Search Results (425)

Accurate defect instance segmentation is essential for automated quality inspection of piezoelectric crystals, where scratches, stains, and other surface defects often show large scale variation, irregular morphology, and weak contrast against complex backgrounds. Existing CNN-based segmentation models capture local texture effectively but are limited in global context modeling, whereas Transformer-based models often require stronger local detail preservation for fine industrial defects. To address these limitations, this paper proposes a hybrid bidirectional bridging CNN–Transformer network, named HBCTNet, for piezoelectric crystal defect instance segmentation. HBCTNet introduces a Convolution-Transformer Bidirectional Bridging Block (CTB) that enables lightweight two-way interaction between convolutional local features and Transformer global representations. A Frequency and Spatial Convolution (FSC) module is designed to enhance defect-related details in both spatial and frequency domains, and a Multi-Scale Feature Pyramid Network (MS-FPN) fuses features across scales. On the reported image partition of the self-constructed dataset, HBCTNet-S obtains 92.3% box AP and 89.9% mask AP with 12.67 M parameters and 48.1 G FLOPs. Its mask AP is 2.1 percentage points higher than YOLO11-SEG and 2.4 percentage points higher than YOLOv9-SEG under the reported comparison. These results support the effectiveness of the architecture within the evaluated dataset; inference speed and cross-domain generalization remain to be assessed. Full article

We develop a design-conditional limit theory for kernel estimators of conditional U-functionals based on locally stationary functional random fields observed at irregular random locations and under incomplete response observation. The covariates take values in a separable Hilbert space, the responses are allowed to take values in a general Polish space, and the target is indexed by a class of symmetric kernels of a fixed order. Functional localization is induced by single-index semi-metrics, while spatial localization is performed on the rescaled observation domain. Missing responses are incorporated through a complete-case construction under a Missing At Random condition and a uniform-positivity assumption. The resulting estimator is a ratio of spatially weighted U-statistics with random tuplewise observation indicators. The asymptotic analysis must account simultaneously for four sources of complexity: dependence within the spatial field, nonstationarity across an expanding domain, concentration in an infinite-dimensional covariate space, and the random thinning generated by missing responses. Conditioning on the sampling locations removes the randomness of the spatial design weights but does not eliminate dependence among the observations. We therefore derive a design-conditional projection decomposition adapted to the triangular-array structure of the model. The leading component is represented by a spatially dependent complete-case empirical process, whereas the higher-order canonical terms are controlled uniformly over the response kernels, functional-target points, single-index directions, and rescaled spatial locations. The proofs combine stationary tangent-field approximations for locally stationary random fields, large-block–small-block decompositions, coupling arguments under spatial absolute regularity, small-ball probability estimates, and entropy bounds for the joint indexing class. These arguments yield a uniform stochastic expansion in which the empirical fluctuation, the spatial–functional smoothing bias, and the local-stationarity approximation error appear as distinct contributions. In particular, the local-stationarity remainder has no counterpart in the strictly stationary theory and quantifies the cost of replacing the observed nonstationary field with its stationary tangent approximation. Under the MAR and positivity conditions, complete-case sampling reduces the effective local information and modifies the covariance structure, but it does not change the formal order of the uniform-convergence rate. Under strengthened moment, mixing, entropy, and negligibility conditions, we establish weak convergence of the normalized conditional U-process in the corresponding supremum-norm function space to a tight centered Gaussian process. The limiting covariance is determined by the complete-case first-order projection and consequently retains the effect of the observation propensity and the spatial dependence structure. We also introduce a complete-case leave-tuple-out spatial prediction criterion for bandwidth selection and prove oracle optimality over admissible bandwidth families. The general theory applies to conditional rank association, discrimination probabilities, set-indexed conditional distribution functionals, and related pairwise statistical-learning criteria. Simulation experiments and applications to spatial environmental and epidemiological data illustrate the finite-sample implications of the theory and the stabilizing role of single-index localization. Viewed through the lens of data-driven science, the framework addresses a fundamental asymmetry between the information carried by irregular, locally heterogeneous functional covariates and the selectively observed response tuples. By combining design conditioning, complete-case normalization, tangent-field localization, and single-index dimension reduction, the proposed approach resolves this inferential asymmetry at the level of the model by matching estimation and uncertainty quantification to the information actually available locally, without imposing artificial stationarity or complete-data symmetry. Full article

