The aim of this study was to quantitatively evaluate the impact of gantry, collimator, and couch rotational errors in a linear accelerator on the irradiation accuracy of off-isocenter targets, and to assess the validity of the rotational error tolerance (±1.0°) specified in American Association of Physicists in Medicine TG142. Using an Elekta linear accelerator (Elekta, Stockholm, Sweden) and the MultiMet-WL QA phantom (Sun Nuclear, Melbourne, FL, USA), an off-isocenter Winston-Lutz test was performed on six targets. In addition to baseline measurements, six conditions were evaluated by intentionally introducing rotational errors of +0.5° and +1.0° in the collimator, gantry, and couch. The vector distance (S value) between the field center and the target center, as well as positional deviations in each direction (gantry-target: GT, left-right: LR, anterior-posterior: AP), were analyzed. Targets located farther from the isocenter exhibited more significant positional deviations. The collimator rotation had the greatest impact; at 7 cm from the isocenter, even a 0.5° error resulted in a maximum S value of 1.24 mm. Couch rotation had the next largest effect, while gantry rotation had relatively smaller effects, likely because most targets were located near the gantry's rotational axis. The rotational errors mainly caused geometric deviations with direction-dependent positional shifts. The effects of the collimator and couch were substantial, with positional deviations exceeding 1 mm even for a 0.5° rotation error. The influence of the gantry was relatively small and dependent on the target configuration. For irradiation of off-axis targets, the TG142 tolerance of ±1.0° should be regarded as a minimum standard that must be strictly observed regardless of the type of linear accelerator. However, depending on the target arrangement, clinically adequate margins may not be ensured. These findings suggest the necessity of applying stricter criteria according to target configuration and emphasize the importance of regular quality assurance.

Stagnating declines in cardiovascular disease mortality in the United States expanded the black-white life expectancy gap.

ABSTRACT This study tests whether misinformation on social media undermines citizens’ capability to perceive the factual public health information provided by the government during the pandemic and whether such misinformation impacts citizens’ distrust in the government. The results of the survey experiment indicate that the simple presence of misinformation on a social media platform distorts citizens’ perceptions of factual pandemic information, while the frequent use of social media shows the opposite but a smaller effect. Also, we find that perceptual distortion due to misinformation does not directly impact citizens’ distrust in the government, while political polarization appears to have a larger effect. We discuss the implications of the behavioral and cognitive mechanisms of information distortion and citizens’ distrust in the government in an international setting.

Abstract Background Dementia has become an important public health challenge as the aging population in China accelerates, highlighting the need to identify modifiable risk factors. The cumulative atherogenic index of plasma (CumAIP), a marker reflecting long-term dyslipidemia, may contribute to dementia via vascular and inflammatory pathways, but longitudinal evidence in Chinese adults around 60 years of age remains scarce. Methods Data were extracted from three waves of the China Health and Retirement Longitudinal Study (CHARLS): 2012 (Wave 1), 2015 (Wave 3), and 2018 (Wave 4). A total of 6473 participants with complete AIP measurements at 2012 and 2015 were included; CumAIP was computed as the time-weighted average of AIP values, normalized by the 2012–2015 observation duration. Dementia risk (primary outcome) was assessed via the Rotterdam Basic Dementia Risk Model (BDRM) using 2018 data, reflecting a 3-year follow-up with 2015 as the baseline. To validate the BDRM, we conducted a secondary analysis: Spearman rank correlation between 2018 BDRM scores and 2015 CHARLS cognitive function scores. Statistical analyses included: multivariable linear regression with three progressive adjustment tiers (sociodemographic, clinical, lifestyle factors); restricted cubic spline (RCS) curves (3 knots at 10th/50th/90th CumAIP percentiles) for linearity testing; quartile-based analyses (CumAIPQ1–Q4, Q1 as reference) for dose–response relationships; and subgroup (by gender, age, residence, and lifestyle/metabolic factors) and sensitivity analyses [multiple imputation, complete-case analysis, linear regression of CumAIP vs. 2015 cognitive function scores, linear regression of low-density lipoprotein cholesterol (LDL-C) vs. BDRM scores] to validate robustness. Results First, the 2018 BDRM score was significantly negatively correlated with the 2015 cognitive function score (Spearman’s r = − 0.26, P < 0.001). Higher CumAIP was associated with elevated BDRM scores, remaining significant after full adjustment ( β = 0.058, 95% CI 0.018–0.098, P = 0.004). RCS confirmed a linear relationship (nonlinear term P = 0.9671), and quartile analyses showed a dose–response trend (Q4 vs. Q1, β = 0.103, 95% CI 0.009–0.197, P = 0.031). Subgroup effects were more pronounced in females, those aged ≥ 60 years, rural residents, nondrinkers, smokers, and BMI ≥ 24 kg/m 2 . Sensitivity analyses validated robustness: (1) CumAIP correlated positively with 2015 cognitive function ( β = 0.123, 95% CI 0.008–0.237, P = 0.037); (2) LDL-C correlated positively with BDRM scores ( β = 0.019, 95% CI 0.009–0.028, P = 0.0001), with a smaller effect than CumAIP. Conclusion Cumulative elevated CumAIP is independently associated with higher BDRM-estimated dementia risk in Chinese adults (mean age 61.4 ± 8.5 years), with linear and dose-dependent relationships. The association is more prominent in high-risk subgroups: females, aged ≥ 60 years, rural residents, nondrinkers, smokers, and BMI ≥ 24 kg/m 2 . The BDRM’s validity supports its reliability. CumAIP’s stronger association with dementia risk than LDL-C underscores its value as a comprehensive lipid marker for risk stratification. Dynamic lipid monitoring to maintain low CumAIP may aid dementia prevention in these high-risk groups.

