Search Results (8,396)

Background/Objectives: Machine learning (ML) models have increasingly been applied to prognostic prediction in oral cavity squamous cell carcinoma (OCSCC), though their performance and methodological quality remain variably reported. This systematic review evaluated contemporary ML-based prognostic models for clinically relevant OCSCC outcomes, with emphasis on independently validated studies. Methods: A systematic review was conducted according to PRISMA guidelines using PubMed, Scopus, Cochrane Library, and CINAHL databases through 1 December 2025. Studies evaluating ML or artificial intelligence prognostic models in adult OCSCC patients were included. Outcomes included overall survival, recurrence, disease-free survival, recurrence-free survival, disease-specific survival, cancer-specific survival, progression, and nodal metastasis. Data extraction and risk-of-bias assessment using PROBAST + AI were performed independently by reviewers. Results: Forty studies comprising 105,619 patients met inclusion criteria. ML architectures included random forests, support vector machines, gradient boosting methods, neural networks, and deep learning frameworks. Most models incorporated clinical and pathologic variables, while many integrated radiologic, immunologic, or genomic features. For overall survival prediction, independently validated models generally demonstrated AUCs between 0.80 and 0.90. Recurrence prediction models similarly showed favorable discrimination, with most externally validated studies reporting acceptable predictive performance. Additional prognostic endpoints including disease-free survival, progression, and nodal metastasis demonstrated AUCs ranging from 0.70 to 0.90. Common methodological limitations included retrospective design, small sample size, inadequate external validation, and risk of overfitting. Conclusions: ML-based prognostic models in OCSCC demonstrate generally favorable predictive performance across survival and recurrence outcomes. However, substantial heterogeneity in methodology and limited external validation continue to restrict clinical implementation. Future work should prioritize prospective multicenter validation, standardized reporting, and reproducible modeling frameworks. Full article

Purpose: Sarcopenia has emerged as a potential prognostic factor for postoperative complications, functional recovery, patient-reported outcomes, and healthcare costs in patients undergoing total knee arthroplasty (TKA). This systematic review aimed to evaluate its impact on these outcomes following primary TKA. Methods: A systematic review was conducted according to PRISMA guidelines and prospectively registered in PROSPERO (CRD420261320013). PubMed/MEDLINE, EMBASE, and Cochrane Library were searched up to 1 December 2025. Comparative clinical studies including sarcopenic and non-sarcopenic patients undergoing primary TKA and reporting postoperative outcomes were included. Methodological quality was assessed using the MINORS tool. Due to heterogeneity in study design, diagnostic criteria, and outcome measures, a narrative synthesis was performed. Results: Nine studies including more than 93,000 patients were analyzed. Sarcopenia prevalence ranged from 7.7% to 25%. Sarcopenic patients demonstrated higher rates of postoperative complications, including medical events, blood transfusion, falls, fractures, reoperations, and implant-related complications. Functional recovery was delayed, particularly in patients with sarcopenic obesity, with slower improvements in range of motion and gait speed. Although both groups improved after TKA, short- to mid-term patient-reported outcomes were often inferior in sarcopenic patients, while long-term differences were less consistent. Sarcopenia was also associated with longer hospital stay and increased healthcare costs. Conclusions: Sarcopenia is associated with worse postoperative outcomes following primary TKA and may represent a modifiable risk factor for perioperative optimization. Full article

In millimeter-wave frequency-modulated continuous-wave (FMCW) multiple-input multiple-output synthetic-aperture radar (MIMO-SAR) imaging, platform displacement beyond the spatial Nyquist limit during a slow-time sampling interval creates aperture gaps, causing azimuth aliasing and degraded resolution. This paper proposes a spectral-prior-guided Swin TransUnet (SSTU) method for suppressing azimuth ambiguity in sparse moving-array imaging. Gaussian soft labels derived from point-scatterer positions formulate localization as heatmap regression and guide mainlobe learning. A two-dimensional fast Fourier transform (2D-FFT) layer then constructs a range–azimuth spectrum that exposes main peaks, sidelobes, and periodic grating lobes. A convolutional encoder extracts local spectral features, Swin Transformer blocks model long-range ambiguity correlations, and a U-Net-style multiscale decoder reconstructs high-resolution range–azimuth images. Simulations show that SSTU reliably recovers multiple point targets from noise and grating lobes despite substantial aperture gaps. At 60% aperture sparsity and signal-to-noise ratio (SNR) above −6 dB, it achieves a root mean square error (RMSE) below ${10}^{-2}$ and an azimuth ambiguity suppression ratio better than −30 dB, outperforming conventional methods. Measurements using a 77 GHz radar platform further demonstrate high-quality outdoor imaging of randomly distributed strong scatterers at 60% moving-aperture sparsity. Full article

