Search Results (3,171)

Nickel-free high-nitrogen austenitic stainless steels (HNSS) are increasingly considered for biomedical applications owing to their excellent biocompatibility, corrosion resistance, and favourable mechanical properties. Their mechanical behaviour is strongly influenced by interstitial alloying, which can enhance both strength and work-hardening capability. However, the deformation mechanisms governing work hardening in HNSS, particularly at high nitrogen contents, remain insufficiently understood. Here, we investigate the effect of nitrogen on the mechanical response and associated deformation mechanisms of a nickel-free austenitic stainless steel containing 1 wt.% nitrogen. Tensile tests revealed a combination of high strength, sustained work hardening, and good ductility, with further strengthening achievable through cold rolling. The annealed material exhibited a distinct constant strain-hardening regime during the early stages of deformation. Using electron backscatter diffraction and in situ tensile testing, planar dislocation slip, evidenced by slip traces, was identified as the predominant deformation mechanism, with no detectable strain-induced martensitic transformation or mechanical twinning in this regime. Mechanical twinning was observed only at higher strains (>15%) and appears to make a secondary, limited contribution to the overall deformation. Despite the relatively high calculated stacking-fault energy, the mechanical and microstructural observations are consistent with strongly restricted cross-slip and localized dislocation glide. These findings suggest that chemical short-range order may play an important role in governing the distinctive plasticity and sustained work-hardening behaviour of Ni-free HNSS. Full article

Surface waviness is one of the most important factors affecting bearing vibration. How to effectively control waviness in actual production is a technical challenge in bearing mass production at present. This study established a ball bearing vibration analysis model based on the relationship between raceway waviness and bearing vibration, analyzed the contact between bearing balls and raceway waviness, studied the influence of waviness amplitude A, order nv, ball diameter Dw and rotational speed v on rolling bearing vibration, and given a bearing vibration prediction method and optimization control model based on waviness amplitude and order. Taking the angular contact ball bearing 7008/P4 as an example, experimental verification was carried out with different waviness states in four processes: coarse grinding, fine grinding, ultra-precision and polishing. The error between the model calculation results and the actual test results does not exceed 5%, which had good accuracy and practicality. The results shown that the amplitude of waviness had the greatest influence on vibration, followed by the order of waviness, and the influence was more significant with the increase of rotational speed. Full article

The asphalt pavements on the Qinghai–Tibet Plateau suffer from severe performance degradation under the coupling effect of intense ultraviolet radiation and cyclic low temperature. Crumb rubber modified asphalt (CRMA) is regarded as a durable pavement material suitable for alpine high-altitude regions, yet the viscoelastic damage mechanism of CRMA under radiation–low-temperature coupled aging remains unclear. In this study, six groups of CRMA specimens with two rubber particle sizes (40 mesh and 80 mesh) and three dosages (10%, 15%, and 20%) were prepared. A laboratory accelerated aging device was adopted to simulate plateau coupled aging with ultraviolet irradiation durations of 0, 4, 6, and 12 months, while rolling thin-film oven aging (RTFO), a pressure aging vessel (PAV), and ozone aging were set as control groups. Dynamic shear rheometer (DSR) and bending beam rheometer (BBR) tests were conducted to characterize the high-temperature rutting resistance and low-temperature crack resistance of aged CRMA, and the Burgers viscoelastic constitutive model was employed to quantitatively fit the low-temperature creep curves and analyze the evolution law of constitutive parameters. The results show that prolonged ultraviolet radiation aggravates the hardening and embrittlement of CRMA synergistically: the complex modulus and rutting factor increase continuously, while the phase angle decreases, which improves high-temperature deformation resistance but deteriorates low-temperature performance; the creep stiffness modulus rises, and the creep rate declines, indicating a weakened stress relaxation capacity and elevated cracking risk at low temperatures. Thermal oxidation pre-aging and ozone aging further amplify the coupled aging damage. Increasing the crumb rubber dosage from 10% to 20% simultaneously optimizes the high- and low-temperature durability of aged CRMA. Under the same dosage, 40-mesh coarse rubber powder better enhances the anti-rutting performance at high temperatures, whereas 80-mesh fine rubber powder presents superior low-temperature crack resistance, which is hypothesized to originate from greater interfacial interaction with the asphalt matrix. The Burgers model achieves outstanding fitting accuracy for all creep curves with the coefficient of determination R2 > 0.994, and the four model parameters E1, E2, η1, and η2 can quantitatively reveal the evolution of instantaneous elasticity, delayed elasticity, and viscous flow behavior during aging. For pavement material design in plateau regions, 40-mesh CRMA with a 20% dosage is recommended for high-temperature rutting control, and 80-mesh CRMA with a 20% dosage is preferred to mitigate low-temperature thermal cracking. This work provides theoretical support for the performance evaluation and material optimization of long-life rubber asphalt pavements serving in extreme plateau environments. Full article

