Search Results (129)

A complex curved reflector made from a 40% silicon–aluminum alloy (AlSi40) can meet the requirements of optical systems operating across the infrared, near-infrared, and visible bands. It enables an athermalization design with simplified alignment and assembly, while offering high manufacturing efficiency and low costs. This makes it ideal for widespread use in high-end optical systems. As an enabling technology for the fabrication of AlSi40 freeform mirrors, error compensation in ultra-precision (UP) diamond turning is currently a research hotspot; however, current error compensation methods still have considerable room for improvement in terms of both accuracy and manufacturing efficiency. To address this issue, this study proposes an efficient and highly accurate method: a polar grid is defined in the machining coordinate system, and the corresponding surface point cloud is calculated. Using measured point clouds from reference spheres and freeform form error in the measurement coordinate system, mounting pose errors and form error with measurement error removed are determined via least squares. Machining error at grid points is then calculated via coordinate transformations and bicubic spline interpolation, and applied to correct cutter contact points (CCPs). Cutter location points (CLPs) are finally obtained using piecewise cubic spline fitting and a bisection method. With this method, average form error of four AlSi40 substrates improved from RMS 114.8 nm to 47.9 nm, and for four AlSi40 substrates with nickel–phosphorus (NiP)-plated surfaces, from RMS 71.3 nm to 31.1 nm. The compensated accuracy meets near-infrared stellar tracker requirements without polishing, greatly enhancing freeform mirror manufacturing efficiency. Full article

Achieving high-precision overlay target center localization is critical for image-based overlay (IBO) metrology in advanced semiconductor manufacturing. This paper proposes a novel IBO target localization algorithm based on symmetry center matching. Leveraging the symmetry design of the IBO optical system as a physical prior, the algorithm reformulates center localization as a global correlation optimization problem. The grayscale projection profile of a single-sided edge is extracted, spatially mirrored, and used as a reference template for sliding correlation matching against the opposite edge. The symmetry center is then determined from the peak of the Pearson correlation coefficient curve. Simulation results demonstrate a center localization accuracy better than 0.00013 pixels (3$\sigma$), with repeatability precision remaining within 0.012 pixels (3$\sigma$) under stringent noise and blur conditions. Experimental validation yields object-space repeatability precision of 0.129 nm (3$\sigma$) and 0.144 nm (3$\sigma$) in the X and Y directions, respectively, surpassing the 0.32 nm measurement uncertainty requirement for advanced process nodes. The average single-frame processing time is approximately 0.07 s, demonstrating that the proposed algorithm simultaneously satisfies the demands of high precision and high throughput. Full article

Background/Objectives: Metabolic dysfunction-associated steatotic liver disease (MASLD) is now the leading cause of chronic liver disease globally, mirroring the increasing prevalence of obesity, insulin resistance, and type 2 diabetes. Early detection of hepatic steatosis is vital for cardiometabolic risk assessment; however, conventional imaging is costly and impractical for population screening. This study aimed to develop interpretable machine-learning models to predict ultrasound-detected hepatic steatosis within the MASLD spectrum using routinely available clinical and biochemical data. Methods: We analyzed data from 644 adults, 50% of whom had ultrasound-detected hepatic steatosis. Preprocessing, imputation, and feature selection were implemented within a single scikit-learn pipeline to avoid information leakage. An Elastic Net-regularized logistic regression identified the top 20 predictors, which were subsequently used across nine supervised machine learning (ML) classifiers. Model performance was evaluated via repeated stratified 5-fold cross-validation (25 resamples) using accuracy, F1 score, sensitivity, specificity, Youden’s J, balanced accuracy, and Area Under the Receiver Operating Characteristic Curve (AUROC). Interpretability was assessed using SHapley Additive exPlanations (SHAP). Results: Participants with ultrasound-detected hepatic steatosis exhibited greater adiposity, insulin resistance, and dyslipidemia compared with controls [p < 0.05 for body mass index (BMI), waist circumference, glucose, glycated hemoglobin (HbA1c), triglycerides]. Elastic Net selection highlighted Weight, Ponderal Index, Fibrosis-4 Index (FIB-4), blood urea nitrogen (BUN)/Creatinine ratio, Aspartate Aminotransferase to Platelet Ratio Index (APRI), and Visceral Adiposity Index as the strongest predictors. Logistic Regression and Gradient Boosting achieved the best performance (accuracy = 0.65 ± 0.03; AUROC = 0.71 ± 0.04; balanced accuracy = 0.66 ± 0.06), outperforming rule-based indices such as Fatty Liver Index (FLI) and Hepatic Steatosis Index (HSI) reported in the literature. SHAP analysis confirmed clinically coherent feature effects, with higher anthropometric and hepatic injury indices increasing the predicted probability of ultrasound-detected hepatic steatosis. Conclusions: Routinely available clinical and biochemical parameters can predict hepatic steatosis with moderate accuracy using transparent, interpretable ML models. Logistic Regression and Gradient Boosting provided best discrimination and robust internal performance, offering a pragmatic, low-cost approach for early identification of ultrasound-detected hepatic steatosis within the MASLD spectrum in primary and metabolic care settings. Full article

