluation setting. Although the computational cost increases from 6.3 to 24.0 GFLOPs, FPW-YOLO11n maintains a compact parameter size of 2.59 M and a high inference speed, demonstrating an improved accuracy–efficiency trade-off for lunar crater detection from DEM data. Full article

Metalenses based on optical metasurfaces enable wavefront manipulation using subwavelength nanostructures and provide a promising route toward compact and integrated optical systems. However, strong chromatic aberration caused by wavelength-dependent phase responses remains a major obstacle for practical metalens applications in the visible regime. In this work, we present the design and analysis of an achromatic metalens operating in the visible spectrum using silicon nitride (Si₃N₄) dielectric metasurfaces. The metalens employs a phase-engineering strategy based on propagation-phase modulation of polarization-independent nanostructures. By constructing a unit-cell phase library through systematic parameter scanning, the phase responses at different wavelengths are accurately mapped. An interleaved arrangement strategy is introduced, where meta-atoms designed for different target wavelengths are alternately distributed within a single metalens aperture, enabling multi-wavelength phase compensation without increasing the structural complexity. Numerical simulations demonstrate that the proposed metalens achieves near-coincident focal positions across a broad visible-wavelength range. The focal length variation is significantly suppressed compared with conventional single-wavelength metalenses. The metalens exhibits stable focusing behavior with symmetric focal spots, consistent focal sizes, and improved chromatic tolerance. The results confirm that the interleaved design effectively mitigates chromatic focal shift while maintaining high transmission efficiency. This study provides a practical and scalable approach to achieving achromatic focusing in visible-wavelength metalenses. The proposed Si₃N₄-based interleaved design offers strong potential for compact imaging systems, integrated photonics, and visible-light optical devices. Full article

Background: Tight junctions are major components of apical junction complexes and are crucial for the maintenance of cell polarity, healthy tissue architecture, adhesion, and permeability. These junctions include the claudin family of transmembrane proteins, which act as paracellular barriers to regulate selective permeability. Abnormal claudin expression disturbs cell adhesions and is associated with cancer through promoting cell invasion, migration, and metastasis. Claudin 6 (CLDN6) overexpression, in particular, is linked to several types of cancer with malignant phenotypes. The present study aimed to investigate the association between CLDN6 protein expression and its copy number variations (CNVs) with clinicopathological features and survival outcomes of ovarian cancer (OC) patients. Methods: A total of 114 formalin-fixed paraffin-embedded blocks from primary OC patients were used to construct tissue microarray slides. Automated immunostaining was used to assess CLDN6 protein expression levels, and next-generation knowledge discovery platforms were used to further evaluate CLDN6 CNV levels using The Cancer Genome Atlas open-source data. The relationships between CLDN6 CNVs and tumor stage, overall survival, disease-specific survival (DSS), and disease-free survival (DFS) were investigated. Results: This study demonstrated that CLDN6 had a mixed membranous-cytoplasmic expression pattern. The cytoplasmic expression of CLDN6 was significantly associated with tumor stage (p = 0.05), tumor size (p = 0.04), and recurrence (p = 0.05). In Univariate analysis, Kaplan–Meier analysis demonstrated that CLDN6 expression was significantly correlated with DFS (p = 0.01). OC patients with lower cytoplasmic CLDN6 expression levels lived longer and had lower recurrence rates. These findings were further confirmed through CLDN6 CNVs analysis, where OC with lower CLDN6 cytoplasmic expression positively correlated with longer DFS and DSS. No independent prognosticator was found when using Cox-regression multivariate analysis (p > 0.05). Conclusions: These results suggest CLDN6 as an interesting prognosticator to identify OC patients at a higher risk of recurrence in order to provide personalized management, alleviate the burden of this disease on women’s health, and improve their survival outcomes. Full article

