Accurate measurement of proptosis is crucial for diagnosing and managing thyroid eye disease (TED). While the Hertel exophthalmometer remains widely used, it demonstrates substantial interobserver variability. This prospective diagnostic accuracy study aimed to validate a novel automated ocular proptosis device against computed tomography (CT) as the reference standard. We enrolled 102 patients (204 eyes) with TED who underwent proptosis measurement using 2 methods: orbital CT, an automated device, senior ophthalmologists (>5y of experience), and junior ophthalmologists (1-3y of experience). Primary outcomes included intraclass correlation coefficient (ICC), Bland-Altman analysis with 95% limits of agreement (LOA), mean absolute error (MAE), and root mean square error (RMSE). The automated device demonstrated excellent agreement with CT [intraclass correlation coefficient (ICC) = 0.985, 95% CI: 0.980-0.989], superior to both senior (ICC = 0.934) and junior (ICC = 0.912) physicians. Mean bias (device versus CT) was minimal at -0.08mm with 95% limits of agreement (LOA) of -1.65 to +1.49mm. In comparison, senior physicians showed significant positive bias (+0.72mm), while junior physicians exhibited wider LOA (-3.15 to +2.53mm). The device achieved the lowest MAE (0.61mm) and RMSE (0.78mm), indicating superior accuracy. Interobserver reliability was significantly higher for the automated device (ICC = 0.992) than for manual measurements. These findings demonstrate that the automated ocular proptosis device provides highly accurate and reproducible measurements, outperforming manual assessments by ophthalmologists across experience levels. Its excellent agreement with CT supports its potential as a new standard for objective proptosis assessment in TED clinical practice and research.

Understanding the evolution of the atmospheric boundary layer height (BLH) is essential for characterizing air–surface exchange and air pollution processes. This study investigates the consistency and applicability of three BLH retrieval methods based on multi-source remote sensing observations at Beijing Southern Suburb station during autumn–winter 2023. Using Doppler wind lidar (DWL) and microwave radiometer (MWR) data, the Haar wavelet covariance transform (HWCT), vertical velocity variance (Var), and parcel methods were applied, and 10 min averages were used to suppress short-term fluctuations. Statistical analysis shows good overall consistency among the methods, with the strongest correlation between HWCT and Var method (R = 0.62) and average systematic positive bias of 0.4–0.6 km for the parcel method. Case studies under clear-sky, cloudy, and hazy conditions reveal distinct responses: HWCT effectively captures aerosol gradients but fails under cloud contamination, the Var method reflects turbulent dynamics and requires adaptive thresholds, and the Parcel method robustly describes thermodynamic evolution. The results demonstrate that the three methods are complementary in capturing the material, dynamic, and thermodynamic characteristics of the boundary layer, providing a comprehensive framework for evaluating BLH variability and improving multi-sensor retrievals under diverse meteorological conditions.

Consciousness, subjectivity, and aging in western thought.

Despite their importance in the climate system, remote ocean regions and their ability to absorb anthropogenic carbon dioxide (CO2) remain highly uncertain. To address this issue, citizen science initiatives, including sailboats, expand the observational network. Using observing system simulations and novel sailboat tracks, we demonstrate how integrating sailboat data improves estimates of ocean carbon uptake. While we underestimate the ocean carbon sink when mimicking real-world sampling, adding available sailboat data does not substantially improve reconstructions. Nevertheless, increased sampling reveals a stronger carbon sink, particularly between 40°S and 60°S. The improvement persists with hypothetical measurement uncertainties, but substantial differences arise depending on whether positive or negative biases are applied to the race track data. While we show that two additional circumnavigations already improve the ocean mean sink estimate, we further highlight the finding that the additional data remain insufficient to correct the overestimated CO2 sink trend, calling for continuation of the ongoing data collection.

ABSTRACT Contemporary tourism scholars increasingly embrace artificial intelligent technologies, such as machine learning algorithms, for sentiment analysis. This paper presents a counterargument by introducing the Tourist Sentiment Evaluation (TSE) model through manual computing algorithms. By a comparative experiment involving six machine learning models and the TSE model, this paper demonstrates that the TSE can outperform the selected machine learning approaches. The test relies on a mixed dataset, comprising 244,974 online reviews and multi-year questionnaire surveys from eight tourism destinations in China. This paper concludes that the manual computing approach retains distinctive advantages over AI approaches, due to the accuracy, explanatory and recursive applicability. Secondly, online score ratings are unreliable because they do not match actual reviews. Thirdly, this paper confirms the existence of social positive bias in the tourism context, known as the Pollyanna effect, where tourists exhibit a propensity to use positive words six times more frequently than negative words. This paper provides a counterargument to the prevailing tendency to adopt AI technologies in various domains, affirming the solid reliability of manual computation. Additionally, the utilisation of the TSE model holds significant potential for overcoming linguistic barriers by converting vast amounts of Chinese texts into quantified sentiment scores.