Background/Objectives: First-trimester preeclampsia (PE) screening is most widely implemented using the Fetal Medicine Foundation (FMF) algorithm, which combines maternal factors with biophysical and biochemical markers via a competing-risks/Bayes framework to produce individualized risks and guide prophylaxis decisions. We aimed to compare commonly used machine-learning (ML) classifiers (logistic regression, random forest, XGBoost) against FMF a priori and a posteriori risk estimates in a Romanian screening cohort. Methods: We analyzed 1583 singleton pregnancies screened at 11–14 weeks’ gestation. Primary analyses excluded aspirin-treated women to reduce treatment-induced outcome modification. We evaluated two feature sets mirroring FMF structure: (1) a maternal-factor “a priori” set and (2) a “a posteriori” set additionally incorporating mean arterial pressure (MAP), uterine artery pulsatility index (UtA-PI), and Pregnancy-Associated Plasma Protein A (PAPP-A). Models were trained using stratified repeated cross-validation (5-fold × 10 repeats) and evaluated using AUC-ROC, DeLong tests, and sensitivity at 10% false-positive rate. Calibration of the model, sensitivity analyses and decision-curve analysis (DCA) were also assessed. Results: In the a priori comparison, the best ML model was logistic regression (AUC 0.796) versus FMF prior risk AUC 0.841 (DeLong p = 0.349). The sensitivity at 10% false positive rate (FPR) was 33.3% for the model versus 50.0% for FMF model. In the a posteriori comparison, the best ML model was random forest (AUC 0.844) versus FMF posterior risk AUC 0.929 (DeLong p = 0.087), with sensitivity at 10% FPR of 57.1% for ML and 71.4% for FMF. Random undersampling did not improve ML performance. Including aspirin-treated pregnancies did not significantly change our results. Conclusions: In this study, the FMF competing-risks outputs outperformed or matched ML classifiers in both maternal-only and biomarker-augmented screening, and DCA favored FMF particularly for the a posteriori model. Full article

We study the center problem for polynomial maps $y=f\left(x\right)=-x-{\displaystyle \sum _{n=1}^{\infty }}{a}_n{x}^{n+1},$ arising from homogeneous algebraic curves $x+y+{\displaystyle \sum _{i=0}^n}{\alpha }_{n-i,i}{x}^{n-i}{y}^i=x+y+H_n(x,y)=0.$ While explicit conditions were previously known only for low even degrees $n=2,4,6,8,10$, their general structure remained conjectural. In this paper we resolve the case $n=12$ and prove that for all even degrees $n=2k$, the center condition is completely characterized by two families of algebraic relations: mirror symmetry conditions and alternating-sum conditions. The proof combines algebraic methods with a direct structural argument. In particular, the necessity part is established without relying on explicit formulas for focus quantities, instead, we make use of the involutive property of the associated map and analyze the symmetric difference $H_n(x,f\left(x\right))-H_n(f\left(x\right),x),$ which leads to a simple and rigorous characterization of the center condition. This provides a complete and conceptually transparent solution of the homogeneous center problem for polynomial maps. Full article