The quality of sweet tamarind fruit, as determined by its total soluble solids (TSS), titratable acidity (TA), and TSS/TA ratio, is important for consumer satisfaction. Nondestructive techniques are therefore required to assess the quality of sweet tamarind fruit. This study investigated whether near-infrared hyperspectral imaging (NIR-HSI) in the wavelength range of 935–1720 nm can be used as a non-destructive method to assess TSS, TA, and the TSS/TA ratio of sweet tamarind fruit and to classify it under commercial standards. NIR-HSI-based chemometric and machine-learning modeling was applied for quantification and qualification analyses. Calibration models for determining TSS, TA, and the TSS/TA ratio were developed using partial least squares regression (PLSR) and support vector machine regression (SVMR). A combination of first derivative and SNV spectral pretreatment was optimized to establish an SVMR model for TSS determination. MSC spectral pretreatment was optimized to develop the SVMR model for TA assessment, and the first derivative spectral pretreatment was optimized to establish an SVMR model for the TSS/TA ratio. Correlation coefficients of prediction (R_p) of 0.959, 0.961 and 0.957 were obtained with root mean square errors of prediction (RMSEP) of 1.102%, 0.369% and 7.850, and a ratio of performance to deviation (RPD) of 3.29, 3.52 and 3.34 for the TSS, TA, and TSS/TA ratio evaluations, respectively. Partial least squares–discriminant analysis (PLS-DA) and support vector machine classification (SVMC) were used for classifying sweet tamarind fruit under a commercial acidity standard (≤4%). The SVMC with SNV spectral pretreatment produced the best prediction results for distinguishing standard and off-standard sweet tamarind fruit with an 82.86% accuracy. NIR HSI can be used to non-destructively predict the quality of tamarind fruit. It can be applied for online sorting to evaluate individual sweet tamarind fruits for grading and quality control in factory environments. Full article

Wood-based materials are a significant source of formaldehyde (HCHO), which is a volatile organic compound (VOC) classified as a human carcinogen that has adverse effects on the indoor air quality (IAQ) and health. This study evaluates HCHO emissions from six materials commonly used indoors, including medium-density fiberboard, melamine, pine boards, birch boards, oak boards, and alder boards, using the desiccator method in accordance with ASTM D5582. The experimental results were integrated with an indoor air model to estimate exposure under representative residential conditions. The measured emissions ranged from 0.235 ± 0.01 to 1.023 ± 0.10 mg m⁻³, with composite materials exhibiting the highest values. The risk analysis showed that all samples exceeded international reference values, with the greatest concern arising in scenarios of chronic exposure. The modeling indicated that indoor air concentrations depend heavily on material load and ventilation, reaching values as high as 0.155 mg m⁻³ under conditions of low air exchange. Overall, the results show that material composition, installed quantity, and ventilation conditions are key factors influencing indoor HCHO concentrations. This study offers an integrated approach that combines experimental measurement and exposure estimation, contributing to informed material selection and the assessment of their impact on IAQ. Full article

