Winter inversions and summer smoke: A season-dependent approach to PM₂.₅ modeling with low-cost sensors in complex terrain.

Bayesian Mendelian randomization methods for index trait bias correction in subsequent trait genome-wide association studies.

Accurate long-term tidal flat elevation mapping is essential for understanding coastal morphodynamics, assessing hazard resilience, and supporting coastal management. However, previous approaches typically rely on separately modeling for each temporal snapshot, leading to inconsistent results over time. To address this limitation, this study proposes an efficient long-term tidal flat elevation inversion method based on inter-annual statistically homogeneous inundation frequency. A phase–tide–based weighted inversion algorithm is developed to correct sampling-induced bias and generate statistically homogeneous annual inundation frequency (AIF) maps, thereby stabilizing the AIF–elevation relationship and enabling a temporally-transferable inversion model for long-term reconstruction within a given region. To further ensure efficiency, a robust rule-based land–water segmentation strategy is integrated. The method was validated across five representative coastal regions in China (Yellow River Delta, Subei Bank, Yangtze River Estuary, Sansha Bay, and Lianzhou Bay), yielding integrated RMSE values of 0.20 m to 0.63 m. Notably, high accuracy was maintained across years using a unified inversion framework without year-specific recalibration, proving the method's strong temporal robustness and computational efficiency. By transforming tidal flat inversion from discrete year-by-year calibration toward statistically stabilized and transferable modeling, the proposed method provides an operationally feasible solution for long-term coastal topographic monitoring.

A physically guided and interpretable SWAT-BiLSTM framework with Bayesian optimization for bias correction in daily streamflow forecasting.

Usefulness of routine clinical follow-up after oral cavity cancer treatment: a single-center retrospective study.

Bias correction of sea temperature in Shandong coastal waters using LSTM-FVCOM and GRU-DF-FVCOM hybrid models

A weather typing adaptive bias correction method for FY-4B satellite-driven solar radiation nowcasting

Abstract. Accurate global monitoring of atmospheric methane (CH4) is essential for tracking progress toward climate mitigation targets such as the Global Methane Pledge (GMP). Ground-based measurement networks are too sparse to provide sufficient spatial coverage, while satellite-derived retrievals are hindered by systematic biases and uncertainties, limiting their reliability for consistent global monitoring. We present the first global fusion of GOSAT, GOSAT-2, and TROPOMI to generate a globally consistent daily 0.1° land dataset for 2020–2023 for enhanced global column-averaged dry-air mole fraction of atmospheric methane (XCH4) mapping. The framework employs a three-step machine-learning (ML) approach: (1) sensor-specific bias correction using TCCON observations, (2) cross-sensor harmonization to GOSAT-2, the sensor with the strongest post-correction TCCON agreement, and (3) priority-based fusion. Tree-based ensemble regressors were trained with satellite retrieval parameters to reduce systematic biases and inter-sensor discrepancies. Independent validation at three withheld TCCON stations demonstrates robust generalization of the Fused product (R2 = 0.81, RMSE = 10.78 ppb), outperforming standard and operational bias-corrected satellite products and previously reported ML-based approaches. Regional assessments show that fusion substantially improves data availability and reduces systematic errors, delivering up to 9.5 % relative coverage gains compared to TROPOMI operational products in challenging regions (South Asia, Amazon Basin, Eastern Siberia). The Fused dataset reveals intensifying positive XCH4 anomalies (+60 ppb) over South Asia, East Asia, and Central Africa during 2020–2023, linked to MODIS-derived agricultural and urban land classes as well as known oil and gas fields. The dataset provides a scalable resource for regional CH4 emissions assessment and continuous monitoring, with the framework extendable to upcoming satellite missions (GOSAT-GW, CO2M) for long-term GMP progress tracking.

We develop a robust bias-corrected method of inference about the ratio of age-standardized rates (RASR) for comparing the age-standardized rate (ASR) between a subpopulation and the whole population. Unlike previous methods, the proposed approach does not rely on the proportional age-distribution (PAD) assumption, which is often unrealistic in many situations. Like an existing approach, the method corrects for bias resulting from sampling errors when using sample-based population estimates, instead of census-based populations, as the denominators for estimating ASRs. This broadens the applicability of the proposed method in studying cancer risk factors beyond the basic demographic characteristics. The robust bias-corrected estimator of the RASR and the associated variance estimator and confidence intervals are derived. We show empirically that the proposed RASR estimator performs significantly better than the existing estimator, which relies on the PAD assumption, especially when the latter assumption fails. Specifically, the proposed RASR estimator significantly reduces the bias without increasing the variance. On the other hand, when the PAD assumption holds, our RASR estimator performs similarly to the existing estimator. The proposed method has also shown highly desirable performance when at-risk population estimates used for calculating ASRs are subject to sampling errors. We also show empirically that the proposed variance estimator performs satisfactorily. A real-data application is discussed.