Port electrification under the green-port transition sharpens peaks and raises demand charges, motivating photovoltaic–battery energy storage (PV–BESS). Yet PV–BESS value depends on forecast error, operating policy and tariff peak memory, a ratchet carrying one peak-setting error across months; evaluations under perfect foresight or monthly-only billing leave their interaction unquantified. We quantify it in a controlled one-year Korean container-terminal simulation (synthetic load, public PV data, rolling dispatch). Three peak-memory structures and forecast-error levels are tested for four published policies; re-billing, an execution-layer ablation and 20 PV-forecast realizations separate mechanical from behavioral effects. At calibrated error, the ratchet raised mean point-forecast regret by 6–8 million KRW/yr (bootstrap interval excluding zero for one policy; no sign test significant), reduced scenario-policy regret by 3 million (nominally significant), multiplied annual-tariff dispersion two- to ninefold and made its sign event-dependent. In the reference realization, threshold-induced early depletion under monthly-only billing interacted with 12-month re-billing, raising point-forecast regret to 2.4–3.3 times the monthly-only value; with that response off, four of five policies matched the re-billing benchmark. Scenario information was worth 12.5 million KRW/yr under the ratchet, 2.7 under monthly-only billing. PV–BESS value thus depends jointly on forecast accuracy, tariff peak memory, policy information use and event-level dispatch. Full article

This paper studies the stationary balancing of a full-scale bicycle robot using a scissored-pair control moment gyroscope (CMG). The test robot weighs 60 kg and includes two counter-rotating flywheels, a 20:1 geared gimbal system, an inertial measurement unit (IMU), and an embedded controller. For controller design, a simplified nonlinear model of the roll and CMG dynamics is derived and linearized around the upright position. The main contribution of this work is the experimental testing of a PDD2-LQR controller. This controller keeps the standard LQR state-feedback baseline and adds feedback from the roll error, roll rate, and filtered roll acceleration. Instead of redesigning the whole controller, the same LQR gains are used in both the baseline LQR and PDD2-LQR setups. This allows us to directly evaluate the benefit of the extra PDD2 terms on the same hardware under the same actuator limits. Experiments were carried out using PID, LQR, and PDD2-LQR controllers. The PDD2-LQR controller reached an average RMSE of 0.660 degrees and an average MAE of 0.477 degrees. These values represent improvements of about 51.8% and 43.2%, respectively, compared with the standard LQR controller. Overall, the results show that adding roll-motion feedback to the baseline controller improves the stationary balancing performance under the tested conditions. Full article

Rolling electrical transmission devices for aerospace applications require high power capacity, long service life, and high reliability. As a critical rolling-contact component, the flexible ring undergoes repeated mechanical deformation and electrical loading, making its surface condition essential to fatigue endurance and contact stability. This study developed an optimized Cu/Ni/Au-Co multilayer electroplating strategy to mitigate surface degradation and unstable electrical contact. The coating comprised an optimized copper layer, a low-stress nickel sulfamate layer, and a gold–cobalt layer deposited from an electrolyte containing 0.6 g/L Co²⁺. The multilayer coating improved the surface and mechanical properties of flexible rings while maintaining stable electrical performance. Under current-carrying tension–compression loading, coated rings withstood over 100 million cycles, compared with approximately 30 million cycles for uncoated rings. Dynamic resistance tests using a rolling-joint verification device further confirmed stable electrical contact and good loop-to-loop consistency. These results demonstrate that the optimized multilayer coating effectively enhances fatigue endurance and dynamic contact stability, providing a practical surface-engineering solution for high-reliability aerospace rolling electrical transmission devices. Full article