Background and Objectives: Delayed contour irregularities may become apparent years after otherwise successful autologous rhinoplasty, particularly in patients with thin nasal skin. Thin-skinned rhinoplasty, autologous graft behavior, and nasal aging have been studied individually, but their potential interaction remains unclear. This review examines current evidence on age-related changes in the nasal soft-tissue envelope, thin nasal skin, autologous graft behavior, and their potential effects on long-term rhinoplasty outcomes. Materials and Methods: A structured narrative review was conducted using PubMed as the primary database, with supplementary searches of Google Scholar and reference lists. Four predefined search domains addressed thin nasal skin, nasal aging, autologous graft behavior, and preservation and camouflage techniques. The PubMed searches identified 463 records. After duplicate removal and title and abstract screening, 205 publications were retained for further assessment. Additional relevant publications were identified through supplementary searches. Evidence was synthesized qualitatively because of differences in study design, surgical technique, follow-up duration, and outcome assessment. Results: Thin nasal skin is associated with increased visibility of underlying structural features after rhinoplasty. Aging is associated with changes in skin thickness and elasticity, soft-tissue volume, and the underlying nasal framework. These findings provide a possible basis for changes in soft-tissue camouflage over time, but direct longitudinal evidence linking aging to delayed graft visibility is lacking. Late contour changes may also result from graft warping or resorption, scar remodeling, or changes in the native nasal framework. The ability of preservation and camouflage techniques to prevent aging-related delayed contour visibility has not been established. Conclusions: Age-related changes in the nasal soft-tissue envelope may contribute to delayed contour visibility after rhinoplasty. However, direct longitudinal evidence showing that these changes increase the visibility of an otherwise stable autologous graft is lacking. This relationship should therefore be considered a hypothesis rather than an established causal mechanism. Prospective longitudinal studies using objective measurements of soft-tissue thickness, graft morphology, and external nasal contour are needed to test this hypothesis. Full article

Accurate brain tumor magnetic resonance imaging (MRI) segmentation is essential for objective lesion assessment and treatment planning, yet it remains challenging because tumors exhibit large-scale variations, irregular morphologies, heterogeneous internal textures, and indistinct boundaries. Conventional U-Net variants commonly improve either spatial feature extraction or skip connection fusion, but they do not jointly address the adaptive representation of pathological texture changes and the semantic filtering of noisy shallow features. To bridge this gap, a texture-span and frequency-gated U-Net (TSF U-Net) is proposed, establishing a collaborative spatial and frequency-domain feature-learning framework. Within this framework, a texture-span gated multi-scale (TSGM) block is developed by integrating parallel multi-scale context extraction with a pathological texture-span gating mechanism derived from local maximum and minimum feature responses. This design enables the encoder to emphasize heterogeneous tumor regions and boundary-related variations without requiring additional boundary supervision. Moreover, a discrete cosine frequency-gated fusion (DCT-FGB) block is introduced into the skip connections. It employs deep semantic features to generate top-down frequency-domain gating masks for shallow skip features, allowing useful structural information to be retained while reducing background and noise interference before decoder fusion. On the Brain Tumor Dataset, TSF U-Net achieves an mIoU of 86.471%, a Recall of 87.424%, an F1-score of 84.693%, and a Dice coefficient of 0.819, improving the U-Net baseline by 1.243 percentage points in mIoU and 1.645 percentage points in F1-score. It also achieves the highest mIoU of 88.882% on the BRICS 2025 Dataset. Ablation results confirm the complementary contributions of TSGM and DCT-FGB. The bidirectional cross-dataset experiments yield mIoU values of 77.369% and 70.881%, providing initial evidence of transferability under dataset distribution shifts. These findings indicate that jointly modeling pathological texture variations in the spatial domain and semantically selecting skip features in the frequency domain provides an effective strategy for improving boundary sensitivity, noise resistance, and cross-dataset robustness in brain tumor MRI segmentation. Full article

