Search Results (277)

Background/Objectives: Tumor–stroma ratio (TSR) and tumor-infiltrating lymphocytes (TILs) were suggested as prognostic markers in oral squamous cell carcinoma (OSCC). Identification of markers assessable in preoperative biopsies that could guide treatment planning is of great importance. This study aimed to evaluate the concordance and prognostic impact of TSR and TILs in preoperative biopsies and matched resection specimens of OSCC. Methods: This study included 100 patients with OSCC. TSR and stromal TILs were evaluated on hematoxylin and eosin-stained slides of biopsies and paired resection specimens and categorized (into low TSR and high TSR; high TILs and low TILs). The agreement between resections and biopsies, and the prognostic significance and clinicopathological correlations of TSR and TILs, were investigated. Results: For TSR, substantial agreement between preoperative biopsies and surgical specimens (kappa correlation coefficient 0.713) was demonstrated. The assessment of TILs showed poor concordance between biopsies and resections (kappa correlation coefficient 0.372). For both biopsies and resections, Cox regression showed an independent negative prognostic impact of low TSR on disease-free, disease-specific, and overall survival. Independent prognostic value of TILs evaluated in biopsies was not found, and the negative prognostic impact of low TILs on disease-free and overall survival was observed only in the main resection specimens. Conclusions: TSR evaluated in preoperative biopsies was highly concordant with results in main resection specimens and may provide significant information for OSCC prognostication, risk stratification, and treatment decisions. In contrast, TILs evaluated in biopsies showed poor concordance with main resection specimens and failed to demonstrate prognostic significance. Full article

Non-exhaust particulate emissions are expected to remain a relevant source of traffic-related air pollution, including an increase in electrified vehicle fleets. Particle formation results from tribological interactions and is influenced by both operating conditions and friction material system. This study presents an extended measurement methodology under application-relevant tribological conditions for the reproducible quantification of PM₁₀ and PM_2.5 emissions from dry-running friction systems and applies it to a systematic investigation of operating parameter and friction pairing effects. A dry inertial brake test bench with an enclosed friction chamber and integrated aerosol measurement chain was used under controlled tribologically relevant conditions. Specific friction work and specific friction power were varied by adjusting sliding velocity, contact pressure, and inertial load. Six friction pairings, comprising four representative friction lining types combined with either C45 cast steel or GGG40 gray cast iron, were examined. In situ PM₁₀ and PM_2.5 measurements were complemented by gravimetric wear and microstructural analyses. The results show that specific friction work has a direct influence on PM₁₀ and PM_2.5 emissions, whereas the independent effect of contact pressure is secondary. Friction power exhibits material-dependent effects. Emissions also vary strongly with friction pairing, indicating that operating conditions and material system must be considered jointly when assessing low-emission brake systems. Full article

The Persistent Scatterers InSAR (PSInSAR) technology, which utilizes pixels with stable phases to extract ground deformation, is an effective tool for large-scale, long-period surface monitoring applications. It has been widely applied to land subsidence monitoring, earthquake research, and infrastructure risk management. Furthermore, some studies have successfully employed this method to monitor the progressive motion of creeping in landslide areas. However, these regions containing active landslides are usually covered by canopy layers, which cause low coherence in InSAR processing and reduce the number of stable pixels, thereby preventing long-term period monitoring in those areas. In this study, the supervised deep learning model, U-Net, based on a convolutional neural network, is applied to the differential InSAR dataset acquired from Sentinel-1 to improve persistent scatterer selection. A well-processed PSInSAR result, utilizing 55 Sentinel-1 images acquired from 5 November 2014 to 19 December 2017, is introduced as a dataset for model training. The pixel-based Persistent Scatterer (PS) labels used for model training are identified using the StaMPS software. The model is designed to identify the distributed scatterer (iDS) index using a single pair of SAR images. As a result, more iDS pixels can be obtained from a single interferogram, indicating a significant improvement over the StaMPS algorithm. The line-of-sight velocity and time series of PS pixels from the model prediction show a long-term uplift on the upper slope, which represents downslope sliding in the target area. Furthermore, some iDS pixels exhibit a seasonal deformation on the lower part of the slope. The capability for these additional deformation analyses underscores the potential of this new deep-learning-based approach. Full article