Hydroclimatic assessment in Xinjiang is constrained by sparse observations, complex terrain and systematic biases in widely used gridded and reanalysis products. Here we present the Three-Dimensional Variational Xinjiang Meteorological Forcing Dataset (3DVAR-MF-XJ), a 0.1° gridded near-surface dataset for 1961-2020 at hourly and daily resolution, including 2 m air temperature, relative humidity, 10 m wind speed, surface pressure, downward shortwave radiation and precipitation. 3DVAR-MF-XJ is generated using a hybrid framework in which regional modelling with three-dimensional variational data assimilation first produces dynamically consistent fields (3DVAR-XJ), followed by station-constrained bias correction on a common 0.1° grid. Evaluation against 30 withheld stations and independent benchmark products shows reduced mean and root-mean-square errors, higher correlations, and improved representation of precipitation over complex terrain and shortwave radiation at high elevations relative to ERA5, ERA5-Land and CN05.1. For precipitation, RMSE is reduced by 65.6%, 64.9% and 49.3% relative to ERA5, ERA5-Land and CN05.1, respectively. 3DVAR-MF-XJ provides a unified, well-documented forcing dataset for hydrological, ecological and land-atmosphere applications in arid Xinjiang.

Introduction Monocular RGB-based human pose estimation is increasingly applied in field-based movement assessments; however, systematic projection-related errors inherent to the simplified camera geometry remain insufficiently characterized relative to laboratory-based biomechanical standards. This study quantified systematic projection bias in a browser-based markerless motion capture (MMC) system and validated a geometry-informed linear correction framework for sagittal-plane squat analysis under standardized monocular acquisition conditions. Methods Bodyweight squats performed by 30 healthy adult males were simultaneously recorded using a three-dimensional marker-based optical motion capture (OMC) system and a two-dimensional webcam positioned at a fixed distance and orientation. Results Raw joint angles obtained from the MMC system revealed significant systematic underestimation (hip: −11.2°, knee: −10.6°), consistent with perspective-induced projection effects predicted by the pinhole camera model and differences in the joint center definitions. To address this projection-consistent bias, a geometry-constrained linear correction model was developed and validated using leave-one-subject-out cross-validation. The correction effectively neutralized the systematic bias (hip: 0°, p = 0.323; knee: 0°, p = 0.645) and substantially reduced root mean square error (hip: from 11.6° ± 4.2° to 4.0° ± 2.3°; knee: from 10.9° ± 3.3° to 3.9° ± 1.5°). Furthermore, mean coefficients of determination were significantly improved and stabilized (hip: from 0.31 ± 0.94 to 0.90 ± 0.17; knee: from 0.67 ± 0.30 to 0.96 ± 0.04). Discussion Importantly, high accuracy was achieved without incorporating participant-specific anthropometric variables, suggesting that projection-consistent geometric factors predominated under controlled camera conditions. These findings demonstrate that systematic errors in monocular pose estimation can be substantially mitigated when the acquisition distance and orientation are standardized. Our results suggest that under such predefined recording constraints, corrected joint angles provide practically relevant estimates for strength assessment and rehabilitation monitoring.

Screening trials require large sample sizes and long time-horizons to demonstrate mortality reductions. We recently proposed increasing statistical power by testing stored control-arm specimens, called the "Intended Effect" (IE) design. The IE design increases power by increasing the effect size via restricting analysis to only those people in both arms who are affected by screening, namely, those who test-positive. Hence the design requires storing control-arm specimens for future testing. To evaluate feasibility of the IE design, the US National Cancer Institute (NCI) is collecting blood specimens in the control-arm of the NCI Vanguard Multicancer Detection pilot feasibility trial. However, key assumptions of the IE design require more investigation and relaxation. We relax the IE design to (1) reduce costs by testing only a stratified sample of control-arm specimens by incorporating inverse-probability sampling weights, (2) correct for potential loss-of-signal in stored control-arm specimens, and (3) correct for non-compliance with control-arm specimen collections. We also examine sensitivity to "unintended effects" of screening (i.e. outcome rates in screen-negatives increasing due to false reassurance). In simulations, testing all primary-outcome control-arm specimens and a 50% sample of the rest maintains nearly all the power of the IE while only testing half the control-arm specimens. Power usually remains increased from the IE analysis (versus the standard analysis) even if unintended effects exist. The IE design is robust to some loss-of-signal scenarios, but otherwise requires retest-positive fractions that correct bias at a small loss of power. The IE can be biased and lose power under control-arm non-compliance scenarios, but corrections correct bias and can increase power. The IE design can be made more cost-efficient and robust to loss-of-signal. Unintended effects will not typically reduce the power gain over the standard trial design. Non-compliance with control-arm specimen collections can cause bias and loss of power that can be mitigated by corrections. Although promising, practical experience with the IE design in screening trials is necessary.