Grounded in structural biomimetics, this study extracts the multi-segmented tapered geometry from the 10-segmented lamellate antenna of Allomyrina dichotoma and maps it to microwave antenna design. Through biological characterization and parametric modeling, key geometric features—multi-segmented configuration, irregular contour, and bilateral symmetry—were extracted. Along the 2D pathway, a 10 × 10 × 1 mm³ PCB microstrip antenna was designed and fabricated, achieving 111% fractional bandwidth from 4.86 to 17.05 GHz with a peak gain of 2.15 dBi and a radiation efficiency of 67–72% across the operating band, plus two additional bands at 24.76–28.58 GHz and 32.66–37.73 GHz. The multiple resonance valleys on S₁₁ curves and frequency-dependent surface current evolution indicate that multi-mode resonant coupling, perimeter increment, and symmetric aperture efficiency together underpin the ultra-wideband performance. Along the 3D pathway, a dipole antenna replicated via metallic 3D printing attains an electrical length of 0.17λ, with 66% bandwidth and 1.45 dBi gain, confirming the same geometric principle in a shape-preserving form. The 2D route favors planar integration and bandwidth, while the 3D route offers extreme miniaturization. This work provides experimental validation of cross-domain geometric mapping from biology to electromagnetics within structural biomimetics, and offers engineering evidence for the intrinsic versatility of this morphology across physical domains. Full article

Background/Objectives: Skull vibration-induced nystagmus (SVIN) is a robust sign of vestibular asymmetry, yet its relationship with tests of impulsive canal function, short-latency otolithic reflexes, and sustained visuo-vestibular processing remains incompletely defined. To describe four clinical–physiological cases in which SVIN was dominant or disproportionate relative to the canal, otolithic-reflex, and sustained-domain profiles, and to propose a temporal-domain interpretation of this physiological non-congruence. Methods: Four patients with persistent disequilibrium, episodic vertigo, or selective vestibular-reflex dissociation underwent multidomain neuro-otological assessment, including bedside examination, video head impulse test/head impulse paradigm (vHIT/HIMP), suppression head impulse paradigm (SHIMP) when available, cervical and ocular vestibular-evoked myogenic potentials (cVEMPs and oVEMPs), rotatory or caloric testing when available, and optokinetic or other visuo-vestibular paradigms. SVIN was elicited by 100-Hz mastoid vibration and characterized by direction, dimensionality, stimulation-site dependence, reproducibility, and slow-phase velocity when available. Results: SVIN remained clinically informative across patients with preserved, minimally abnormal, or selectively dissociated canal and otolithic-reflex findings. The recurring feature was not normality of all conventional tests, but physiological non-congruence between the vibration-induced ocular response and the canal, otolithic-reflex, or sustained-domain profile. One patient showed normal six-canal vHIT and symmetric VEMPs despite an exceptionally large fixed-direction SVIN; another showed selective cVEMP absence with preserved oVEMPs and normal vHIT. Atypical multidimensional SVIN coexisted with marked optokinetic directional asymmetry in the adolescent case. Conclusions: SVIN is not a surrogate for vHIT, VEMPs, caloric, rotatory, or velocity-storage-related testing. It should be interpreted as a distinct phase-locked probe of vibration-sensitive vestibular imbalance. Irregular afferents provide the supported physiological framework, whereas dimorphic afferents represent a plausible, hypothesis-generating bridge population capable of contributing to otherwise unexplained clinicophysiological dissociations. Full article

Small- and medium-sized landslides frequently occur in clusters and exhibit fragmented morphologies, irregular boundaries, and spectral characteristics similar to surrounding roads, bare soil, and sparsely vegetated surfaces, making their automated extraction from remote sensing imagery challenging. Although the Segment Anything Model (SAM) provides strong general-purpose segmentation capabilities, its direct application to landslide mapping is limited by the geoscience domain gap and its dependence on external prompts. This study proposes the Asymmetric Boundary-aware Segment Anything Model (AB-SAM), a parameter-efficient adaptation of SAM for automated landslide semantic segmentation. AB-SAM integrates three task-specific components. First, the offline Multi-Feature Variation-Guided Prompting (MF-VGP) module generates cached auxiliary bounding boxes from registered pre- and post-event images without accessing ground-truth masks. Second, the Asymmetric Feature Augmentation (AFA) strategy combines geometric perturbation, CutMix, and asymmetric dual-branch supervision, in which a Hint-free branch serves as the primary optimization pathway and a lower-weight box-guided branch provides auxiliary spatial supervision. Third, the Boundary-Aware Morphological Prompting (BAMP) module injects trainable boundary-aware morphological information into the largely frozen SAM image encoder. During validation, testing, and application, only the Hint-free branch is retained, enabling inference using post-event imagery without external point, box, or mask prompts. On the fixed, spatially disjoint Zixing test set, AB-SAM achieved an overall accuracy of 96.171%, a precision of 68.149%, a recall of 60.011%, an F1-score of 63.822%, a landslide-class Intersection over Union of 46.867%, and a mean Intersection over Union of 71.452%. Repeated experiments with three random seeds showed low run-to-run variation. Direct evaluation without retraining on the Hokkaido Iburi-Tobu dataset yielded a mean Intersection over Union of 66.136%, providing evidence of cross-region and cross-event transferability. These results demonstrate that AB-SAM provides a practical parameter-efficient framework for automated, hint-free landslide segmentation, although further evaluation across additional regions, sensors, and landslide-size distributions remains necessary. Full article

