Search Results (557)

The development of rapid and reproducible image analysis approaches that support genus-level pre-classification of macrofungi is important for taxonomic pre-evaluation and controlled microscopic data analysis. In this study, an advanced deep learning-based approach, namely the Attention-Enhanced Hybrid CNN–ViT Framework, was rigorously evaluated for genus-level classification, using basidiospore micrographs of five carefully selected macrofungal genera. The proposed approach integrates the ability of convolutional neural networks to identify local texture and contour patterns with the global context-modelling capability of Vision Transformer structures. The objective is to enhance the extraction of distinctive representations from microscopic spore images through feature fusion and attention mechanisms. A series of experiments was conducted on a curated dataset consisting of light microscopy images of the genera Agaricus, Hebeloma, Inocybe, Amanita, and Russula. The models were compared using a range of evaluation metrics, including accuracy, F1-score, MCC, ROC-AUC, and PR-AUC. The results showed that the InceptionV3 + ViT-B16 + Fusion configuration was the most successful hybrid model, achieving an accuracy of 0.9213 ± 0.0182, an F1-score of 0.9212 ± 0.0179, a Matthews correlation coefficient (MCC) of 0.9040 ± 0.0222, a receiver operating characteristic (ROC)-area under the curve (AUC) of 0.9896 ± 0.0069, and a precision-recall (PR)-AUC of 0.9684 ± 0.0192, respectively. The present findings demonstrate that basidiospore images can carry distinctive visual information for genus-level automated classification under controlled conditions. However, it is important to note that these results should not be interpreted as claims of species-level identification or field generalisability. This is due to the use of a single microscope-camera system, a single preparation protocol, and the absence of an independent external test set. The present study demonstrates that deep learning-based microscopic image analysis can be evaluated as a preliminary classification tool in macrofungal taxonomy. It also shows that such tools can provide a foundation for future work supported by specimen-level validation, external test sets, and different imaging protocols. Full article

Reliable delineation of brain tumor boundaries in multimodal magnetic resonance imaging (MRI) remains challenging despite substantial advances in deep learning–based segmentation. Although modern encoder–decoder architectures achieve strong volumetric overlap, precise geometric alignment of tumor contours remains inconsistent, particularly for small lesions and heterogeneous clinical cases. In neuro-oncology, even minor boundary deviations may influence surgical planning, radiotherapy targeting, and longitudinal treatment assessment. These limitations suggest that segmentation performance is not determined solely by network depth or loss design, but also by how multimodal information is structured prior to learning. We introduce CMYD-SurfaceNet, a scale-aware cascaded framework that restructures multimodal MRI inputs at the representation level to enhance boundary-sensitive segmentation. Rather than treating modalities as independently concatenated channels, selected sequences are first organized into a task-guided pseudo-RGB projection. This intermediate representation is subsequently transformed into the CMYK color space to disentangle shared luminance structure from modality-specific contrast dominance. To further encode geometric priors, a gradient-derived boundary density channel is incorporated to explicitly emphasize spatial discontinuities corresponding to tumor margins. The resulting CMYD representation is integrated within a two-stage nnU-Net cascade, where global tumor localization is followed by high-resolution region-of-interest refinement with auxiliary contour supervision. This scale-aware design improves sensitivity to small tumor components while stabilizing contour delineation. Extensive evaluation on the BraTS benchmark demonstrates consistent improvements in boundary-sensitive metrics. Compared with baseline nnU-Net, the proposed framework reduces HD95 from 3.6 mm to 2.4 mm and increases Surface Dice at 1 mm tolerance from 0.82 to 0.89, while maintaining competitive Dice performance. These findings suggest that representation-level structural decoupling, when combined with scale-aware refinement, may provide clinically relevant boundary-aware multimodal MRI segmentation support without increasing architectural complexity. Full article