Short-term air-quality forecasting is important for environmental monitoring and public-health planning, particularly in Indian cities affected by persistent air pollution. This study presents a next-day air-quality prediction framework for seven pollutants (PM_2.5, PM₁₀, NO₂, SO₂, CO, NO_x, and O₃) across 11 Indian cities over the period 2019–2023, using daily observations from the Central Pollution Control Board (CPCB) and meteorological data from NASA POWER. Next-day pollutant concentrations are predicted using historical pollutant measurements, lagged and rolling features, calendar variables, and meteorological covariates available at the feature date. The study documents Variational Quantum Circuit (VQC) configurations implemented using PennyLane and Qiskit, alongside a candidate quantum-feature-augmentation approach for XGBoost. Quantitative evaluation is restricted to the XGBoost model, using mean absolute error (MAE), root mean squared error (RMSE), and the coefficient of determination $R^2$ over the common chronological final-test target window spanning 28 June 2022 to 31 March 2023. For the evaluated XGBoost predictions, the reported pollutant-specific $R^2$ values range from 0.613 to 0.864. The quantum-circuit configurations and quantum-feature-augmentation approach are presented as candidate methods whose comparative predictive performance remains to be established through evaluation using aligned final-test predictions and observed targets. Full article

Empirical standoff and look-ahead selection motivates this closed-form law for passive dual-UAV bearing-only localization in a wall-type leader–follower formation. An off-axis-dependent bearing-noise model with a nonzero floor yields the Fisher information matrix, Cramér–Rao lower bound ellipse area, and A-optimal information trace. Under sufficient conditions for a symmetric configuration, both bounds increase strictly with standoff, so the field-of-view (FOV) lower bound sets the optimum rather than an interior stationary point. The FOV region also yields closed-form baseline and look-ahead limits. The law is $R_p^{\star }\approx \mathrm{m}\mathrm{i}\mathrm{n}(R_{\mathrm{m}\mathrm{a}\mathrm{x}},{\kappa }_s{d}_{\mathrm{F}\mathrm{O}\mathrm{V}})$, where $d_{\mathrm{F}\mathrm{O}\mathrm{V}}=l/(2\mathrm{t}\mathrm{a}\mathrm{n}\phi )$ and ${\kappa }_s=1.5–2$ is recommended for the investigated straight-cruise domain. Twenty-one 30-run straight-cruise Monte Carlo grids gave mean ${\kappa }_s=1.61$ and unchanged values over tested noise and speed ranges; independent baseline and field-of-view combinations later show that ${\kappa }_s$ is a domain-specific empirical coefficient rather than a universal constant. Using separate selection and test seeds, the frozen direct law reduced hard-turn overall mean position error (MPE) by 17.7% and 52.2% relative to dwell approximation and closed-loop rolling, respectively. Other scenarios precluded cross-scenario maneuver guarantees. The law provides preflight nominal placement; maneuvering requires dynamic feasibility, dropout, and estimator consistency checks before online adjustment. Full article

This study asks whether physical climate-risk transmission across insurance, reinsurance, climate-vulnerable sovereign bond, real-estate, agricultural, water, and adaptation markets is detectable in daily data. A news-based physical-risk index and seven publicly documented market series, observed from January 2020 to June 2026, are analysed with quantile vector autoregression connectedness. Inference uses bias-corrected bootstrap intervals, three placebo nulls, and a Monte Carlo power analysis. The markets are strongly connected (total connectedness 47.0%, 45.1%, and 47.2% at the 5%, 50%, and 95% quantiles), but this connectedness belongs to the equity block and two-fifths of it is a common market factor. Tail amplification is small and disappears once the March 2020 crash is excluded. Reinsurers are net receivers and insurers net transmitters at every quantile. The physical-risk index contributes no more to the markets, or to sovereign bonds in the lower tail, than a placebo index, and rolling estimates show no upward trend. A power analysis shows that the connectedness tests detect only large, linear transmission; a complementary co-exceedance test has 86% power against tail-only transmission above about 2% of receivers’ variance (smaller effects cannot be excluded). Weekly, monthly, and disaster-loss specifications agree. An uncentred innovation covariance mechanically inflates tail connectedness to about 81%. Full article

The rust layer on carbon steel controls how the metal beneath it corrodes, but its state is usually assessed by destructive analysis. This work followed uncoated cold-rolled carbon steel through 30 cycles (240 h) of a modified ISO 14993 cyclic corrosion test in which ultrapure water replaced the NaCl spray. Two specimens were retrieved at each of nine intervals. Surface color and rust coverage were measured from photographs and compared with SEM/EDS, XRD, linear polarization resistance and confocal topography. Three stages were resolved. Rust coverage stayed below 6% up to cycle 5. Between cycles 10 and 15, coverage rose to about 50%, γ-FeOOH appeared and polarization resistance fell from 441.1 to 301.3 Ω∙cm². Coverage then leveled off near 63% while the instantaneous corrosion rate kept rising. Among the color coordinates, b* correlated most strongly with corrosion rate (Spearman ρ = 0.867, exact p = 0.005, 95% CI 0.46–0.97). These correlations rest on nine intervals under one condition, and they partly reflect exposure time. No predictive model was built or validated. Within these limits, surface color serves as a non-destructive indicator of rust-layer stage. Full article