Context: In numerous low- and middle-income nations, malaria remains a significant issue due to the challenges associated with diagnosing it through thin blood smears. The appearance of images can vary significantly depending on the microscope type, magnification, lighting conditions, slide preparation methods, and staining techniques. Due to the delicate morphology of parasites, false negatives might adversely affect patient care. Objective: To achieve optimal outcomes from validation, it is essential to construct a robust and easily replicable process. This pipeline should integrate the optimal elements of classical machine learning and end-to-end deep learning, enhance reliability by pairwise ensembling, and select ensemble weights in a logical, data-driven manner. Method: To achieve our objective, we propose two tracks. The initial track encompasses real-time augmentation, convolution-based feature extraction, and the training of calibrated classical classifiers. The second module focuses on training many convolutional networks from inception to completion. Subsequently, we construct paired ensembles and employ a hybrid methodology to select convex weights for combining the findings. This method initially evaluates a set of candidate weights and then refines them to maximise validation accuracy. Results: The precision of the two-track architecture consistently improves, transitioning from conventional baselines to end-to-end models. Optimal and consistent enhancements are achieved through weighted ensembling. Utilising optimised fusion reduces the incidence of false negatives for subtle parasites and false positives caused by staining artefacts. This yields an accuracy of 96.35% on the reserved data and reduced variance across folds. Conclusions: The integration of augmentation, multiple modelling tracks, and optimal pairwise ensembling yields the highest accuracy in categorising malaria smears. It facilitates further enhancements by incorporating supplementary models, multi-class extensions, and operating-point calibration. Full article

Starved lubrication poses a critical challenge to hybrid ceramic bearings operating under severe conditions. This study investigates the tribological behavior of carburized 20CrMo steel sliding against Al₂O₃ ceramic balls and GCr15 steel balls under dry sliding, with oil-lubricated tests as a reference. Under oil lubrication, the 20CrMo/Al₂O₃ pair exhibits superior wear resistance, attributed to the high hardness of the ceramic counterpart. Under dry sliding, however, this pair shows a slightly lower friction coefficient but a wear rate approximately three times that of the 20CrMo/GCr15 pair. This counterintuitive behavior stems from two mechanisms: lower contact stress and friction-induced work hardening in the GCr15 pair, which together suppress wear. Further analysis reveals that secondary carbides in the carburized layer detach under repeated high shear stress, acting as hard third-body abrasives and accelerating surface damage. These findings highlight that hybrid ceramic bearings are more susceptible to lubrication failure than all-steel bearings. Under heavy loads and poor lubrication, residual compressive stress plays a key role in governing the tribological behavior of carbides on carburized surfaces. Full article

Cervical cytology screening remains dependent on manual analysis, which is time-consuming and subject to variability. This study proposes a leakage-free hybrid deep learning framework for multiclass classification of cervical cells extracted from whole-slide Papanicolaou images. A fine-tuned DenseNet121 feature extractor was combined with three classifiers: Support Vector Machine (SVM), Stacked Extreme Learning Machine (SELM), and Cascaded Deep Forest (CDF). Experiments were conducted on the CRIC Cervix Collection dataset using slide-level data partitioning and group-aware stratified 7-fold cross-validation. Model comparison followed a paired non-parametric protocol (Friedman test with Wilcoxon post hoc and Holm correction). DenseNet121 + CDF achieved the highest cross-validation Accuracy (0.7370 ± 0.0357), significantly outperforming SVM (0.6644 ± 0.0287) and SELM (0.6431 ± 0.0471) (χ²(2) = 11.14, p = 0.0038; Kendall’s W = 0.79). Independent testing showed competitive generalization across models. These results support the statistical robustness of the Cascaded Deep Forest-based hybrid architecture for multiclass cervical cytology classification under realistic slide-level conditions. Full article