The performance of visual detection models in industrial applications is strongly influenced by training dataset quality. Although imaging scheme design and algorithm optimization are often emphasized, systematic dataset quality evaluation remains insufficient. To address this gap, this study proposes a dataset quality evaluation framework for industrial object detection. It includes four dimensions and thirteen quantifiable indices: three for acquisition environment, two for image quality, three for dataset scale, and five for annotation quality. Normalization based on theoretical maximum scores is used to reduce biases caused by different score ranges, and dimension weights are assigned using Taguchi orthogonal experiments. Validation is performed on five public and three self-constructed datasets using YOLOv12n and RT-DETR-R18. A positive correlation trend is observed between the proposed scores and detection accuracy, with PLCC/SRCC/Kendall’s tau values of 0.685/0.850/0.764 and 0.656/0.826/0.691, respectively. After second-level weight optimization, the correlations increase to 0.775/0.922/0.837 and 0.748/0.898/0.764. Corresponding p-values and 95% confidence intervals are reported to quantify statistical uncertainty. Sensitivity analysis and ablation comparisons further verify the robustness and necessity of the proposed multidimensional framework. The proposed framework provides a quantifiable method and practical acquisition guidelines for improving industrial image dataset quality. Full article

Accurate estimation of the Doppler centroid is a prerequisite for achieving high-quality Synthetic Aperture Radar imaging. In highly squinted working scenarios, traditional frequency-domain methods depend on antenna pattern fitting. They are easily affected by pattern mismatch and strong scatterer interference. In addition, these methods cannot directly determine the Doppler ambiguity number. The range envelope-based Hough transform can correct linear range walk. It further realizes Doppler centroid estimation without ambiguity. However, range curvature hinders its estimation accuracy. To solve the above problem, this paper proposes a novel squint angle estimation scheme. The scheme organically combines closed-loop iterative range curvature compensation and energy-weighted Hough transform. Within a closed-loop iterative architecture comprising curvature compensation, line feature extraction, direction measurement, and angle refinement, the presented method progressively rectifies curved trajectories, yields robust squint angle estimates, and further derives the unambiguous Doppler centroid indirectly. Both simulated datasets and real airborne SAR measurements demonstrate the effectiveness and robustness of the proposed method. Full article

Molten salts are increasingly regarded as promising fluids for high-temperature heat transfer, thermal energy storage, and advanced reaction processes, including concentrated solar power (CSP), molten salt oxidation (MSO), and next-generation nuclear reactors. Among these materials, the ternary eutectic mixture Li₂CO₃–Na₂CO₃–K₂CO₃ (32.12–33.36–34.52 wt%) has emerged as a leading candidate due to its wide operating temperature range and favourable thermodynamic properties. Despite its relevance, substantial inconsistencies and gaps remain in the available thermophysical property data, posing challenges for reliable design, modelling, and industrial deployment. This work revisits the Li₂CO₃–Na₂CO₃–K₂CO₃ eutectic through a critical assessment of the literature from its reported melting point at 670 K (397 °C) up to approximately 1200 K (927 °C). Using a methodology inspired by IUPAC-supported strategies previously applied to common liquids such as water and hydrocarbons, we examine the quantity, quality, and coherence of existing measurements. Reference correlations are proposed only where the data are sufficiently robust to justify them. The analysis highlights a pressing need for more accurate and comprehensive measurements—particularly for heat capacity, thermal conductivity, and viscosity—to enable the development of reliable standard reference correlations. Brief recommendations are given on the measurement methods that should be used in high-temperature measurements, namely for heat capacity, viscosity, and thermal conductivity. Reliable thermophysical property data for (LiNaK)₂CO₃ remain limited and inconsistent, despite its relevance for high-temperature energy applications. Density data are comparatively robust, but heat capacity, thermal conductivity, and viscosity still require high-accuracy measurements at elevated temperatures. Addressing these data deficiencies is essential for advancing the safe and efficient use of molten carbonates in high-temperature energy technologies. Full article