This study examines whether the directional organization of predictive relationships within a multi-asset portfolio contains information about subsequent downside risk beyond conventional diversification diagnostics. Using daily returns for 15 liquid U.S.-listed exchange-traded funds from 2014 to 2025, we construct rolling directed predictive networks and use a Hodge decomposition to obtain a global transmitter–receiver hierarchy. Its concentration is summarized by the normalized range of the gradient potential, $C_{\varphi }$. Under the reference 126-trading-day first-order vector autoregressive specification, high-$C_{\varphi }$ states are followed by maximum drawdown losses approximately 1.65 percentage points larger over the subsequent 63 trading days than matched low-$C_{\varphi }$ states with similar volatility, average correlation, leading correlation-eigenvalue concentration, and diversification ratio. The association is stable across tested network densities but sensitive to matching tolerance, temporal specification, downside horizon, and market regime. A simple net-flow concentration measure reproduces a similarly strong association, indicating that directional imbalance is an important part of the underlying phenomenon, while residualized tests indicate that the Hodge hierarchy retains information not captured by a linear projection on that benchmark. The result is reproduced under an alternative Granger-type edge weighting but not under generalized forecast-error variance decomposition. Overall, directional structural concentration is best interpreted as a specification-dependent portfolio risk diagnostic for monitoring, stress testing, and discretionary risk budgeting rather than as a universal early-warning or trading signal. Full article

Forecasting food costs is an important tool for food security planning and management. This study aimed to evaluate and compare the performance of statistical and machine learning models for forecasting the monthly cost of the Basic Family Food Basket (BFFB) in Panama and San Miguelito. Monthly data from 2009 to 2024 were chronologically divided into training and test sets. A Naïve model was used as a benchmark, while ARIMA, Holt, Random Forest and XGBoost models were evaluated using MAE, RMSE and MAPE. The main evaluation used a fixed-origin multi-step forecasting scheme, complemented by a rolling-origin evaluation. Predictive accuracy was also assessed using the Diebold–Mariano test with Holm correction for multiple comparisons. Random Forest yielded the lowest test-set errors (MAE = 13.82, RMSE = 17.23, MAPE = 3.92%), followed by XGBoost (MAE = 15.84, RMSE = 18.14, MAPE = 4.53%). The rolling-origin evaluation also showed favorable performance for both machine learning models, while the Diebold–Mariano test identified statistically significant differences in four of the ten pairwise comparisons based on the Holm p-values, all involving XGBoost. These results highlight the potential of machine learning models for BFFB cost forecasting and support their usefulness as tools for food security-related monitoring and planning. Full article

Surface defects on hot-rolled steel strips are often low in contrast, resemble the background, and co-occur. This paper presents DFE-Net, a refinement of YOLO11n for steel surface defect detection. A defect-aware dual-stream gated cross-modulation module (DA-GCM), derived from the FCM block, lets a semantic stream and an appearance stream modulate each other through a dual-pooling channel attention and a multiscale strip spatial attention and combines them with a pixel-wise gate. A spatial-frequency detail-compensation pyramid (SFDP) injects space-to-depth P₂ features into the P3 fusion node, refines them with an FFT-based large-kernel block, and aggregates the inputs of every node with an adopted Global Modulated Fusion (GMF) module. A shared-stem parameter-efficient detection head (SSP-Head) shares a grouped-convolution stem between regression and classification. On NEU-DET, over five paired seeds, DFE-Net reaches 74.27 ± 0.81% mAP@0.5 and 41.83 ± 0.55% mAP@0.5:0.95 with 2.24 M parameters, 0.91 and 0.58 points above YOLO11n; the 95% confidence intervals include zero, although mAP@0.5:0.95 is higher in all five seeds and in all five folds of a cross-validation, a consistent direction that tests corrected for the overlap of the cross-validation training sets do not confirm. DFE-Net is significantly more accurate than YOLOv8n and YOLO26n but on par with YOLOv9t. It has 13.2% fewer parameters than YOLO11n, but its computation rises from 6.3 to 7.4 GFLOPs and its batch-1 latency from 5.63 to 14.31 ms on a desktop GPU and from 10.15 to 28.73 ms on a Jetson Orin Nano. DFE-Net is thus a compact detector with a modest accuracy gain; its robustness and suitability for online inspection require further validation. Full article