The present research article deals with the thermal degradation study of epoxy resins filled with hybrid nanostructured forms of carbon under oxidative conditions. In particular, the formulated polymer composites (denoted as HYB_0.1%_CNTs:GNs and HYB_0.5%_CNTs:GNs, respectively) consist of two kinds of fillers, namely multi-walled carbon nanotubes (CNTs) and graphene nanosheets (GNs), mixed together with two different total mass amounts: 0.1 and 0.5%. In both kinds of nanocomposites, three different CNT:GN mixing ratios were considered (5:1, 1:1, and 1:5, respectively), thus providing a total of six hybrid samples. The thermal behavior of these samples was studied by simultaneous thermogravimetry and differential thermal analysis (TG/DTA) under flowing air, and two processes took place in distinct temperature ranges. In each step, about 50% of mass loss is detected with an exothermic effect in the corresponding DTA curve, with the second one accompanied by an intense heat release. The kinetic analysis of the two-stage oxidative thermal degradation was investigated using a model-free isoconversional approach. A non-Arrhenian behavior of the temperature function k(T) was assumed, and lifetime prediction was estimated at temperatures close to those of the possible applications. Isoconversional analysis shows nearly constant activation energies for all composites except HYB_0.1%_5:1 (from 142 to 96 kJ·mol⁻¹), while lifetime predictions indicate that thermal stability increases with graphene content at 0.1% loading (HYB_0.1%_1:5) and with CNT content at 0.5% loading (HYB_0.5%_5:1), with uncertainties below 7%. Finally, because of the π–π bond interactions between the CNTs and the GNs dispersed in the epoxy resin matrix, an effective and remarkable electrical performance was found and a correlation with both electrical and morphological properties was established. In this regard, Tunneling Atomic Force Microscopy (TUNA) proved to be particularly powerful in allowing the simultaneous mapping of topography and localized conductive networks with exceptional sensitivity to nanofiller dispersion, such as CNTs and GNs. DC conductivity increased by up to nine orders of magnitude at 0.1 wt% hybrid loading (up to 3.73 × 10⁻⁴ S/m vs. 1.06 × 10⁻¹³ S/m for CNT-only), with nanoscale TUNA currents (−1.9 to 4.5 pA) mirroring macroscopic trends, while at 0.5 wt% all hybrids reached 10⁻² S/m, indicating reduced synergy once a fully developed conductive network is established. Full article

The study addresses the challenge of accurately simulating and visualising the kinematics of agricultural machinery during field operations. The research is motivated by the current lack of comprehensive guidelines for selecting optimal movement and turning modes under varying forward speeds, working widths, and field geometries. A spreadsheet-based environment was utilised to perform simultaneous kinematic simulation and trajectory visualisation. Turning manoeuvres were modelled using smooth composite curves, consisting of straight segments, clothoids, and circular arcs, with trajectories represented in a Cartesian coordinate system through geometric transformations including translation, rotation, and mirror symmetry. Continuity between curve elements was ensured by dimensional chains linking abscissas, ordinates, and direction angles at their start and end points. The influence of key operational factors—forward speed, angular turning velocity, working direction, and field boundaries—was evaluated for a range of turn types, including semicircle, pear-shaped, figure-eight, side exit, U-turn, and P-turn manoeuvres. Field experiments conducted on selected patterns confirmed that the proposed approach can reproduce actual trajectories with sufficient practical accuracy. These results demonstrate that spreadsheet-based kinematic modelling is a robust and accessible tool for optimising tractor–implement movement, enhancing operational planning, and providing a reliable framework for further research into machinery performance under complex field conditions. Full article