Introduction: Fish stock identification and delineation are fundamental requirements for preventing local depletion and promoting the sustainable exploitation of marine resources. The blackspot seabream, Pagellus bogaraveo, is the most commercially valuable sparid species across the Northeast Atlantic and the Mediterranean Sea. To effectively discriminate fish stocks, researchers increasingly rely on the use of natural tags, which reflect both environmental and genetic influences, providing critical information regarding fish movements and population structure. Objective: To broaden the understanding of P. bogaraveo stock structure, samples originally obtained for a parasite-based discrimination study were used to provide complementary insights through otolith shape and geochemical signatures. Methodology: A subset of 150 individuals (30 per location) collected across five Portuguese locations (Portugal mainland: Matosinhos, Figueira da Foz, and Sagres; and Archipelagos: Azores and Madeira) was selected for otolith analyses. Otolith contour phenotypic variation was quantified through Elliptical Fourier Descriptors (EFDs) and Shape Indices (SIs), while elemental signatures (element: Ca) were analyzed using solution-based inductively coupled plasma mass spectrometry (SB-ICP-MS). Statistical analyses involved both univariate (one-way ANOVA, followed by Tukey tests, if needed) and multivariate approaches (MANOVA and LDFA), considering both individual and combined datasets. Results: EFDs + SIs yielded the lowest discriminatory power, with an overall reclassification accuracy of 38%. In contrast, Ca signatures provided the highest discrimination at 79%. The combination of both markers resulted in a slightly lower overall accuracy of 75%, likely due to the higher variance associated with the morphological data. Conclusions: In agreement with the previous parasite assessment, these otolith-based approaches confirm that the Macaronesian archipelagos consist of distinct stocks, separate from the Portuguese continental shelf. Furthermore, significant differences in otolith geochemical signatures between Sagres and Figueira da Foz point to a further subdivision of stocks. These findings are consistent with recent genetic data identifying three distinct stocks along the western and southern Iberian Peninsula, reinforcing the need for localized management of P. bogaraveo populations to ensure long-term fishery sustainability. Full article

Residual stresses and distortions are critical challenges in laser beam powder bed fusion (PBF-LB) of Ti6Al4V components (PBF-LB/M/Ti6Al4V), impacting structural integrity and dimensional accuracy. This study assesses the inherent strain method (ISM) as a computationally efficient alternative to full thermomechanical simulations for predicting these effects. By integrating ISM with experimental validation via the contour method, the research provides specific insights into stress distribution patterns in geometries featuring stress concentrators such as notches. Results demonstrate a strong correlation between simulation and experimental data, particularly at the mid-height regions. Quantitatively, the orthotropic ISM successfully predicted the peak residual stress at 1101.4 MPa, showing excellent agreement within a 4% error margin against the experimental maximum of 1144 MPa captured via the contour method. These findings underscore how ISM can be effectively applied to practical engineering components to predict high-stress zones, enabling the design of distortion-compensated parts without the high computational cost of traditional models. Ultimately, this method facilitates more robust process optimization and enhances the quality and reliability of Ti6Al4V components manufactured via PBF-LB. Full article

Previous research on Mandarin Chinese tones and intonation has focused primarily on universal sentence pitch patterns (declination) and sentence types (declarative and interrogative). The specific impact of internal syntactic structures remains under-explored. This study presents two acoustic experiments using controlled Tone 1 (high-level) stimuli to isolate intonational “big waves” from lexical “small ripples”. Experiment 1 investigates how syntactic position (subject vs. object), relative clause type (subject-relative vs. object-relative), and word class (verb vs. noun) influence pitch contours. Experiment 2 resolves conflicting findings regarding word-class pitch by testing nouns and verbs across four sentential contexts. The results indicate that subject positions carry significantly higher pitch than object positions, reflecting an interaction between SVO word order and declination. Crucially, subject-relative (SR) clauses exhibit a falling pitch tendency, while object-relative (OR) clauses show a rising trend. These results suggest that pitch realization is a complex “algebraic sum” of universal phonological trends, syntactic hierarchy, and semantic information structure. Full article