High-speed machining of complex freeform geometries faces fundamental challenges in balancing computational efficiency, kinematic constraints, and precision, particularly in high-curvature regions where traditional interpolation methods suffer from geometric errors and jerk-induced vibrations. This study presents a Galerkin-NURBS interpolation framework that integrates Galerkin projection to optimize NURBS parameterization, minimizing geometric approximation error, and couples it with a jerk-limited S-curve trajectory planning algorithm that enforces $C^3$ continuity while respecting feedrate, acceleration, and jerk constraints. Numerical simulations and machining experiments were conducted on butterfly-shaped and horse-shaped curves using a five-axis CNC machine equipped with rotary/linear encoders and validated via profilometer-based surface roughness measurements. The proposed method achieved a 32.9% reduction in processing time (3.091 s) compared to the CQSF method (4.61 s) and a 35.1% reduction in interpolation steps relative to FSRC. Surface roughness ($R_a$) values ranged from 0.1271 μm to 0.2009 μm, with most measurements compliant with ISO 21920-1:2021; the maximum value (0.2009 μm) represents the upper bound of the standard’s high-precision threshold for aluminum alloy 6061. These findings demonstrate that the proposed framework significantly improves machining efficiency and surface quality while maintaining geometric fidelity, making it suitable for precision manufacturing applications where sensor-guided process optimization is critical. Full article

In high-pier bridge construction, the quality and accuracy of embedded rebar placement are critical to ensuring structural safety and durability. However, conventional manual inspection methods are inefficient, subjective and pose significant safety risks in high-altitude operations. These methods are unable to comprehensively inspect all pier columns on a daily basis, and frequently result in delays in acceptance that necessitate rework. In order to address these challenges, the current study proposes a smart vision-based inspection framework for the automatic and high-precision quality assessment of rebar under long-distance imaging conditions. This approach allows quality inspectors to remotely predict and evaluate the embedment quality of rebars from a safe distance. Notably, this work introduces a novel dual-source coordinate fusion mechanism that integrates improved instance segmentation with corner detection for global-to-local precision enhancement, representing an original contribution to rebar placement inspection in complex high-pier scenarios. The framework integrates an improved YOLOv8-CD segmentation model and a corner detection algorithm through a dual-source coordinate fusion mechanism, achieving an integration of global rebar detection and local feature enhancement. The YOLOv8-CD model, when optimised, features the Convolutional Block Attention Module (CBAM) integrated into the backbone, with the objective of enhancing recognition accuracy for small targets. Additionally, a Dilation-Wise Residual (DWR) module has been inserted before the neck C2f layer for the purpose of strengthening multi-scale feature extraction. The process of perspective correction and pixel-to-actual-length conversion coefficienting is performed in order to achieve a millimetre-level measurement of the rebar spacing and diameter. Empirical validation through real high-pier construction scenes demonstrates that the proposed framework attains a detection accuracy of 98.82%, surpassing conventional YOLO-based and single-source methodologies. The experimental results demonstrate that this framework is able to detect objects at longer distances, and to maintain its performance when the target is at a greater distance than that which was used for training. The proposed approach is expected to provide an efficient, safe, and quantitative solution for intelligent bridge construction quality monitoring, offering valuable insights for the future development of smart construction and structural health inspection systems. Full article

Background/Objectives: Pediatric distal diametaphyseal radius fracture (DDRF) occurs in a challenging transitional zone between the metaphysis and diaphysis, presenting distinct biomechanical characteristics. This study compared outcomes of plate versus Kirschner wire (K-Wire) fixation—including different configurations—in a consecutive cohort of pediatric unstable DDRF. Methods: We conducted a single-center retrospective review of 63 patients (aged 6–15 years) treated between 2023 and 2025, divided into plate (n = 26) and K-Wire (n = 37) groups. The K-Wire cohort was subclassified as bicortical (n = 14), bicortical + intramedullary (n = 10), and intramedullary alone (n = 13). Outcomes included alignment quality, fracture healing by modified RUST (mRUST), and the Forearm Fracture Index (FFI). Multivariable regression was used to adjust for confounding. Results: Plate patients were older (11.92 ± 2.87 vs. 9.32 ± 2.96 years, p < 0.05) and heavier (43.23 ± 11.31 vs. 30.84 ± 11.19 kg, p < 0.001) and had higher FFI (median 1.13 vs. 1.09, p < 0.05). Multivariable regression showed plate fixation was independently associated with good alignment (adjusted OR = 14.48, p = 0.018). Bicortical K-Wires yielded the highest alignment rate among percutaneous techniques (85.7%), while bicortical + IM showed poor outcomes (0% good). The unadjusted 1-month healing advantage of K-Wires (mRUST 2.53 ± 0.60 vs. 2.19 ± 0.47, p = 0.022) was non-significant after adjusting for body weight (p = 0.248). The complication rate was low (3.2%, 2/63). At final follow-up (range 6–18 months), all patients demonstrated unrestricted wrist motion and forearm rotation, and functional outcomes were assessed clinically without validated patient-reported outcome measures. Conclusions: Plate fixation independently predicts superior alignment, whereas the K-Wire group’s higher 1-month mRUST scores were attributable to their younger, lighter profile rather than the implant itself. Among K-Wire options, bicortical configuration achieves reduction quality comparable to plates, while the bicortical + IM configuration should be avoided. In this cohort, plate fixation was more common among older, heavier patients with higher FFI, whereas younger, lighter patients more often received K-Wires; both methods demonstrated low complication rates. Full article