Pacific white shrimp (Litopenaeus vannamei) is a major aquaculture product worldwide. For consumers, discriminating domestic from imported sources of shrimp meat, and individual domestic sources, can be highly desirable because of the different meat quality and environmental contamination from geographically different origins of shrimp. This study evaluated the potential of stable isotope analysis (δ¹³C, δ¹⁵N, δ²H, δ¹⁸O) with chemometric models to authenticate the origins of Pacific white shrimp sold in China. Shrimp samples from domestic (Guangxi, Fujian, Shandong, Inner Mongolia) and foreign (Ecuador) sources were analyzed, using statistical analyses. The four-isotope model achieved 89.3% cross-validation accuracy in distinguishing domestic and foreign shrimp, with an overall prediction Area Under the Curve (AUC) of 0.901 (95% CI: 0.819–0.983)—significantly outperforming single-isotope models. Differences in δ¹³C and δ¹⁵N reflected feed source variations, while δ²H and δ¹⁸O (Variable Importance in the Projection (VIP) > 1, key discriminatory indicators) mirrored geographic environmental difference. Although δ¹⁵N did not differ significantly among groups, the combination of all four isotopes reduced limitations of individual δ²H/δ¹⁸O use. This approach enhanced the precision, reliability, and applicability of stable isotope analysis for origin authentication by leveraging complementary isotopic data and robust statistical frameworks. These findings demonstrate the proposed model’s potential as a cost-effective, copyright-compliant framework for shrimp origin authentication, with implications for isotopic traceability across food science fields. Full article

Ancient Chinese architecture, with its typical symmetrical structures, curved roofs, and upturned eaves presenting a unique architectural aesthetic, is a treasure of Chinese culture. Recently, unmanned aerial vehicle oblique photogrammetry and laser scanning technology have greatly facilitated the realistic replication of ancient buildings and have become crucial data sources for the HBIM of ancient buildings. However, parameter extraction and geometric model representation are more difficult because of the curved surfaces and upturned eaves of traditional Chinese roofs. As symmetrical features are typical of ancient Chinese architecture, the parameter quantity and modelling difficulty of the model representation can be effectively reduced by recognizing the symmetrical structure of traditional Chinese roofs and using “mirror replication” to quickly generate the other half of the model. Accurate symmetry detection and highly efficient parameter extraction are crucial for the HBIM of traditional Chinese roofs. Therefore, in this study, a deep learning network, namely, TCRSym-Net, is proposed to identify the symmetry from point clouds of traditional Chinese roofs. Each roof point cloud is then relocated and reoriented to obtain longitudinal and cross sections, and parametric modelling scripts are coded in Dynamo to model traditional Chinese roofs via curve lofting and solid Boolean operations. The experimental results reveal that the symmetry detection network is effective for symmetry detection, and five different types of traditional Chinese roofs are successfully recreated, which confirms the dependability of the method. Full article

Plastic pollution raises concerns for health and the environment. Plastics are not biodegradable but gradually erode to microplastic and nanoplastic particles spreading almost everywhere. Nanoplastics exhibit colloidal behavior. Thereby, their analysis can be accomplished by refractometry, preferably by an on-chip tool. We present a study of such colloids using a microfabricated Fabry–Pérot cavity with curved mirrors, which holds a capillary micro-tube used both for fluid handling and light collimation, resulting in an optically stable microresonator. Despite the numerous scatterers within the sample, the sub-millimeter scale cavity provides the advantages of reduced interaction length while maintaining light confinement. This significantly reduces optical loss and hence keeps resonance modes with quality factors (resonant frequency/bandwidth) above 1100. Therefore, small quantities of colloids can be measured by the interference spectral response through the shift in resonant wavelengths. The particles’ Brownian motion potentially causing perturbations in the spectra can be overcome either by post-measurement cross-correlation analysis or by avoiding it entirely by taking the measurements at once by a wideband source and a spectrum analyzer. The effective refractive index of solutions with solid contents down to 0.34% could be determined with good agreement with theoretical predictions. Even lower detection capabilities might be attained by slightly altering the technique to cause particle aggregation achieved solely by light. Full article