This article introduces the Evidence Propagation Calculus (EPiC), an operational framework for first-order reasoning built on a simple but productive observation: familiar inference patterns such as Modus Ponens and Modus Tollens behave like the movement of evidential markers across a structured graph. Positive evidence at an antecedent propagates forward to the consequent; negative evidence at a consequent propagates backward. When both markers coexist at a node, the system is locally inconsistent but not operationally broken. To make this observation precise, EPiC grounds reasoning in a four-valued evidential domain $\mathbb{V}=\{N,T,F,B\}$, where N denotes absence of evidence, T positive evidence, F negative evidence, and B their coexistence. Each logical connective is assigned a local evidential table, and inference is treated uniformly as the progressive restriction of admissible configurations under an evidential order: inadmissible values are eliminated, minimal surviving values are selected as the next effective evidential states, and the resulting restrictions propagate across shared variables. Compound formulas are decomposed into families of local unary and binary constraints through auxiliary variables, making the propagation process explicit and structurally uniform. Within this setting, Modus Ponens, Modus Tollens, and polarity-switching negation are not postulated as primitive rules. They emerge as derived consequences of the same local table calculus. The framework distinguishes different operational routes of justification. In some cases, positive support reaches the target formula directly through successive local restrictions. In others, propagation first stabilizes the relevant components and the target occurrence is then fixed by the corresponding connective table. Consistency is not a second basic notion of justification but a distinguished property of certain justified outcomes. The article establishes local and global soundness, conservativity over the classical fragment, and a conditional adequacy result. It further develops a translation between decomposed formulas and informational graphs, with a reverse reconstruction theorem for well-formed graphs. The result is a unified operational account of first-order reasoning situated between model-theoretic and proof-theoretic approaches, in which semantics, propagation, and graphical structure are mutually supporting rather than independently layered. Full article

Background/Objectives: Hormonal changes during the perimenopausal period are linked to alterations in lipid metabolism and increased cardiovascular disease risk. Dairy polar lipids (PLs), including phosphatidylcholine (PC), phosphatidylethanolamine (PE), and sphingomyelin (SM), have attracted growing interest for their potential effects on intestinal cholesterol absorption, lipoprotein metabolism, and circulating lipid concentrations. However, evidence on habitual dietary exposure to naturally occurring dairy PLs remains limited. The aim of the present study was to estimate habitual dietary exposure to dairy PLs by integrating season-specific dairy product composition with individual dietary intake data, and to examine whether this estimated exposure was associated with post-intervention plasma lipid outcomes in perimenopausal women in Latvia. Methods: The analysis included 61 perimenopausal women, comprising an intervention group (n = 31) and a control group (n = 30), who were further stratified into winter and spring cohorts. The intervention group consumed 250 mL day⁻¹ of fermented buttermilk for 28 days. Dietary intake was assessed using a food frequency questionnaire (FFQ). Season-specific concentrations of total PL, PC, PE, and SM were quantified in locally produced dairy products using LC-ESI-MRM-TQ-MS/MS and integrated with individual dietary intake data to estimate dairy PL exposure. Multivariable linear regression models were adjusted for baseline lipid concentrations, intervention group, season, and total dairy PL intake. Results: Estimated total dairy PL intake was positively associated with post-intervention HDL-C concentrations in the adjusted models (β = 0.059; 95% CI: 0.020–0.099), whereas no statistically significant associations were observed for total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), or triglycerides (TGs). After adjustment for baseline lipid concentrations, season, and estimated total dairy PL intake, intervention-group allocation was associated with higher post-intervention TC (β = 0.485; 95% CI: 0.023–0.948) and LDL-C (β = 0.330; 95% CI: 0.024–0.636) than in the control group. The spring-versus-winter season indicator was not independently associated with lipid outcomes. Conclusions: Estimated habitual dairy PL exposure was positively associated with post-intervention HDL-C concentrations but not with TC, LDL-C, or TGs. These findings do not support a clear lipid-lowering effect of the 28-day fermented buttermilk intervention in this secondary, exposure-based analysis. The results should be interpreted with caution, as dairy PL exposure was estimated from FFQ-derived intake data and season-specific product composition rather than measured directly. This trial was retrospectively registered in the ISRCTN registry ISRCTN11974930, registered on 25 June 2026. Full article