Accessibility model of overgeneral autobiographical memory.

Tunable positive and negative exchange bias in TmCrO3 thin films grown for heat-assisted magnetic recording

Neurofilament light chain - matrix comparison and long-term stability in frozen serum.

Abstract Metal oxide thin-film transistors (TFTs) have garnered considerable attention as promising alternatives to liquid-crystal displays and organic light-emitting diode displays. Among different metal oxide semiconductors, zinc tin oxide (ZnSnO, ZTO) stands out as a promising option owing to its environmental friendliness, affordability, and suitability for low-temperature fabrication. Nevertheless, pristine ZTO TFTs exhibit considerable drawbacks in both performance and reliability, particularly when exposed to negative bias illumination (NBIS) and thermal stress, leading to pronounced threshold voltage variations. Here, we report an in-situ sulfur anion doping strategy to modulate the defect landscape of ZTO, yielding high-performance and highly stable S-doped ZTO (S-ZTO) TFTs. The incorporation of S anion at oxygen lattice sites effectively suppresses oxygen-vacancy–related trap states, leading to marked improvements in both carrier transport and operational stability. The optimized S-ZTO TFT (10 at% S) exhibits a field-effect mobility of 12.7 cm 2 (V·s -1 , a threshold voltage of 3.8 V, a subthreshold swing of 0.31 V dec −1 , and an ultralow leakage current of 7 × 10 −12 A. More importantly, in-situ sulfur incorporation significantly mitigates threshold voltage shifts under prolonged positive bias, NBIS, and thermal stress, outperforming pristine ZTO devices. This work demonstrates that in-situ sulfur doping offers a practical route to high-performance ZTO-based oxide TFTs that meet the reliability requirements of AMOLED and AM-LCD backplanes.

This study assesses the suitability of UAV aerial imagery-based photogrammetry for reconstructing underwater riverbed topography and its application in two-dimensional (2D) hydrodynamic modelling, with a particular focus on comparing RGB, multispectral, and fused RGB–multispectral imagery. Four Lithuanian rivers—Verknė, Šušvė, Jūra, and Mūša—were selected to represent a wide range of hydromorphological and hydraulic conditions, including variations in bed texture, vegetation cover, and channel complexity. High-resolution digital elevation models (DEMs) were generated from field-based surveys and UAV imagery processed using Structure-from-Motion photogrammetry. Two-dimensional hydrodynamic models were created and calibrated in HEC-RAS 6.5 using measurement-based DEMs and subsequently applied using photogrammetry-derived DEMs to isolate the influence of terrain input on model performance. The results showed that UAV-derived DEMs systematically overestimate riverbed elevation, particularly in deeper or vegetated sections, resulting in underestimated water depths. RGB imagery provided greater spatial detail but was more susceptible to local anomalies, whereas multispectral imagery produced smoother surfaces with a stronger positive elevation bias. The fusion of RGB and multispectral imagery consistently reduced spatial noise and improved hydrodynamic simulation performance across all river types. Despite moderate vertical deviations of 0.10–0.25 m, relative flow patterns and velocity distributions were reproduced with acceptable accuracy. The findings demonstrate that combined spectral UAV aerial imagery in photogrammetry is a robust and cost-effective alternative for hydrodynamic modelling in shallow lowland rivers, particularly where relative hydraulic characteristics are of primary interest.