Universal health coverage requires simultaneous progress in service coverage and financial protection, yet these dimensions are commonly assessed separately. We conducted a secondary ecological panel analysis using official World Health Organization/World Bank indicators. A document-oriented workflow preserved irregular subgroup structures before conversion to an unbalanced country–year panel comprising 981 observations from 159 countries/economies between 2000 and 2023. The primary inferential specification was a two-way fixed-effects model with country and year effects and country-clustered standard errors; a country fixed-effects model with a linear time trend, a random-intercept model, and generalized estimating equations were complementary robustness analyses. We also examined service domains, wealth- and rural-urban inequalities, beta convergence, and exploratory country typologies. Mean service coverage rose from 58.9 in 2000 to 74.0 in 2023, whereas mean financial hardship fell from 24.0% to 17.3%. In the primary model, each one-point increase in service coverage was associated with a 0.441-percentage-point reduction in hardship (95% confidence interval, −0.707 to −0.175; p = 0.001). Mean poorest–richest and rural–urban hardship gaps were 53.7 and 12.5 percentage points, respectively. The inverse association was largest in low-income settings, where socioeconomic gradients were also steepest. Service coverage gains should therefore not be interpreted as sufficient evidence of equitable universal health coverage; monitoring systems and financing reforms should jointly target access, financial protection, and distributional gaps. Full article

Objectives: This study aimed to investigate the sociodemographic factors influencing oral hygiene habits and oral health-related quality of life (OHRQoL) among complete denture wearers at Kuwait University Dental Clinic (KUDC). Methods: A cross-sectional observational study was conducted using a self-administered questionnaire and an Oral Health Impact Profile-19 (OHIP-19) instrument to assess oral hygiene habits and OHRQoL in 61 complete denture patients, treated by dental undergraduate students at KUDC, between January 2019 and December 2023. Additionally, a clinical assessment of denture hygiene was performed using the Budtz-Jörgensen denture plaque index for all patients (n = 61). Sociodemographic, general health status, oral hygiene habits, and OHRQoL-related data were collected. Results: A total of 37.7% of included participants were aged 51–60 years, with 75.4% being male. Most participants (81.96%) had a high school education or less. Caries was reported as the primary cause of tooth loss (83.6%), and 77.04% recorded irregular dental visits. Only 46.05% of participants demonstrated optimal and/or good denture hygiene. Toothpaste was the most common cleaning method (79.21%), and 70.62% removed their dentures at night. A significant association was found between education level and denture wearing (p = 0.035), as well as between gender and the frequency of dental visits (p = 0.024). However, no significant relationship was observed between gender and denture hygiene (p = 0.52). The OHIP-19 results revealed that poor denture hygiene impacted OHRQoL across multiple domains, including functional limitation, physical pain, psychological discomfort, and social disability. Conclusions: Unsatisfactory denture hygiene was prevalent among participants, possibly due to limited awareness or inadequate instructions provided by dental health practitioners. Education level and gender influenced certain aspects of denture care, highlighting the need for targeted oral health education programs and improved patient guidance from dental practitioners. Additionally, poor denture hygiene was associated with reduced OHRQoL, emphasizing the importance of addressing both oral hygiene practices and their psychosocial impacts to improve overall well-being. Full article