Battery state-of-health (SoH) estimation is central to transportation electrification because it conditions safety limits, warranty accounting, power capability management, and long-horizon fleet optimization. Although deep temporal architectures can achieve high laboratory accuracy, field deployment is frequently limited by laboratory (Lab)-to-field (L2F) domain shift that alters input statistics, feature definitions, and noise regimes. Under such a shift, predictors may remain strongly monotonic, preserving degradation ordering and become operationally unreliable due to systematic output distortion (e.g., compression/warping of the SoH scale). A deployment-complete L2F transfer learning pipeline is presented, built around a gated Temporal Convolutional Network (TCN)–Transformer fusion backbone, domain-specific adapters and heads, alignment-regularized fine-tuning, and row-level inference via sliding-window overlap averaging. To address the dominant deployment failure mode, a Safe Calibration stage robustly filters calibration pairs and selects among candidate calibrators under a strict do-no-harm criterion. On an unseen deployment stream (2154 labeled rows), overlap-averaged raw inference achieves MAE = 0.0439, RMSE = 0.0501, and R² = 0.7451, consistent with mid-to-high SoH range compression, while Safe Calibration (Isotonic-Balanced selected) corrects nonlinear scaling without violating monotonic structure, improving to MAE = 0.0188, RMSE = 0.0252, and R² = 0.9357 to obtain a complete understanding of the challenges due to domain shifts, evaluation is extended to include other architecture baselines such as TCN-only, Transformer-only, Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM), and a Ridge regression baseline. Also added is explicit alignment and calibration ablations that include CORAL off/on, that is, none vs. Safe-Global vs. Context-Aware under identical leakage-safe splits and the same overlap-averaged deployment inference operator. This work goes beyond peak-score reporting and looks at the robustness of a pipeline under domain shift, which is quantified across four random seeds and multiple deployment streams, with uncertainty summarized via mean ± std and bootstrap confidence intervals for Mean of Absolute value of Errors (MAE)/Root of the Mean of the Square of Errors (RMSE) computed from per-example absolute errors. Full article

Helical gear transmissions in wind turbine gearboxes operate under high torque, variable speed, and complex rolling–sliding contact conditions, where friction-induced wear evolves in a spatially non-uniform manner. However, most existing dynamic models assume uniform or mild wear and therefore fail to capture the nonlinear coupling between localised tooth surface degradation, gear mesh dynamics, and vibration response. In this work, a nonlinear dynamic model of a helical gear pair is formulated by incorporating time-varying mesh stiffness, elasto-hydrodynamic lubrication (EHL)-based friction forces, and wear-dependent contact geometry. The governing equations of motion are derived to explicitly account for the influence of inhomogeneous tooth wear on the contact load distribution and frictional excitation during meshing. Wear evolution is represented as a spatially varying modification of tooth surface topology, enabling the progressive coupling between wear depth, mesh stiffness perturbations, and dynamic transmission error. The model is employed to analyse the effects of non-uniform wear on system stability, vibration spectra, and dynamic response under wind turbine operating conditions. Numerical results reveal that uneven wear introduces nonlinear modulation of gear mesh forces and generates characteristic sidebands and amplitude variations in the vibration signal that are absent in conventional mild-wear formulations. These wear-induced dynamic features provide mathematically traceable indicators for the onset and progression of uneven tooth degradation. The proposed framework establishes a physics-based link between wear evolution and measurable vibration responses, providing a rigorous foundation for advanced vibration-based diagnostics and model-driven condition monitoring of wind turbine gearboxes. Full article

This study presents a harmonic-based method for non-contact heart rate (HR) estimation from continuous-wave (CW) Doppler radar signals, validated across multiple species including humans and small animals (cat). Traditional frequency-domain methods struggle when the HR fundamental frequency is weak or overlaps with respiratory components. The proposed approach addresses this by identifying three higher-order HR harmonics (2nd, 3rd, and 4th) then reconstructing the HR fundamental frequency from their integer ratios (3/2, 4/3, 2/1). The algorithm processes 20-s sliding windows (1-s overlap) using bandpass filtering to remove respiratory components and HR fundamental while preserving higher harmonics, followed by Power Spectral Density (PSD) analysis. When a complete harmonic set cannot be found, the proposed algorithm switches to harmonic pair detection, enhancing robustness when one harmonic is absent or attenuated. Besides, an adaptive tolerance mechanism enables detection under non-ideal conditions. The method was validated using a public human dataset and an experimental cat dataset with varied positions (supine/prone) and anesthesia levels (1–3% isoflurane). For humans, the algorithm achieved HR Accuracy consistently above 98% with an average RMSE of 1.33 bpm (MAPE: 1.29%, MAE: 0.86 bpm) and Bland-Altman bias below 0.9 bpm. For the cat dataset, performance was even better with HR Accuracy remaining above 99%, an average RMSE of 0.39 bpm (MAPE: 0.22%, MAE: 0.30 bpm), and bias below 0.14 bpm. Full article