Background/Objectives: Long-term clinical data on direct posterior composite restorations are scarce, particularly beyond simple survival outcomes. This study aimed to characterize the long-term functional, esthetic, and biological behavior of posterior composite restorations after nearly three decades of service using selected FDI criteria and to assess changes across available follow-up examinations, including within a predefined sub-cohort. Methods: This observational follow-up involved 21 patients with 57 posterior composite restorations placed in 1995–1996 at the Department of Operative Dentistry, Goethe University Frankfurt, by undergraduate dental students under supervision. The 2025 follow-up used FDI criteria to assess functional, aesthetic, and biological properties, classifying outcomes as clinically acceptable, intervention needed, or failure. Descriptive analyses were applied to the entire cohort. Longitudinal analyses were conducted on a sub-cohort of 14 patients with 27 restorations at three time points. Exploratory analyses assessed associations with restoration factors, caries experience, and gingival health. Results: In 2025, 54.4% of restorations were clinically acceptable, 28.1% required intervention, and 17.5% were failures. Functional criteria remained mostly acceptable, though form and contour showed the highest mean values. In the longitudinal sub-cohort, significant changes over time were observed in anatomical form and occlusal wear. Retention, marginal adaptation, proximal contact, and surface luster did not change significantly. Biologically, restorations available for direct assessment had low incidences of secondary caries, hard-tissue defects, and postoperative sensitivity or pulpal issues. Conclusions: Posterior composite restorations can function for nearly three decades but gradually deteriorate in certain aspects. Long-term changes mainly involve cumulative functional aging of the anatomical form and occlusal wear, rather than widespread biological failure. These findings underline the importance of differentiated long-term assessment and support conservative management approaches where clinically feasible before replacement is undertaken. Full article

Image enhancement is of pivotal importance in the detection of defects in wood veneers. However, acquired images frequently exhibit signs of blurring, uneven illumination, and insufficient contrast, which can lead to a reduction in the accuracy of defect recognition. In this study, an algorithm based on Grünwald–Letnikov (G–L) fractional-order differentiation is proposed for the enhancement of wood veneer defect images. Initially, the gain characteristics of differential amplitude-frequency responses on high- and low-frequency image components are analyzed, and the feasibility of the method is demonstrated by linking these characteristics with the frequency-domain distributions of live knot, dead knot, and crack defects. Secondly, an eight-direction mask operator is constructed based on the G–L definition, and a DC component preservation factor is introduced to eliminate the luminance drift caused by mask truncation. The application of the mask is performed independently on the R, G, and B channels, and a dynamic blending mechanism is designed to achieve a balance between texture enhancement and structural fidelity. Finally, a set of six evaluation metrics (AG, E, PSNR, RMSE, SSIM, and VIF) is employed to assess the quality of enhanced images. The proposed algorithm is then compared with five existing algorithms (SSR, MSR, MSRCR, CLAHE, and AGC) under both noise-free and additive white Gaussian noise conditions. The findings indicate that the G–L fractional-order differentiation algorithm facilitates a more balanced representation of image features, thereby enhancing contrast, brightness, and textural contours. This approach results in more authentic color reproduction and superior visual quality. Full article

Effective management and conservation of sea turtles is often constrained by a lack of knowledge of at-sea distribution and abundance. While abundance estimates of nesting females are typically well-documented on nesting beaches, counting sea turtles at sea presents challenges due to their widespread distribution and cryptic habits. Given nesting beaches only document adult females, at-sea data are also more informative of greater population demographics. To estimate the abundance and density of green sea turtles (Chelonia mydas) in the Red Sea waters of Saudi Arabia we conducted strip transect aerial surveys in four survey zones that spanned ~66% of shallow water habitats (<200 m depth), within which we counted sea turtles, and also other species such as dugongs and other marine mammals, sharks, and rays. Corresponding abundance estimates were modelled to account for perception bias (whether a surveyor saw a turtle that was available) and detection bias (whether a turtle was available to be seen). Our results suggest an abundance of ~201,427 green sea turtles potentially present between the 200 m bathymetric contour and the Saudi Arabian shore. However, there was a statistically significant relationship between turtle location and proximity to coral reefs, with over 90% of turtles found within 3500 m of coral reef structures (whether coastal fringing reefs, barrier reefs or atolls), and therefore it would be inappropriate to use an estimate assuming equal distribution. Adjusting for this buffer area we estimated ~95,000 turtles (95% CI: 64,000–142,000) within the proximity of reef structures. These findings represent the first abundance estimates of green turtles in the Red Sea. Repeated over time, surveys such as these can identify changes in population structure, distribution and abundance, and inform conservation and management agencies. Full article