Global path planning for autonomous orchard robots must balance obstacle clearance, path smoothness, and trajectory trackability. This balance is difficult to achieve in unstructured orchards, where irregular tree rows, scattered trunks and ground obstacles, and narrow inter-row passages can cause conventional planners to generate low-clearance paths with frequent local turns. This study proposes SDF-Theta*, a safety- and smoothness-aware global path planning framework for orchard robots in unstructured environments. The framework constructs a Euclidean signed distance field (ESDF) from a two-dimensional occupancy planning map and defines a safe navigable domain using the robot width and a grid-discretization approximation margin. Within this domain, the bidirectional SDF-Theta* search performs candidate-node screening, applies Safe LOS checks along candidate connection segments, and uses safety–geometry multi-criteria state selection based on minimum clearance, mean clearance, path length, and turning cost. The resulting initial discrete path is processed through path skeleton refinement and local Bézier curve smoothing. Differential-flatness-based time parameterization then converts the smoothed geometric path into a time-indexed motion reference. In the Orchard Field Experiment, SDF-Theta* increased the minimum obstacle clearance by 16.6% compared with Theta* (ESDF) and achieved a safe path ratio of 100.00%. It also reduced the 99th-percentile curvature, maximum curvature, and total curvature variation by 54.3%, 85.5%, and 82.1%, respectively. Trajectory Tracking Validation yielded a path-overlap ratio of 94.65% at a nearest-path distance threshold of 0.03 m, with no physical collision or map-boundary violation. These results show that SDF-Theta* improved path safety and geometric smoothness and demonstrated trajectory trackability under the tested orchard conditions. Full article

Iced conductor galloping poses a critical threat to high-voltage transmission line safety and stability, yet existing monitoring systems are constrained by single-sensor dependency, inadequate signal denoising, and limited early warning accuracy. This paper presents a field-validated framework integrating multi-source sensor fusion, adaptive signal denoising, and deep learning-based early warning for iced conductor galloping. A five-layer Internet of Things (IoT) monitoring architecture is designed, fusing fiber Bragg grating (FBG) arrays, MEMS inertial sensors, and micro-meteorological stations, with dual-spectrum cameras providing auxiliary visual verification. Self-powered MEMS nodes utilizing electromagnetic induction energy harvesting are shown to have achieved year-round zero-external-power maintenance. An improved hummingbird local optimization algorithm (IHLOA) adaptively optimizes variational mode decomposition (VMD) parameters, combined with wavelet threshold denoising (WTD) for joint signal preprocessing, achieving a 17.78 dB signal-to-noise ratio improvement [95% CI: 17.2–18.3 dB]. A 26-dimensional multi-domain feature vector is constructed and reduced to 12 discriminative features via ReliefF selection. A Temporal Adaptation Gated Recurrent Unit with Attention (TA-GRU-Attention) model incorporating an adaptive irregular time series perception module is developed for galloping early warning, with all models evaluated exclusively on real-event test samples. Experimental validation through 1:20 scale wind tunnel aeroelastic tests and a 15-month field demonstration on an operating 220 kV transmission line at 2800–3200 m elevation demonstrates a galloping prediction accuracy of 91.3% [95% CI: 81.5–97.2%] under stratified time series split, a missed alarm rate of 12.2%, and a median advance warning time of 38.5 min (range: 18–65 min, IQR: 26–52 min), with 97.3% system availability over the deployment period. Full article

Time-series analysis is important for various scientific and industrial fields, such as remote sensing where observations may be disturbed by clouds, have irregular revisits and different sensors. Current transformer-based approaches have enhanced the modeling of distant time relationships, but they are still constrained by specific task designs and poor adaptability to various time-series patterns. To solve these problems, we present TimeHome, a universal sparse transformer basic model for handling heterogeneous time series. TimeHome incorporates a Heterogeneous Mixture-of-Experts (H-MoE) component, where different expert types are chosen dynamically based on a low-rank temperature-controlled gating mechanism to fit various sequence features. Moreover, a hybrid local–global attention mechanism is designed to consider both short-term variations and long-distance correlations, while specific heads are used for unified prediction, missing value estimation and abnormal event detection. TimeHome is pretrained on TS-200B, a huge database of time series including various temporal patterns from different domains. Comprehensive tests on several benchmark datasets and remote sensing extended evaluations show that TimeHome performs well in long-term prediction, missing value replacement and abnormal event detection. The model also exhibits good zero-shot adaptation ability and fast inference speed by adjusting experts dynamically. The source code and pre-training data will be released publicly. Full article