Introduction: Pancreatic ductal adenocarcinoma (PDAC) represents one of the most aggressive, heterogeneous, and lethal malignancies in humans. Mismatch repair (MMR) proteins constitute a fundamental component of the DNA mismatch repair pathway, which is responsible for correcting replication-associated errors, including incorrect base pairings and small insertions or deletions. This study aims to evaluate the immunohistochemical expression of MSH2, MSH6, MLH1, and PMS2 in resected PDAC and to analyze their association with pTNM stage, perineural and lymphovascular invasion, HER2 and HER3 expression, and tumor volume. Methods: A cohort of 106 patients with currative intent Whipple procedure was evaluated, their corresponding paraffin blocks and slides were analyzed using tissue microarray. Immunohistochemical analysis of MLH1, PMS2, MSH2, and MSH6 was performed. Patients were grouped based on MMR expression profiles: isolated MutS loss (MSH2/MSH6), and isolated MutL loss (MLH1/PMS2). Results: Among the 106 subjects evaluated, 13 (12.3%) exhibited isolated MutS complex loss and 16 (15.1%) showed MutL complex loss. A total of 7 patients (6.6%) demonstrated concurrent loss of all four MMR proteins, representing a pattern suggestive of MMR deficiency MSI-H. These ones were significantly younger (median 56 vs. 64 years, p = 0.0492) and had distinct T-stage distribution (p = 0.0237). Two intermediate subgroups were identified: five patients with isolated MutL loss and one patient with isolated MutS loss. HER3 positivity was observed in 3/5 of the intermediate MutL cases and HER2 positivity in only one. Conclusions: MMR deficiency and potential MSI-H status were identified to be relevant prognostic biomarkers for pancreatic cancer patients, with MSI-H patients displaying a younger age and distinct tumor features. Full article

Wear, a critical factor governing the performance and durability of mechanical systems, is typically characterized using point-contact and line-contact test configurations. However, it remains unclear whether the wear trends observed in one test configuration would be observed in the other configuration under the same nominal conditions. In this study, ball-on-disk (ASTM G99) and block-on-ring (ASTM G77) tests were conducted under an identical maximum Hertzian contact stress and sliding speed, using the same material pair and lubricating oil, to clarify which contact configuration exhibits more wear and why. The results show that, under the same Hertzian contact stress, the line-contact configuration exhibits a specific wear rate two orders of magnitude higher than the point-contact configuration, despite exhibiting a lower and more stable coefficient of friction. The disk wear is negligible and the ball shows only mild material loss, whereas the line-contact system displays wear rates several orders of magnitude higher, with the rotating ring contributing the dominant share of the total wear. White-light interferometry and scanning electron microscopy observations reveal directional, groove-dominated surface morphologies on the ball and disk, while wear on the block is confined to edge-localized regions and the worn ring surface has smooth, polished morphology. Energy-dispersive X-ray spectroscopy confirms that a Zn- and P-rich tribofilm forms exclusively on the ring surface. Finite element analysis shows stress amplification at the finite line-contact edges, explaining the observed wear severity. These results demonstrate that matching Hertzian contact stress alone is insufficient to ensure comparable wear behavior between point and line contacts. Full article

Clear-cell renal cell carcinoma (ccRCC) accounts for the majority of kidney cancer diagnoses and exhibits widely variable clinical behaviour. The dataset described here was generated to support the discovery of robust biomarkers of tumour cell-cycle arrest and to inform the risk-stratified management of ccRCC. We assembled four independent cohorts including 480 patients from the UK arm of the SORCE adjuvant trial, 300 patients from a surgically treated series in Korea, 120 patients from a retrospective Scottish cohort, and a paired primary–metastatic cohort comprising 62 patients. Formalin-fixed paraffin-embedded nephrectomy specimens were processed for routine hematoxylin and eosin (H&E) histology, and for multiplex immunofluorescence (mIF). The mIF panels detect the cyclin-dependent kinase inhibitor p21^CDKN1a, the DNA replication licencing factor MCM2, endoglin/CD105, Lamin B1 and nuclear DNA (Hoechst). Whole-slide images (WSIs) were acquired at high resolution, and artificial-intelligence pipelines were used to segment nuclei, classify individual cells into arrested phenotypes, and calculate the fraction of cells. Accompanying metadata include demographics, tumour stage, grade, Leibovich score, treatment arm (sorafenib/placebo), relapse events, and disease-free survival. All images and derived tables are released under a CC0 licence via the BioImage Archive, ensuring unrestricted reuse. This multi-cohort dataset provides a rich resource for studying cell-cycle arrest and proliferation markers, training image-analysis algorithms, and developing prognostic signatures in RCC. Full article