BackgroundMonte Carlo (MC)‐based single photon emission computed tomography (SPECT) reconstruction utilizes advanced system models that stochastically sample all possible photon interactions within the patient and detector, potentially increasing quantitative accuracy and precision. Despite this, there have been few studies that have rigorously compared conventional SPECT reconstruction and MC‐based reconstruction for metrics that are pertinent to radiopharmaceutical dosimetry.PurposeThis paper aims to compare conventional reconstruction with hybrid‐MC reconstruction and explores accuracy and precision in total activity estimation within various regions of interest in imaging.MethodsThe MC engine Simulation of Medical Imaging Nuclear Detectors (SIMIND) was integrated with the reconstruction software PyTomography to enable MC‐based reconstruction. The following are explored: (i) elimination of triple energy window (TEW)‐induced bias obtained by using an MC system model and (ii) improvements to precision and effective reductions in required scan time that are attained when using MC‐based reconstruction. The study explores multiple acquisitions of simulated and real phantom/patient data.ResultsConventional reconstruction with TEW induces a positive bias (e.g., 116.3% recovery coefficient, or RC, in a 72 mm sphere from the MC‐simulated data) that is not present with MC‐based reconstruction (e.g., 101.3 RC%). This source of positive bias raises RC in smaller spheres, effectively canceling with negative bias incurred from finite resolution; in real phantom and patient data, RCs are lower with MC‐based reconstruction than conventional, suggesting that MC‐based reconstruction is able to remove the additional source of bias caused by TEW. Furthermore, MC‐based reconstruction is able to reduce variability between subsequent acquisitions of the same phantom (e.g., reducing variability by (4.19±1.39)% in 10 mm sphere), thus resulting in reconstructed images comparable to those obtained with longer scan times (e.g., by a factor of 2.78±0.33 in the 32 mm sphere).ConclusionsCompared to conventional reconstruction that uses TEW to correct for scatter, MC‐based reconstruction for (i) reduces sources of systematic bias caused by inadequate TEW scatter estimation and (ii) reduced required scan times by reducing total uptake variability in regions of interest across different scans. For this reason, MC‐based reconstruction should be preferred to conventional reconstruction in clinical practice.

ABSTRACT Global warming is intensifying hydrological cycles, causing significant spatiotemporal variations in extreme precipitation. Since the 1980s, Northwest China has shifted from a warm‐dry to a warm‐wet climate regime, raising widespread concern. Employing skill metrics, this study quantitatively evaluates 23 statistically downscaled CMIP6 models from the NASA NEX‐GDDP dataset in simulating historical (1961–2014) extreme precipitation over Northwest China and, based on their performance, projects future changes in the mid‐ (2031–2060) and late‐21st century (2071–2100) under two scenarios. Six extreme precipitation indices are analysed: total wet‐day precipitation (PRCPTOT), very wet‐day precipitation (R95pTOT), maximum 5‐day precipitation (Rx5day), heavy precipitation days (R10mm), consecutive dry days (CDD) and consecutive wet days (CWD). Results show that most models reasonably capture spatial patterns (pattern correlation: 0.4–0.9), yet exhibit systematic dry biases (positive biases for CDD; negative biases for other indices). Interannual variability is better simulated in western subregions than eastern, particularly for PRCPTOT, R95pTOT, Rx5day and R10mm. Four models (CESM2, CESM2‐WACCM, CMCC‐CM2‐SR5 and EC‐Earth3‐Veg‐LR) demonstrate superior skill in spatiotemporal simulations. Projections from these best‐performing models indicate a mitigation of aridity and an increase in the frequency of extreme precipitation under SSP2‐4.5 and SSP5‐8.5 scenarios.

Sustainable agriculture, particularly in semi-arid areas, depends on precise crop mapping and yield forecasting. In this study, Random Forest (RF) classification and regression implemented in Google Earth Engine (GEE) are combined with GeoAI and machine learning techniques. It evaluates crop distribution and predicts yields in Sokoto State, Nigeria, using Sentinel-2 surface reflectance imagery. 3,167.57 hectares of cropland were analysed using 690 cloud-filtered Sentinel-2 scenes that were gathered between January and December of 2023. Crop classification demonstrated good reliability in identifying rice, pepper, and onion crops with an overall accuracy of 99.7% and a Kappa coefficient of 0.82. With a coefficient of determination (R²) of 0.84 and a Pearson correlation of 0.92, the Random Forest yield model predicted crop yields, demonstrating a very significant positive link between observed and anticipated yields. With a little positive bias of +9,244 kg/ha, the model's root mean squared error (RMSE) and mean absolute error (MAE) were 10,870 kg/ha and 9,244 kg/ha, respectively. According to yield figures, the average yield was 20,971 kg/ha, with a range of 7,000 to 40,000 kg/ha. These findings show how precise crop mapping and yield estimation can be achieved by combining GeoAI, multispectral satellite data, and machine-learning approaches. The results assist better decision-making, sustainable farming methods, and increased food security in semi-arid regions by providing agricultural scientists and policymakers with practical insights.

Facial recognition in late-life insomnia: Preserved positivity bias and associations with negative emotions.