Egypt possesses substantial potential for renewable energy generation, prompting heavy national investments to increase the share of wind power in its overall energy portfolio. Consequently, it is crucial to evaluate the long-term vulnerability of future wind energy production to climate change. This study fills a critical gap in regional climate-energy modelling by providing a novel quantification of turbine-specific capacity ratios across four Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5). Through a comparative assessment of 23 CMIP6 Global Climate Models (GCMs), EC-Earth3-Veg, EC-Earth3, and CESM2-WACCM were identified as the most reliable models against historical ERA5-Land data using the Kling-Gupta Efficiency (KGE) metric, followed by Quantile Mapping for bias correction of both historical and future scenarios. Evaluating nine wind turbine models (T1-T9) revealed that T1 and T2 maintained the highest historical capacity ratios, peaking at 68.0-76.5% and 59.5-68.0%, respectively. By 2100, meteorological projections indicate a regional warming trend coupled with a decrease in mean wind speed; notably, the high-emission SSP5-8.5 scenario projects the highest mean temperature (28°C) and lowest mean wind speed (3.8m/s). Despite these declines, future projections for T1 and T2 indicate resilient power generation and localized increases in strategic locations, such as Ras Ghareb and southern Egypt, particularly under the SSP2-4.5 scenario. Ultimately, these findings provide essential data-driven insights for energy planners to optimize turbine selection and site development, ensuring the long-term resilience of Egypt's wind energy infrastructure.

Accurate assessment of fertilizer nitrogen (N) fate is essential for optimizing rice production, yet regional-scale estimates remain limited. This progress has been fundamentally constrained by the prohibitive cost of isotopic tracing, which has not only limited large-scale deployment but also prevented the correction of apparent priming effect (APE)-induced bias, resulting in a systematic misestimation of soil N retention. To overcome this limitation, a continental-scale framework is developed to quantify fertilizer N fate across Asian rice systems. Ensemble modeling produces the first high-resolution (5 arcmin) maps of Net Residue and Loss, identifying Eastern andCentral China,along with Northern India, as critical Loss hotspots. Accounting for APE reveals that positive priming suppresses the Net Residue of applied N to below 7% (-1%-15%), while 48% (43%-53%) of applied N is lost to the environment, leading to annual environmental costs ofUS$98.53 (83.25-108.27) billionfrom reactive-N emissions. Crucially, three N-risk archetypes that together encompass 42% of global rice fields emerge: 37% high-loss/high-net-residue, 2% high-loss-soil-depleting, and 3% low-loss-soil-mining. Overall, this framework converges high-resolution ensemble mapping, apparent priming bias correction, and policy-oriented N-risk archetypes to transform N governance from retrospective accounting into a spatially targeted, forward-looking strategy reconciling food security with environmental and economic sustainability.

Achieving ultra-stable optical frequency dissemination over long-haul fiber networks is essential for numerous applications. Although optical frequency transfer systems based on optical phase-locked loops (OPLLs) have achieved unprecedented stability levels, their performance is limited by continuous compensation bias caused by noise asymmetry from bidirectional frequency shifts and loss of lock in long-distance, high-noise links. Here, we propose a bias-free noise compensation method based on digital radio-frequency phase recording using a time-to-digital converter, which eliminates residual errors and offers a theoretically unlimited dynamic range for enhanced reliability. By incorporating multifunctional relay stations and hertz-level optical bandpass filtering to improve OPLL robustness, our scalable architecture achieves a frequency instability of 2.9times {10}^{-21} at 1 day over a 2067 km field fiber link under extreme noise conditions (5000 {mathrm{rad}}^{2},{mathrm{Hz}}^{-1},{mathrm{km}}^{-1} at 1 Hz). Bias correction improves the instability by threefold and breaks through the theoretical limit of uncalibrated systems. The setup maintains continuous phase lock for over four days, and noise purification enables virtually unlimited link extension. This advance establishes a robust, field-deployable optical frequency network compatible with standard telecommunication infrastructure.