Tropical cyclones pose major risks to life and property, especially as coastal populations grow and climate change increases the likelihood of intense storms, making seasonal prediction of tropical cyclones an important scientific and societal goal. This study uses HURDAT best-track records from 1950 to 2024 to quantify annual tropical cyclone, hurricane, and major hurricane counts across the Atlantic basin, Caribbean Sea, and Gulf of Mexico. These nine targets are paired with 34 monthly climate predictors from NOAA and NASA GISS—including SST and ENSO indices, Main Development Region (MDR) wind and pressure fields, and latent heat flux empirical orthogonal functions—evaluated under nine predictor-set configurations. Four forecasting approaches were developed and tested under operationally realistic conditions—Lasso regression, K-nearest neighbors (KNN), an artificial neural network (ANN), XGBoost—using a 30-year sliding-window cross-validation design and a Poisson log-likelihood skill score relative to climatology. Lasso performs reliably with concise, physically interpretable predictors, while XGBoost provides the most consistent overall skill, particularly for basin-wide total cyclone and hurricane counts. The skill of ANN is limited by small sample sizes, and KNN offers only marginal improvements. Forecast skill is the highest for basin-wide storm totals and decreases for regional major-hurricane targets due to lower event frequencies and stronger predictability limits. Full article

The microstructure and wear behaviors of Mg₂Si/Al composites with 0 wt.%, 5 wt.%, and 10 wt.% SiC particles were studied using XRD, OM observation, SEM observation, EDS analysis, an extraction experiment, a hardness test, and the dry sliding wear test. It is shown by the results that after the addition of 10 wt.% SiC particles, the population of primary Mg₂Si particles increased, while the mean size of these particles reduced from 40 ± 10 μm (in the SiC-free composite) to 25 ± 8 μm. Both the matrix and the eutectic structure were refined. The tetrakaidecahedral morphologies of Mg₂Si crystals were confirmed by the results of extraction tests. The wear test results with GCr15 steel as the friction pair show that the Mg₂Si/Al composite with 10 wt.% SiC particles displayed more favorable wear resistance than the specimens with 0 wt.% and 5 wt.% SiC particle additions under both constant load and constant sliding velocity conditions. Under applied loads of 10 N, 20 N, and 30 N at a fixed sliding speed of 300 r/min, the wear rate of the Mg₂Si-Al composites reinforced with 10 wt.% SiC particles was 36.01%, 48.29%, and 23.32% lower than that of the SiC-free composites, respectively. When the sliding speed was set to 300 r/min, 550 r/min, 750 r/min, and 1000 r/min under a constant applied load of 20 N, the wear rate of the 10 wt.% SiC-reinforced Mg₂Si-Al composites was reduced by 40.37%, 40.87%, 26.20%, and 25.78%, respectively, compared with the SiC-free counterparts. The wear failure mechanisms of (Mg₂Si + SiC_P)/Al composites were mainly adhesive wear and abrasive wear, but the proportion of oxidation wear increased after the addition of the SiC particles. Full article

Vulcanized NR-SBR is widely used in vehicle components; however, its irreversible crosslinking limits recyclability and contributes to the large number of tires discarded annually worldwide, and in this context, this work presents an experimental comparative assessment of the tribological behavior of conventional tire rubber and silicone VMQ, motivated by a wheel concept based on a detachable tread aimed at improving durability and sustainability rather than proposing an immediate material substitution. Wear and friction behavior were investigated under abrasive and self-friction conditions using pin-on-disk testing with an abrasive counterpart representative of asphalt, supported by optical and scanning electron microscopy. The results show that NR-SBR undergoes severe abrasive and erosive wear, characterized by deep and irregular wear tracks, pronounced fluctuations in the dynamic friction coefficient, and strong sensitivity to load and sliding speed, particularly during the initial stages of track formation. In contrast, VMQ exhibits mild abrasive wear dominated by viscoelastic deformation, leading to shallow and stable wear tracks, lower friction coefficients, and significantly reduced material loss once the contact track is fully developed. These differences are attributed to the distinct mechanical responses of the elastomers, as the higher hardness and limited strain capacity of rubber promote micro-tearing and unstable material removal, while the high elasticity of silicone enables stress redistribution and stable contact conditions under abrasive loading. UV aging increases stiffness of rubber, resulting in reduced wear and friction, while silicone remains largely unaffected after 750 h due to the stability of its Si–O–Si backbone. Self-friction tests further indicate that smooth silicone sliding against rubber yields the lowest friction values, highlighting a favorable material pairing for detachable tread concepts. Factorial design analysis confirms material type as the dominant factor influencing both wear and friction. Overall, for the specific materials and operating conditions investigated, VMQ demonstrates higher durability, greater tribological stability, and improved aging resistance compared to NR-SBR, providing experimental evidence that supports its potential for long-life, more sustainable detachable tread applications. Full article