Sporulation is essential for asexual reproduction and dispersal in filamentous fungi, but the roles of upstream sporulation regulators in nematode-trapping (NT) fungi remain poorly understood. In this study, we characterized two Flb homologs, AoFlbB and AoFlbD, in Arthrobotrys oligospora. AoFlbB contains a bZIP-type domain, whereas AoFlbD contains a Myb-like DNA-binding domain, and both proteins are conserved among NT fungi. Deleting AoflbB or AoflbD had little effect on hyphal growth but markedly reduced spore production and altered spore germination, and real-time quantitative PCR analysis showed that the central sporulation regulators were significantly differently expressed, collectively indicating that AoFlbB and AoFlbD play positive roles in sporulation. Furthermore, both mutants produced more traps and showed enhanced nematode predation efficiency. The mutants also showed altered cell wall stress responses, hyphal cell length, nuclear number, endocytic trafficking, and metabolite profiles. The ΔAoflbB mutant showed irregular cell wall wrinkling, whereas the ΔAoflbD mutant displayed increased vesicle-like structures and accelerated FM4-64 uptake. Extracellular protease activity was comparable to the WT in ΔAoflbB but reduced in ΔAoflbD. Transcriptomic analysis of ΔAoflbD showed that the differentially expressed genes were significantly enriched in metabolism-related pathways. These findings indicate that AoFlbB and AoFlbD promote sporulation and contribute to the coordination of asexual reproduction and trap formation in A. oligospora. Full article

Accurate and timely identification of slippery road surfaces is essential for ensuring driving safety and operational efficiency on highways. However, blurred vehicle-background boundaries, uneven illumination, and water splashing caused by passing vehicles make existing image-based recognition methods prone to low accuracy. To address these challenges, this paper proposes an improved SegFormer-based framework with two task-specific innovations: (1) a novel Guided Multi-scale Fusion (GMF) module for task-guided multi-scale feature integration, designed to incorporate auxiliary information such as vehicle type, relative speed, and splash regions, enabling the network to focus on slipperiness-relevant road areas while suppressing background interference; and (2) an improved Boundary Attention Awareness (BAA) module with directional Sobel-based boundary initialization, which provides explicit geometric priors to preserve fine boundary details and reduce ambiguity in slippery regions with irregular or weak edges. A multi-scale input and enhancement strategy is further adopted, along with a weighted combination of cross-entropy loss and Dice loss to mitigate class imbalance. Experimental results on our self-constructed Guangzhou Beierhuan Expressway dataset achieve an mIoU of 95.80%, accuracy of 97.84%, and F1-score of 97.86%. To verify cross-domain generalization, we further evaluate the model on two additional benchmarks: it achieves an mIoU of 93.51% on the synthetic SYN-UDTIRI dataset, and attains an mIoU of 95.80% with an AmIoU of 76.20% on the public Cityscapes dataset, achieving competitive performance against several mainstream architectures. The proposed method offers considerable application potential for highway safety warning systems. Full article

Phloroglucinol is an oxygen-rich aromatic precursor whose thermal evolution can yield emissive carbonaceous materials with excitation-dependent photoluminescence. However, the influence of the reaction atmosphere on its low-temperature transformation remains insufficiently understood. In this work, phloroglucinol was thermally treated at 200 °C under air or static vacuum for 5 and 10 h. The resulting materials were investigated by electron microscopy, energy-dispersive X-ray spectroscopy, Raman spectroscopy, steady-state and time-resolved photoluminescence, transient absorption spectroscopy, and density functional theory calculations. The reaction atmosphere mainly affected the early stages of structural evolution. Air-treated samples formed irregular networks of filamentous substructures, whereas vacuum-treated samples displayed more compact quasi-spherical aggregates with a fibrous internal organization. Raman spectra indicated the progressive transformation of crystalline phloroglucinol into a disordered carbonaceous network containing small sp²-rich domains. Optical measurements revealed violet/deep-blue and cyan emissive centers whose relative contributions depended on atmosphere and treatment time. Transient absorption supported a multi-center photophysical picture involving ultrafast relaxation, intermediate trapping or interconversion, and longer-lived decay. Calculations on representative molecular motifs identified furan-containing conjugated domains as plausible candidates for the violet/deep-blue centers and compact triangular phloroglucinol-derived structures for the cyan center. Overall, oxygen availability and treatment duration modulate the morphology and emissive-center distribution of phloroglucinol-derived carbonaceous materials. Full article