Textile-based displays are emerging as promising candidates for next-generation wearable electronics owing to their conformability and wearability. However, their backplanes suffer from severe electrical instabilities arising from the intrinsic properties of textile substrates. Here, 2T1C pixel circuits were fabricated on textiles through low-temperature processes below 120 °C, exhibiting a representative mobility of ∼8.5 cm2 V-1 s-1, an on/off ratio of ∼7.0 × 108, and a reliable operation under tensile strain up to 0.87%. Integration with OLEDs confirmed the feasibility of extending textile circuits toward AMOLED implementation. Nevertheless, comprehensive evaluations, including bias stress, breakdown-voltage testing, and temperature-dependent transport, revealed pronounced instabilities arising from substrate deformation, low thermal conductivity, and surface charges. To address these limitations, a bottom shield metal (BSM) structure was introduced as a structural strategy to improve thermal management, electrostatic shielding, and mechanical stability. The BSM suppressed threshold-voltage shifts under positive bias stress, enhanced charge retention, and increased breakdown voltage by over 12 V while also stabilizing pulsed operation. Integration with top-emitting OLEDs further validated the practical applicability of BSM-integrated circuits to textile AMOLEDs. Overall, the application of the BSM structure effectively mitigates the fundamental instabilities of textile backplanes, providing a pathway toward high-performance and reliable textile display systems.

Mistakes are valuable learning opportunities, yet in uncertain environments, whether a lack of reward is due to poor performance or bad luck can be hard to tell. To investigate how humans address this issue, we developed a visuomotor task where rewards depended on either skill or chance. Participants consistently displayed a self-attribution bias, crediting successes to their own ability while blaming failures on randomness, an effect that influenced their subsequent decisions. Computational modelling revealed two underlying mechanisms-a distorted perception of ability and a positivity bias in the skill condition. Notably, while distorted self-perception shaped behaviour, it did not affect confidence; instead, self-attribution bias led to overconfidence in external blame. These findings suggest a more complex picture in which self-attribution biases arise from both perceptual distortions and post-decision evaluations, highlighting the need for an interplay between experimental design and computational modelling to understand behavioural biases.

Mistakes are valuable learning opportunities, yet in uncertain environments, whether a lack of reward is due to poor performance or bad luck can be hard to tell. To investigate how humans address this issue, we developed a visuomotor task where rewards depended on either skill or chance. Participants consistently displayed a self-attribution bias, crediting successes to their own ability while blaming failures on randomness, an effect that influenced their subsequent decisions. Computational modelling revealed two underlying mechanisms—a distorted perception of ability and a positivity bias in the skill condition. Notably, while distorted self-perception shaped behaviour, it did not affect confidence; instead, self-attribution bias led to overconfidence in external blame. These findings suggest a more complex picture in which self-attribution biases arise from both perceptual distortions and post-decision evaluations, highlighting the need for an interplay between experimental design and computational modelling to understand behavioural biases.

ABSTRACT Indium gallium zinc oxide (IGZO) thin film transistors (TFTs) with high stability are highly desired for future memory devices, which demand high stabilities. However, residual precursors inevitably present in the dielectric layers deposited by atomic layer deposition (ALD) introduce hydrogen contamination to the devices, thereby impairing their electrical stabilities. In this work, an effective approach is proposed: performing oxygen plasma treatment on the dielectric‐channel interface to suppress hydrogen diffusion, thereby enhancing the electrical stability of IGZO TFTs. X‐ray photoelectron spectroscopy (XPS) results confirm the suppression effect of oxygen treatment on hydroxyl groups, while time of flight secondary ion mass spectrometry (TOF‐SIMS) revealed a 16.46% reduction in hydrogen content within the treated Al 2 O 3 layer. The devices after treatment exhibited a field‐effect mobility (µ eff ) of 17.4 cm 2 /V·s, a threshold voltage (V TH ) of −0.04 V, and a subthreshold swing (SS) of 84.7 mV/dec, with an optimized oxygen plasma power of 50 W. The positive bias temperature stability of the device is significantly promoted due to reduced hydrogen content. The V TH shift (ΔV TH ) is merely 3.5 mV under a bias electric field of 2 MV/cm for 10 000 s. Such treatment provides a promising solution for the integration of IGZO TFTs with Si‐based electronics.