In this work, we explore the nonlinear wave phenomena of the (3+1)-dimensional Boussinesq (II) equation, a significantly higher-dimensional model that describes dispersive wave propagation in fluid dynamics, plasma systems, and nonlinear optics. Using exact analytic and qualitative dynamic approaches, we study a wide range of solutions and stability characteristics of the model. Initially, we use the Hirota bilinear method to obtain a number of exact solutions, such as breather waves, two-wave interaction solutions, and other types of localized nonlinear waves. These solutions display remarkable physical properties, including periodic energy trapping, oscillatory modulations, and nonlinear wave interactions in higher dimensions. In addition, the $(m+{\displaystyle \frac{1}{G^{\prime }}})$-expansion method is used to derive new soliton solutions, such as bright solitary waves and W-shaped solitons, which are found to be stable and undergo pulse-shaping dynamics under certain conditions. Three-dimensional, two-dimensional, and contour plots are displayed for some of the solutions to demonstrate the physical significance of the results. The visualizations reveal the presence of localized waves, wave interactions, periodical breathing, and stable soliton profiles. Furthermore, we conduct modulation instability analysis to describe the conditions under which small perturbations of continuous wave backgrounds are unstable. The dispersion relation and the instability gain spectrum are obtained, which explain the formation of breathers, soliton trains, and other coherent structures. Furthermore, a Galilean transformation converts the governing equation into a planar nonlinear dynamical system, enabling its qualitative study. The Hamiltonian structure is revealed, and the fixed points are identified as centers, saddles, and cusps through bifurcation analysis. To investigate more complex dynamics, a periodic forcing term is introduced into the system, resulting in chaos in the forced system. The chaotic behavior is confirmed via phase portraits, three-dimensional attractors, time series, Poincaré sections, return maps, fractal dimension, and positive Lyapunov exponents. We also perform a sensitivity test to show the effect of initial condition variations on the system’s long-term dynamics. The findings greatly expand the exact solution set and dynamics of the (3+1)-dimensional Boussinesq equation (II). The analytical approach presented in this paper can also be applied to other multidimensional nonlinear evolution equations of mathematical physics. Full article

This study examines the dynamical behavior of the dual-mode nonlinear Schrödinger equation (d-mNLSE), which describes the interaction, amplification, and attenuation of two coexisting wave modes in nonlinear media. The model incorporates key physical parameters including the nonlinearity coefficient, interaction phase velocity, and dispersion parameter, which significantly influence the evolution of nonlinear waves. By applying the modified Sardar sub-equation method (mSS-EM), a wide spectrum of exact analytical solutions is derived. These solutions include mixed trigonometric waves, shock-type structures, singular solutions, complex dark–bright solitons, multi-peak solitons, periodic and mixed-periodic waves, as well as mixed hyperbolic structures. The analytical findings provide useful insight into nonlinear wave propagation phenomena arising in fluid mechanics, water wave dynamics, ocean engineering, and related physical systems. Moreover, the conservation laws of the d-mNLSE are established, which leads to the conserved quantities of impulse power, momentum, and energy and describes the invariant characteristics of the soliton solutions during their propagation. The bifurcation analysis of the reduced dynamical model is carried out to explore the qualitative characteristics of the obtained solutions. The equilibrium points of the considered model are calculated, and their stability properties are analyzed systematically. To demonstrate the physical characteristics of the obtained solutions, different kinds of two-dimensional, three-dimensional, and contour plots are plotted using symbolic computations software. These findings confirm that the analytical method used to obtain the soliton solutions can be used to obtain a variety of soliton solutions of nonlinear evolution equations that appear in applied sciences and engineering. Full article