Background: NPM1 mutations are established biomarkers for measurable residual disease (MRD) monitoring in acute myeloid leukemia (AML). RT-qPCR is widely used for NPM1 MRD assessment, but sensitive DNA-based methods offer advantages in sample stability and workflow. superRCA is a novel mutation detection technology that combines genotyping with rolling circle amplification to convert target molecules into fluorescent particles that can be enumerated by flow cytometry. Here, we evaluated the performance of superRCA for detection of the most common NPM1 variants, MutA, MutB, and MutD. Methods: Analytical performance was assessed by determining limit of blank (LoB), limit of detection (LoD), limit of quantification (LoQ), precision, linearity, and cross-reactivity. Method comparison was performed using 83 biobanked clinical samples from patients with NPM1 MutA-positive AML previously analyzed by RT-qPCR, qPCR, ddPCR, and deep sequencing. Correlation, agreement, and positive and negative percent agreement (PPA/NPA) versus RT-qPCR were evaluated. Results: The superRCA assay demonstrated LoD below 0.005% variant allele frequency (VAF) for all three NPM1 variants. Linearity was tested and demonstrated over 0.01–10.24% VAF. superRCA showed strong correlation with the other methods. Using LoD as the positivity threshold, PPA and NPA versus RT-qPCR detectable signal were 64% and 100%, respectively; using LoB, PPA was 89% and NPA 92%. Several superRCA-positive samples were negative by lower-input DNA methods. Conclusions: superRCA introduces ultra-sensitive molecular testing by combining sequence-specific DNA genotyping and flow cytometric enumeration. The NPM1 superRCA assay demonstrated low background, high sensitivity, precision, and broad linearity, with strong correlation with established molecular methods. These findings support further evaluation of superRCA for NPM1 MRD monitoring in AML. Full article

Reliable short-term ozone forecasting requires not only low average prediction error but also evidence that apparent model leadership remains robust across forecast horizons, monitoring locations, temporal-validation designs, statistical assumptions, and high-ozone conditions. This study evaluates the robustness of machine-learning model rankings for hourly ozone forecasting using observations from three geographically separated United States Environmental Protection Agency (EPA) Air Quality System (AQS) monitoring stations: Salt Lake County, Utah; Dorchester County, Maryland; and Hamilton County, Ohio. Thirteen forecasting approaches were evaluated independently at 1-, 6-, 12-, and 24-h horizons using a common 58-predictor autoregressive-temporal feature set, leakage-controlled chronological hold-out testing, expanding- and rolling-window temporal cross-validation, dependence-aware statistical inference, post-hoc explainability and feature-family ablation, and high-ozone threshold-exceedance verification. Minimum hold-out root mean squared error (RMSE) values ranged from 3.035–4.689 parts per billion (ppb) at 1 h, 7.359–8.952 ppb at 6 h, 8.421–9.946 ppb at 12 h, and 8.603–10.140 ppb at 24 h. Across the 12 station–horizon tasks, Extreme Gradient Boosting (XGBoost) achieved four numerical RMSE minima, HistGradientBoosting and ExtraTrees three each, and Gradient Boosting two. Friedman tests identified overall model differences at every station and horizon ($p<0.001$), whereas Holm-adjusted pairwise comparisons showed substantially weaker separation among the leading ensemble approaches. Sensitivity analyses using Newey–West heteroskedasticity-and-autocorrelation-consistent (HAC) lags of 24, 48, and 72 h did not materially alter the qualitative interpretation, although these analyses do not establish independence beyond the evaluated lag windows. High-ozone performance was evaluated using station-specific upper-tail thresholds and a 70 ppb hourly analytical threshold, with uncertainty quantified using 1000 calendar-day block-bootstrap replicates and explicit reporting of valid bootstrap replicates. At the 24-h horizon, the numerical minimum-RMSE model at all three stations had a probability of detection of zero for observed concentrations at or above 70 ppb. Retraining-based ablation showed that joint removal of calendar and cyclical temporal representations produced the largest and most consistent degradation, increasing RMSE by approximately 8.01% on average across the 12 tasks. The contribution is therefore not the rediscovery of site- or horizon-dependent ozone-forecasting performance, but a leakage-controlled evaluation of whether numerical model leadership remains robust under alternative temporal validation, dependence-aware inference, multiplicity correction, and high-ozone verification. Full article