ABSTRACTBackgroundAnemia remains a major health concern, particularly during pregnancy. However, blood draws, laboratory capacity, processing time, and equipment cost required for hemoglobin testing to diagnose anemia can be prohibitive. The Total Hemoglobin SpHb Rad-67 Pulse CO-Oximeter offers a non-invasive, portable, and affordable point-of-care alternative. We aimed to validate SpHb against complete blood count (CBC) in a pregnant and postpartum population.MethodsThis was a substudy of the Pregnancy Risk, Infant Surveillance, and Measurement Alliance (PRISMA) Maternal and Newborn Health Study. A total of 2,700 participants in Zambia, Kenya, and Pakistan provided hemoglobin measurements both by CBC and the Rad-67 Masimo SpHb device at four visits during pregnancy and at six-weeks postpartum. We assessed agreement between SpHb and CBC and used mixed models to identify factors that explained or influenced differences between the two methods.ResultsWe found the mean hemoglobin measurement by SpHb (12.8±1.6 g/dL) was higher than by CBC (11.0±1.6 g/dL). However, this overall positive bias masked systematic misclassification at the extremes: SpHb overestimated values among women with very low hemoglobin (Mean Difference (MD) =-3.95; 95%CI-4.28,-3.62) and underestimated values at very high hemoglobin levels (MD = 2.44; 95%CI 2.14, 2.74), even after adjustment. Using CBC, 48% of observations were classified as anemic (<11 g/dL), compared to 9% by SpHb; conversely, just 7% of CBC readings fell within 13-15 g/dL, compared to 38% by SpHb. Agreement metrics consistently showed poor concordance between the two methods.ConclusionsSpHb systematically overestimated hemoglobin on average and showed poor agreement with CBC, particularly at clinically relevant extremes. Until greater accuracy of SpHb is demonstrated in this population, hemoglobin testing with laboratory-based methods is recommended to inform clinical decision-making in pregnancy.AUTHOR SUMMARYAnemia is a major global health challenge linked with maternal morbidity, adverse birth outcomes, and impaired infant development. Accurate and accessible hemoglobin testing is critical for timely anemia diagnosis and treatment. The gold standard for hemoglobin testing is complete blood count using venous blood; however, this method requires laboratory infrastructure, blood sample collection, and trained personnel, limiting feasibility in low-resource and rural settings where anemia burden is highest. We evaluated the accuracy of the Masimo Total Hemoglobin SpHb® measured by Rad-67® Pulse CO-Oximeter®: a device that measures hemoglobin via an optical sensor placed on the patient’s finger. We found the mean hemoglobin measurement by SpHb (12.8±1.6 g/dL) was higher than by CBC (11.0±1.6 g/dL).We found that the Masimo device consistently overestimated hemoglobin at very low levels and underestimated at very high levels. Masimo SpHb classified 9% of women as anemic (i.e. hemoglobin <11 g/dL), compared to 48% using the gold standard method. The Masimo SpHb was even less accurate for women later in gestation, living with HIV, and who reported using betelnut, tobacco, or smoking. Device improvements or software-based correction factors are needed to improve performance of the Total Hemoglobin SpHb Rad-67 Pulse CO-Oximeter before it can be used to measure hemoglobin in pregnancy.

Abstract We examine the intensity, duration, and physical drivers of Northern Hemisphere summer heat extremes in observations, reanalyses, CMIP6 models, and prescribed sea surface temperature (SST) simulations representing historical- and future-projected climates. Extremes are defined using a 90th percentile anomaly defined relative to a 29-day × 11-yr running mean to examine how the tail of temperature anomaly distributions changes in projections. Models generally capture the observed regional variations of intensity and duration. The magnitude of 90th percentile temperature anomalies shows a generally positive meridional gradient with some zonal variation but with warm biases over eastern India and southern North America. Both the reanalysis and climate model simulations exhibit positive biases in the duration of heat extremes in regions where the lowest model level temperature budget shows that diabatic effects dominate: Mexico extending through Central America, northeastern South America, and India. By investigating both prescribed SST forced by observations and coupled ocean simulations, heat extreme biases do not seem to be SST driven. In the warming simulations, projections under medium [shared socioeconomic pathway (SSP) 2–4.5] and high emission (SSP5–8.5) scenarios for late twenty-first century demonstrate sign change agreement for more intense anomalies over the Southeast United States, the Sahel and central Africa, and polar regions, along with weaker anomalies over Greenland brought on by melting ice. In terms of duration, the models have lesser agreement, likely due to internal variability, but nonetheless project increased duration over Northwest and Southeast United States and a reduction in northwest Canada and northern Africa.