Objectives: Hyaluronic acid augmentation of the mandibular angle has become a widely performed procedure for improving lower facial contour and definition; however, because of its anatomical proximity to the masseter muscle, concerns remain regarding possible functional effects on the stomatognathic system. This longitudinal study investigated whether hyaluronic acid injection in the mandibular angle could affect neuromuscular recruitment patterns of the masseter and temporalis muscles through surface electromyography assessment. Methods: Ten adults were assessed at baseline and at 15, 30, and 60 days after injection. Electromyographic activity was recorded during mandibular tasks and chewing conditions. Repeated measures ANOVA with Bonferroni correction was used for statistical analysis (p < 0.05). Effect sizes were calculated using partial eta squared (η²p), and pairwise comparisons were explored using Cohen’s d. Data are presented as means with 95% confidence intervals (95% CIs), and individual trajectories were analyzed to characterize temporal patterns and within-subject variability. Results: Most variables did not show significant changes over time (p > 0.05), with small-to-moderate effect sizes. Significant reductions were observed in the right masseter during left laterality (p = 0.03; η²p = 0.17) and in the left masseter during maximum voluntary contraction with and without parafilm (p = 0.02–0.04; η²p = 0.19–0.22). These findings were temporary and were not consistently identified among subjects. Chewing conditions remained stable across all time points (p > 0.05). Conclusions: Hyaluronic acid injection in the mandibular angle was not associated with clinically relevant electromyographic changes. The observed variations were transient, showed small-to-moderate effect sizes, and demonstrated interindividual variability, suggesting preservation of neuromuscular function and physiological adaptive responses. Full article

Building extraction from high-resolution remote sensing imagery remains challenging due to spectral heterogeneity, complex background interference, and incomplete boundary delineation. Thus, we propose GWNet, which integrates gated Mamba-CNN modeling with wavelet-based boundary enhancement. Specifically, a Gated Mamba-CNN Module (GMC) is embedded into the medium- and low-resolution branches to jointly capture local texture features and long-range dependencies. In addition, a channel-wise gating mechanism is introduced to adaptively balance global contextual information and local structural details, thereby alleviating fragmented predictions and internal holes within the same building caused by variations in roof materials, while reducing the misclassification between buildings and background objects such as roads and bare land. Furthermore, a Wavelet Boundary Optimization Module (WBO) is designed to exploit multi-directional high-frequency components extracted by fixed Haar wavelet filters, thereby enhancing the representation of building boundaries and corners. This design effectively mitigates boundary blurring, incomplete contours, and missed detections caused by the loss of high-frequency edge information during downsampling. Extensive experiments on four public datasets, namely WHU, Massachusetts, WHU Satellite I, and Potsdam, demonstrate the effectiveness and robustness of GWNet across diverse spatial resolutions and scene complexities. Specifically, GWNet achieves IoU/BIoU scores of 90.68%/66.88% on the WHU dataset, 73.02%/93.19% on the Massachusetts dataset, 63.86%/83.77% on the WHU Satellite I dataset, and 83.21%/58.96% on the Potsdam dataset, consistently outperforming several competitive methods. Qualitative results further confirm that GWNet produces more complete building regions and sharper, more continuous boundaries. These findings validate the effectiveness of the proposed global–local feature extraction mechanism and wavelet-based boundary enhancement strategy. Full article