The calibration accuracy of key weather radar antenna parameters, including beam pointing, beamwidth, and antenna gain, directly affects quantitative precipitation estimation (QPE) and multi-radar network products. Conventional calibration approaches such as external field beacons and far-field tests are often constrained by site conditions and high implementation costs, making them difficult to apply routinely in operational radar networks. To address this limitation, this study proposes a robust solar calibration method for key antenna parameters of weather radars based on a dedicated Volume Coverage Pattern for Sun calibration, hereafter referred to as VCPSun. The proposed method uses a high-density solar scanning strategy with midpoint time alignment and feed-forward control of solar apparent motion. Combined with solar sample identification, propagation path correction, two-dimensional Gaussian surface fitting, and deconvolution of solar-source broadening and scan-smearing effects, the method enables reliability retrieval of beam pointing, beamwidth, and antenna gain. A high-frequency intensive observing experiment was conducted using a China New Generation Weather Radar, model SA-D (CINRAD/SA-D), deployed at the Changsha Meteorological Radar Calibration Center, with independent far-field test results used for validation. The results show that the retention rate of quality-controlled solar samples reached 85.7%, supporting stable reconstruction of the main-lobe power pattern. The retrieved mean beam pointing biases for both polarizations were within ±0.05°. After correction, the relative differences in beamwidth with respect to far-field measurements were respectively 3.26% and 1.52% for the H-polarization azimuth and elevation directions and 2.09% and 1.84% for the V-polarization azimuth and elevation directions, with the overall mean relative difference being less than 3.5%. The antenna gain differences relative to the independent far-field reference values were within 0.2 dB, at −0.062 dB for H-polarization and −0.144 dB for V-polarization. Comparative analysis with historical one-dimensional SunCheck records and an ablation test of the beamwidth correction chain further demonstrate that high-density two-dimensional sampling and physical deconvolution corrections improve the robustness and quantitative accuracy of the solar-based retrieval. These results demonstrate the feasibility of reliable in situ calibration of key antenna parameters for operational weather radars. The proposed method provides a potential technical pathway for in situ quantitative assessment of antenna performance in S-band CINRAD/SA-D radars, although further validation using additional radars and longer observation periods is required prior to network-wide application. Full article

The radiometric stability of satellite thermal infrared (TIR) channels is an indispensable prerequisite for the accurate retrieval of land surface temperature (LST) and the generation of reliable climate data records. This study evaluates the on-orbit radiometric calibration stability of the Fengyun-3D (FY-3D)/MEdium Resolution Spectral Imager-II (MERSI-II) TIR channels (channels 24 and 25) over four years (2021–2024) via a rigorous cross-calibration framework against Aqua/Moderate Resolution Imaging Spectroradiometer (MODIS). By imposing stringent spectral, spatial, temporal, and angular constraints to ensure the high fidelity of collocated pixel pairs, the cross-calibration results demonstrate that FY-3D/MERSI-II exhibits exceptional radiometric stability. Absolute brightness temperature biases are typically less than 0.1 K, with root mean square errors (RMSEs) limited to 1.20 K over a range of diurnal and seasonal conditions, demonstrating no noticeable systematic degradation. Furthermore, the downstream impact of this calibration on LST retrieval was quantified using the adapted National Oceanic and Atmospheric Administration Joint Polar Satellite System Enterprise algorithm. Validated against independent ground-based longwave radiation measurements collected from the Heihe Watershed Allied Telemetry Experimental Research network (HiWATER) and the Surface Radiation Budget Network (SURFRAD), the retrieved LST yielded overall biases of 0 K and −0.37 K, respectively, with RMSEs below 2.5 K. Cross-calibration demonstrates a limited and context-dependent impact on daytime LST, while the nighttime LST accuracy can be marginally improved using seasonal calibration coefficients derived from combined day/night matchups. Mechanistically, the integration of a soil directional emissivity model into the retrieval algorithm effectively mitigates viewing-zenith-angle (VZA)-induced uncertainties, systematically reducing biases by 0.12–0.20 K and RMSEs by 0.04–0.06 K. These findings confirm that the on-orbit radiometric calibration of FY-3D/MERSI-II meets scientific quality requirements and provide practical guidance for optimizing LST retrieval. Full article