Background and Objectives: Contrast-enhanced mammography (CEM) provides both morphological and functional information and may reflect breast cancer biology similarly to Magnetic Resonance Imaging (MRI). Materials and Methods: This single-center retrospective study included 399 women with Breast Imaging Reporting and Data System (BI-RADS) category 0 screening mammograms who subsequently underwent CEM. A total of 76 malignant lesions (68 invasive cancers, 8 ductal carcinoma in situ (DCIS)) with complete imaging and pathology data were analyzed. Invasive cancers were classified into luminal A, luminal B, luminal B/Human Epidermal Growth Factor Receptor 2 (HER2)-positive, HER2-enriched, and triple-negative, and grouped as luminal (Group 1) versus HER2-positive/triple-negative (Group 2). Results: Luminal subtypes predominated (47 of 68, 69%), while 21 of 68 (31%) were HER2-positive or triple-negative. Most cancers appeared as masses with spiculated margins and heterogeneous enhancement. Significant differences were observed in mass shape (p = 0.03) and internal enhancement (p = 0.01). Luminal tumors were more often irregular and spiculated with heterogeneous enhancement, whereas the HER2-positive/triple-negative tumors more frequently appeared round with rim or homogeneous enhancement. Deep learning-derived malignancy scores (iCAD ProFound AI^®) demonstrated good diagnostic performance (area under the curve (AUC) = 0.744, 95% confidence interval (CI) 0.654–0.821, p < 0.001). The median AI score was significantly higher in malignant compared with benign lesions (70% [interquartile range (IQR) 47–93] vs. 38% [IQR 25–61]; Mann–Whitney U test, p < 0.001). Among malignant lesions, iCAD scores varied across molecular subtypes, with higher median values observed in Group 1 versus Group 2 (87% vs. 55%), although the difference was not statistically significant (Mann–Whitney U test, p = 0.35). Conclusions: CEM features mirrored subtype-specific phenotypes previously described with MRI, supporting its role as a practical tool for enhanced tumor characterization. Although certain imaging and AI-derived parameters differed descriptively across subtypes, no statistically significant differences were observed. As deep-learning models continue to evolve, the integration of AI-enhanced CEM into clinical workflows holds strong potential to improve lesion characterization and risk stratification in personalized breast cancer diagnostics. Full article

Background: Duchenne muscular dystrophy (DMD) results from pathogenic variants in the DMD gene, one of the most significant and most mutation-prone genes in the human genome. Although global mutation registries are well developed, genetic data from Central Asian populations remain extremely limited, leaving essential gaps in regional epidemiology and in the understanding of genotype–phenotype patterns. Methods: We conducted a retrospective analysis of patients with genetically confirmed dystrophinopathy in Kazakhstan. Variants were identified using multiplex ligation-dependent probe amplification (MLPA) for exon-level copy number alterations and next-generation sequencing (NGS) with Sanger confirmation for sequence-level changes. All variants were classified under ACMG guidelines. Statistical modelling incorporated mutation-class grouping, exon-hotspot mapping, reading-frame status, CPK stratification, chi-squared association testing, Spearman correlations, Kaplan–Meier ambulation survival curves, and multivariable logistic and Cox regression. Results: multi-exon deletions were the predominant mutation class, with a marked concentration within the canonical hotspot spanning exons 44–55. Recurrent deletions affecting exons 46–50 and 45–50 appeared in several unrelated patients. NGS confirmed severe protein-truncating variants, including p. Lys1049* and p. Ser861Ilefs*7. Phenotypic severity followed a consistent hierarchy: hotspot-associated deletions and early truncating variants showed the earliest loss of ambulation, whereas splice-site variants and duplications demonstrated the mildest courses. CPK levels correlated with the extent of genomic involvement, though extreme elevations did not consistently predict early functional decline. Regression models identified hotspot localization and out-of-frame effect as independent predictors of ambulation loss. Conclusions: This study provides the first statistically modelled characterisation of DMD gene mutations in Kazakhstan. While the mutational landscape largely mirrors global patterns, notable variability in clinical severity suggests the presence of population-specific modifiers. Integrating comprehensive molecular diagnostics with statistical-genetics approaches enhances prognostic accuracy and supports the development of mutation-targeted therapeutic strategies in Central Asia. Full article