Fractional derivatives introduce an effective mathematical structure to describe memory effects, long-range interactions, and anomalous transport processes that are not well represented by the traditional integer-order models. This paper presents the unidirectional fractional longitudinal wave equation as a governing equation where a model is proposed to explain the steady wave propagation of solitary waves in a magneto-electro-elastic circular rod. Magneto-electro-elastic substances are a groundbreaking category of advanced functional materials with tremendous nanotechnology and biomedical engineering prospects because of their effective multi-field energy conversion and temperature responsiveness. In order to solve this complicated fractional nonlinear equation, we introduce a new computation-analysis approach: the Riccati subequation neural network method. This hybrid solution is a synergistic combination of an analytical solution structure and a neural network structure consisting of input, hidden, and output layers, with interconnection between neurons through weighted connections and activation functions. It is important to note that every neuron in the first hidden layer is coupled to the solutions of the Riccati equation, and this allows the systematic use of the new trial functions. With the suggested method, analytical solutions are obtained for the spacetime fractional partial differential equations of the unidirectional fractional longitudinal wave equation in the exact form of trigonometric, hyperbolic, and rational functions. This paper is the first attempt to combine the Riccati subequation method with a neural network model, which has given rise to new types of solitary wave solutions. The three-dimensional, two-dimensional, and contour plots are used to visualize the dynamic nature of these solutions and to display the rich nonlinear wave behavior. The effectiveness and the robustness of the implemented technique is not only proven through our findings but also provides more profound information about the nonlinear wave phenomena in the advanced multifunctional materials, which can inform future developments in energy harvesting and the design of biomedical devices. Full article

Advanced nanoimprint lithography (NIL) is promising for inorganic semiconductor patterning because it enables high-resolution replication with a relatively simple process flow; however, yield loss increasingly originates from spatially distributed, subcritical distortions accumulated across coating, exposure, etching, and imprinting. In this study, we propose an integrated physics-based and data-driven framework for pre-manufacturing defect-risk prediction in NIL. The framework combines an NDA-safe layout database, a physics-based process twin, and a stochastic risk prediction model using a physics-augmented convolutional neural network with conformal uncertainty calibration. Starting from binary design layouts, the process twin sequentially captures resist thickness variations during spin coating, proximity-induced dose redistribution and development-induced pattern deformation during electron-beam lithography (EBL), density-sensitive pattern transfer during reactive ion etching (RIE), and three-dimensional resist filling during imprinting, thereby generating physically consistent parameter maps for downstream learning. The results demonstrate an end-to-end virtual inspection flow that converts layouts into spatially resolved risk maps before fabrication. In addition, patterns with similar contour extent but different local density exhibit distinctly different risk distributions, indicating that manufacturability is governed not only by nominal geometry but also by local pattern environment. These findings support pre-manufacturing virtual inspection as a physically interpretable route for early yield-risk screening in advanced NIL. Full article

Background/Objectives: Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal gastrointestinal cancers, and reliable preoperative predictors of aggressive tumor biology are essential for optimizing treatment strategies, particularly in resectable PDAC (RPDAC). This retrospective study evaluated the diagnostic accuracy of dynamic computed tomography (CT) for detecting extrapancreatic extension, peripancreatic plexus (PL), serosal (S), and retroperitoneal (RP) invasion, and assessed its prognostic significance. Methods: Ninety-four patients who underwent curative-intent upfront surgery for resectable PDAC between 2007 and 2020 were included. Dynamic CT was reviewed using standardized window settings (WL 35/WW 350) to identify soft-tissue projections extending beyond the pancreatic contour. Results: Pathological S, RP, and PL invasion occurred in 29.8%, 56.3%, and 17.0% of patients, respectively. Dynamic CT demonstrated accuracies of 73.4%, 76.6%, and 87.2% for S, RP, and PL invasion, respectively. Notably, patients with PL-positive CT findings had significantly poorer disease-specific survival (DSS) than those with PL-negative, with 3- and 5-year DSS rates of 37% and 0% versus 61% and 53% (p < 0.001). Multivariate analysis confirmed preoperative PL invasion as the only independent predictor of poor prognosis. Conclusions: Dynamic CT provides reasonable diagnostic performance for assessing extrapancreatic invasion. In addition, CT-identified PL invasion reflects aggressive tumor behavior and may justify consideration of neoadjuvant therapy, even in anatomically resectable disease. Full article