Background: Processed extracellular lipid–protein preparations require rigorous analytical characterization to determine whether their compositional profile, processing stability, and short-term cellular compatibility are preserved across storage and handling conditions. Methods: In this study, we quantitatively characterized a processed phospholipoproteic secretome-derived formulation under fresh, concentrated, cryopreserved, and lyophilized conditions. Results: Label-free quantitative proteomic analyses performed using timsTOF Pro mass spectrometry coupled to dia-PASEF acquisition identified 574 human proteins across all experimental conditions following predefined analytical quality criteria. Comparative analyses demonstrated preservation of the overall structural proteomic profile following processing and storage procedures, with retention of membrane-associated and extracellular structural proteins consistently exceeding 90% relative to the fresh reference condition. Quantitative reproducibility remained high across all experimental groups, with coefficients of variation ranging from 3.0% to 4.5% and strong inter-replicate Pearson correlations. Principal component analysis, hierarchical clustering, peptide/protein overlap analyses, and differential expression profiling demonstrated limited proteomic divergence while preserving the majority of quantified proteins within conserved abundance ranges. Complementary real-time live-cell kinetic imaging performed in non-malignant dermal-derived cells using the IncuCyte^® S3 platform demonstrated stable short-term confluence kinetics and cellular viability exceeding 92% over 48 h across all evaluated formulations. No sustained proliferative suppression or detectable morphological evidence of cytotoxicity was observed. Collectively, these findings support the preservation of compositional stability, analytical reproducibility, and short-term ex vivo cellular compatibility across defined processing and storage conditions. These integrated proteomic and kinetic datasets provide a quantitative framework for the analytical evaluation of processed extracellular phospholipoproteic preparations, while functional barrier activity, membrane incorporation, lipid raft engagement, and long-term tissue-level effects remain to be addressed in dedicated future studies. Full article

Fusing infrared and visible images can effectively compensate for the inherent limitations of each modality in different scenes, resulting in fused images that contain richer information. However, existing methods often struggle to balance global dependency modeling with local detail preservation and to effectively coordinate heterogeneous local and global features during fusion. To address these issues, this paper proposes a Significance-Preserving Progressive Fusion Network (SiPFusion). First, a progressive feature extraction framework was designed, which hierarchically extracts multi-scale local features using CNNs and then models long-range dependencies across scales via a Transformer-based global module. To adaptively integrate local-global complementary features, a significance-preserving fusion module was designed to obtain significance attention maps with a spatial selection mechanism, enabling dynamic fusion of multi-source features. Furthermore, we propose a significance similarity loss function that leverages intermediate feature guidance to enhance structural consistency and preserve salient-region information in the fused image. Extensive experiments on the MSRS, RoadScene, and TNO datasets demonstrate that SiPFusion achieves competitive visual quality and strong overall quantitative performance against 15 state-of-the-art fusion methods, obtaining leading results on most evaluated metrics. Full article