Heatwaves (HWs) are among the most impactful climate extremes affecting tropical urban environments, yet local‑scale assessments of their future characteristics remain scarce in the Amazon. Here, we quantify projected changes in key HW indicators for the Belém urban core, eastern Amazon, using a station‑referenced framework based on a carefully evaluated and bias‑corrected ensemble of CMIP6 global models. HWs are identified from daily maximum air temperature (Tmax) using a smoothed day‑of‑year 95th percentile threshold calculated over the 1994-2023 baseline period. Changes in annual frequency, event duration, and thermal intensity are assessed for near‑future (2025-2049) and far‑future (2050-2074) periods under four Shared Socioeconomic Pathways (SSP1‑2.6, SSP2‑4.5, SSP3‑7.0, and SSP5‑8.5). A multi‑metric performance evaluation is first applied to select the best‑performing CMIP6 models for Tmax over Belém, followed by bias correction using Quantile Delta Mapping calibrated against local observations. Results show a clear, scenario‑dependent intensification of HW hazards. In the current climate, 126 HW events were detected, with pronounced interannual variability and a marked increase during the last decade. Near‑future changes are modest and variable across scenarios. In contrast, far‑future projections under the high‑emission SSP5‑8.5 pathway reveal a substantial transformation of the HW regime: annual frequency more than doubles relative to the baseline, and the 99th percentile of event duration increases from approximately 19 to 65 days (Δ = +46 days), with a 14.5% probability that any given HW will exceed the current extreme threshold. Peak Tmax during HWs increases from 37.0°C to 37.55°C (Δ = +0.55°C), thus indicating an upper-tail amplification. Under the strong mitigation pathway SSP1‑2.6, changes in HW duration and intensity remain statistically indistinguishable from present‑day conditions. While this study does not assess impacts or adaptation capacity, the results provide a hazard‑based, scenario‑dependent characterization of future heatwave behavior in an Amazonian urban context, offering a quantitative foundation for subsequent assessments of climate risk in tropical cities.

In medical diagnostic studies, the area under the receiver operating characteristic curve (AUC) is a widely used metric that captures a continuous test's overall ability to discriminate between diseased and non-diseased individuals across all possible cutoffs. However, in practice, disease status is sometimes only partially verified, introducing verification bias that undermines the validity of AUC estimation. While numerous methods address bias correction for AUC estimation, approaches that directly construct confidence intervals for the AUC remain limited. This paper proposes two robust methods for constructing bias-corrected confidence intervals for the AUC under the missing-at-random assumption: one based on bootstrap resampling and the other on empirical likelihood. Both approaches accommodate missing disease verification by leveraging the bias-corrected ROC estimators introduced by Alonzo and Pepe. Extensive simulation studies and real-world data analyses demonstrate that our proposed methods yield valid and precise interval estimates for the AUC under various clinically relevant settings.

Skin colour is known to confound optical devices, adversely impacting care for patients with darker skin. Photoacoustic imaging (PAI) combines optics and ultrasound for deep tissue imaging, creating a complex relationship between PAI-derived biomarkers and skin melanin concentration, yet no generalisable bias correction has been demonstrated. Drawing on a healthy volunteer cohort of 42 participants spanning Fitzpatrick types I-VI and vitiligo - the most diverse ever assembled in PAI - we characterise optical and acoustic mechanisms driving skin colour-dependent degradation in image quality and biomarker quantification. Wavelength-dependent melanin absorption causes spectral colouring, dominating at low melanin levels, while epidermal ultrasound backscattering dominates at high melanin levels, producing a non-linear relationship between unmixed sO₂ and skin tone. Leveraging this understanding, we propose a practical spectral colouring correction and adapt a plane-wave reconstruction algorithm to resolve backscattered ultrasound artefacts. Our findings underscore the need for advanced reconstruction methods to enable equitable clinical PAI.

Organized breast cancer screening programmes are a cornerstone of cancer control policy across Europe. Population-based cancer registries (PBCRs) play a central role in monitoring screening performance, outcomes, and programme quality. However, the extent and methodological approaches of registry-based evaluations across Europe have not been comprehensively synthesized. Following PRISMA-ScR guidelines, we conducted a scoping review of peer-reviewed studies evaluating organized breast cancer screening programmes in Europe using PBCR data. Studies were identified through systematic database searches and screened using predefined eligibility criteria. Data were extracted on study design, definitions of detection mode, outcome measures, and approaches to bias adjustment. The Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework guided the methodological analysis. Twenty-six studies were included, mainly from Western and Northern Europe, with most published before 2010. Screen-detected cancers consistently showed lower mortality, better survival, and more favourable tumour characteristics than interval or non-screened cancers. However, reporting of screening indicators was inconsistent. Definitions of detection modes, particularly interval cancers, and approaches to bias adjustment varied widely, limiting comparability. Few studies applied comprehensive bias correction or clearly reported registry–screening data linkage. Registry-based evaluations provide valuable evidence on the impact of breast cancer screening programmes, However, the evidence is limited by methodological heterogeneity and limited analytical standardization, reducing its comparability and policy relevance. Strengthening standardized definitions, improving transparency in analytical approaches, and better integrating programme monitoring with research could enhance the public health value of registry-based screening evaluation.

Accuracy, repeatability, and interchangeability of smartphone-based digital anthropometry using multi- and dual-image approaches for body fat estimation.