The efficiencies of additive manufacturing (AM) over conventional processes have enabled the rapid production of aluminum (Al) alloys with AM. Because laser beam powder bed fusion (LB-PBF) parts do not offer the surface quality and geometrical accuracy for direct use, the functional surfaces of LB-PBF parts are usually machined by subtractive machining. The machinability of LB-PBF AlSi10Mg was studied in dry, MQL (used corn oil), and cryo-LN₂ cutting environments across distinct speed–feed combinations using CVD-AlTiN-coated carbide inserts, and surface integrity and tool life were quantified in terms of surface roughness (Ra) and flank wear (Vb), respectively. The lowest Ra (0.98–1.107 μm) was obtained with cryo-LN₂, followed by MQL and dry cutting environments, because the trends observed were consistent with the surface mechanisms observed in 3D topography and bearing curves. Similarly, the tool wear results mirrored the Ra results, lowest with LN₂ (0.087–0.110 mm), due to improved thermal management, reduced adhesion and abrasion, and shorter contact length. Cryo-LN₂ provided the best surface finish and tool life among all tested environments. To enable data-driven prediction, the limited dataset was augmented using SMOTE, and machine learning (ML) models were trained to predict Ra and Vb. CatBoost was found to yield the best Ra predictions (R² = 0.9090), while Random Forest and XGBoost yielded the best Vb predictions (R² ≈ 0.878). Full article

Organic electrochemical transistors (OECTs) are compelling artificial synapses because mixed ionic–electronic coupling and transport enables low-voltage, analog weight updates that mirror biological plasticity. Here, we engineered solid-state, polymer electrolyte-gated vertical OECTs (vOECTs) and elucidate how electrolyte molecular weight influences synaptic dynamics. Using Pg2T-T as the redox-active channel and pDADMAC polymer electrolytes spanning low- (~100 k), medium- (~300 k), and high- (~500 k) molecular weights, cyclic voltammetry reveals reversible Pg2T-T redox, while peak separation and current density systematically track ion transport kinetics. Increasing electrolyte molecular weight enlarges the transfer curve hysteresis (memory window ΔV_mem from ~0.15 V to ~0.50 V) but suppresses on-current, consistent with slower, more confining ion motion and stabilized partially doped states. Devices exhibit rich short- and long-term plasticity: paired-pulse facilitation (A₂/A₁ ≈ 1.75 at Δt = 50 ms), frequency-dependent EPSCs (low-pass accumulation), cumulative potentiation, and reversible LTP/LTD. A device-aware CrossSim framework built from continuous write/erase cycles (probabilistic LUT) supports Fashion-MNIST inference with high accuracy and bounded update errors (mean −0.02; asymmetry 0.198), validating that measured nonidealities remain algorithm-compatible. These results provide a materials-level handle on polymer–ion coupling to deterministically tailor temporal learning in compact, robust neuromorphic hardware. Full article

Accurate suction control underpins thermo-hydro-mechanical (THM) characterization of unsaturated soils, yet conventional polyethylene-glycol (PEG) osmotic methods suffer from membrane degradation, polymer intrusion, and marked temperature sensitivity. This study evaluates a potassium-neutralized poly (acrylamide-co-acrylic acid) hydrogel (PP) as a high-suction osmotic medium for water-retention testing of compacted kaolin using a sealed cell with a grade-42 filter paper separator (no semi-permeable membrane). The water-activity–suction relation of PP was calibrated with a chilled-mirror hygrometer (WP4C) over the high-suction domain, and temperature effects were assessed between 20–30 °C. The PP imposed stable target suctions across the practical engineering range, with cross-validation to WP4C of R² ≈ 0.985 and RMSE ≈ 0.09 MPa, and exhibited modest thermal sensitivity (~2–3% per 10 °C). Mass–time records showed a two-regime equilibration (rapid first-day moisture loss then slowing to asymptote), with time to 95% equilibrium t₉₅ ≈ 3–7 days depending on suction, and equilibrium within ~2 weeks under a normalized mass change, ${\displaystyle \frac{1}{m}}\left|{\displaystyle \frac{∆m}{∆t}}\right|<{\displaystyle \frac{0.1\%}{24h}}$ criterion. The resulting kaolin water-retention curves are smooth soil moisture factor (SMF) reproducible, and exhibited minor wetting–drying hysteresis (~20–25% gap at matched suctions). Collectively, the results indicate that PP provides a practical, membrane-free (in the semi-permeable sense) and accurate means to control high-range suction for unsaturated soil testing, showing only modest suction variations within the tested 20–30 °C range, while mitigating long-standing PEG limitations and simplifying laboratory workflows. Full article