Atmospheric haze is a major source of image degradation in Earth observation systems, reducing visibility, distorting spectral information, and obscuring surface details in remote sensing imagery. Physics-based dehazing methods often hinge on simplified atmospheric assumptions, whereas purely data-driven networks struggle with ultra-high-resolution overhead imagery and the wide spatial variability of haze. To address these challenges in a way that respects the characteristics of very large remote sensing scenes, we introduce WaveDiff-R, a wavelet-guided diffusion framework with residual sub-band enhancement. Rather than running diffusion directly in the full spatial domain, WaveDiff-R performs a multi-level discrete wavelet transform (DWT) to separate low- and high-frequency components in a geometry-aware manner. The wavelet-guided diffusion module (WGDM) performs conditional diffusion only on the low-frequency approximation coefficients $A^K$ after a K-level DWT, reducing the denoising target by $4^K$ while restoring global luminance and chromaticity. In parallel, the residual sub-band enhancement module (RSEM), built with residual state space blocks (RSSBs), refines the high-frequency sub-bands, recovering sharp edges and textures by jointly modeling long-range dependencies and local details. This collaborative design couples global consistency with fine-grained fidelity while maintaining an efficiency suitable for real-world remote sensing pipelines. Extensive experiments on six benchmark datasets covering synthetic and real scenarios showed that WaveDiff-R achieved consistently strong results, surpassing state-of-the-art natural-image and remote sensing dehazing baselines in both quantitative metrics and visual quality. Full article

Background: Delta large-channel endoscopic decompression is an emerging, minimally invasive approach for lumbar degenerative disease, but its comparative effectiveness and perioperative performance have not been comprehensively synthesized. We performed a systematic review and meta-analysis to evaluate clinical, functional, and safety outcomes of Delta large-channel endoscopy relative to established decompression techniques. Methods: PubMed, Embase, and Scopus were searched from database inception through July 2025 (PROSPERO #CRD420251107750). Peer-reviewed English-language studies reporting extractable outcomes after Delta large-channel endoscopic surgery in adults were included. Random-effects meta-analyses were used for pooled comparisons. Results: Nine studies met the inclusion criteria, comprising 737 patients overall, including 379 treated with Delta large-channel endoscopy and 358 treated with comparator procedures. Reported outcome assessment generally ranged from 1 week to 12 months postoperatively, or variable latest follow-up timepoints, and mean follow-up was inconsistently reported. All studies originated in China and included five retrospective cohorts, two randomized controlled trials, one prospective cohort, and one case series. The pooled mean operative time for Delta procedures was 88.28 min (95% CI, 79.24–97.31), pooled mean intraoperative blood loss was 24.58 mL (95% CI, 11.14–38.02), and pooled mean hospital stay was 4.59 days (95% CI, 2.87–6.31). Compared with microscopic, endoscopic, and open techniques, Delta large-channel endoscopy showed no statistically significant differences in operative time (MD = 6.95 min, 95% CI: −11.51–25.40 min; p = 0.4037), intraoperative blood loss (MD, −40.62 mL; 95% CI, −83.68–2.44; p = 0.0598), 3-month ODI change from baseline (MD, 1.26; 95% CI, −2.17–4.69; p = 0.3661), or complication rates (OR, 0.67; 95% CI, 0.40–1.12; p = 0.1094). Delta procedures were associated with shorter hospital stay (MD, −1.69 days; 95% CI, −2.83 to −0.56; p = 0.0122) and marginally greater improvement in six-month VAS low back pain change from baseline (MD, 0.28; 95% CI, 0.26–0.29; p = 0.0002), though clinically insignificant. The pooled complication rate for Delta procedures was 6.0% (95% CI, 4–10%) and reported rates of excellent or good MacNab outcomes ranged from 80 to 93% across Delta cohorts. Conclusions: Delta large-channel endoscopy may provide clinical and functional outcomes comparable to established decompression techniques, with similar safety and potential perioperative benefits, including shorter hospitalization. However, these findings are based on limited, low-certainty evidence with high heterogeneity and should be interpreted as exploratory rather than definitive. Additional multicenter studies with longer follow-up, broader geographic representation, and higher methodological quality are necessary before definitive conclusions regarding comparative effectiveness can be established. Full article