There are widespread fears that conversational artificial intelligence (AI) could soon exert unprecedented influence over human beliefs. In this work, in three large-scale experiments (N = 76,977 participants), we deployed 19 large language models (LLMs)-including some post-trained explicitly for persuasion-to evaluate their persuasiveness on 707 political issues. We then checked the factual accuracy of 466,769 resulting LLM claims. We show that the persuasive power of current and near-future AI is likely to stem more from post-training and prompting methods-which boosted persuasiveness by as much as 51 and 27%, respectively-than from personalization or increasing model scale, which had smaller effects. We further show that these methods increased persuasion by exploiting LLMs' ability to rapidly access and strategically deploy information and that, notably, where they increased AI persuasiveness, they also systematically decreased factual accuracy.

The role of ammonia in virus inactivation: A systematic and meta-analysis review.

BackgroundAlzheimer’s disease (AD) is a multifactorial disease influenced by both genetic and clinical risk factors, yet how these factors are associated with AD‐specific or neurodegeneration pathologies remain unclear. Clinical risk scores (CRS) reflect susceptibility to cognitive decline based on lifestyle and other modifiable factors. Genetically, APOE ε4 is the strongest known risk factor for AD, but additional genetic contributions can be captured through polygenic risk scores (PRS). This study investigates how APOE‐ε4, PRS, and CRS independently contribute to amyloid‐β/tau (i.e. AD) and neurodegenerative pathways in the Healthy Aging Brain Study–Health Disparities (HABS‐HD) cohort.MethodsIn HABS‐HD (N =1,890; mean age=67±7.7; African‐descent=328, Amerindian‐descent=647, European‐descent=915), we constructed AD‐PRS using PRS‐CSx‐auto with ancestry‐specific AD genome‐wide association study summary statistics from European, African American, East Asian, and Caribbean Hispanic populations. The Cognitive Health and Dementia Risk Assessment score (CogDrisk), a CRS, was calculated using 17 risk factors for dementia (e.g. age, sex, education, hypertension). Structural equation modeling was used to derive an AD latent variable, indicated by plasma /Aβ40 ratio, plasma pTau181, Aβ PET positivity, and global SUVR; and a neurodegeneration latent variable, indicated by plasma NfL, cortical thickness, and hippocampal volume (Figure 1). Latent variables were regressed on AD‐PRS, CogDrisk, and APOE‐ε4 status, adjusting for race and population stratification.ResultsAPOE‐ε4 was significantly associated with both AD (β=0.30, p <0.001) and neurodegeneration latent variables (β=0.11, p<0.001). AD‐PRS was significantly associated with the AD (β=0.089, p=8.22e‐3) but not with the neurodegeneration latent variable (β=0.039, p=0.21). In contrast, CogDrisk was significantly associated with both the AD (β=0.34, p<0.001) and the neurodegeneration latent variables (β=0.72, p<0.001).ConclusionsAPOE‐ε4 and CRS captures changes related to both neurodegeneration and AD neuropathological proteins, whereas PRS is more specific to AD with the smallest effects. APOE‐ε4 remains as the stronger genetic predictor. These findings highlight distinct yet orthogonal roles of genetic and clinical risk factors in dementia pathogenesis.

ABSTRACT Atmospheric water vapour constitutes the primary source of delay errors in time-series InSAR. Even after implementing conventional spatiotemporal filtering and GACOS-based atmospheric correction, residual errors persist, compromising deformation monitoring accuracy. Consequently, using Tianjin as the study area. Sentinel-1A SAR imagery and PS-InSAR technique are utilized to derive time-series land subsidence information, following prior atmospheric correction via GACOS. By integrating observational data from 15 GNSS station, a systematic analysis is conducted on the residual atmospheric effects remaining in post-GACOS time-series InSAR deformation results. Furthermore, leveraging ERA5 PWV datasets, correction models tailored to residual atmospheric error in time-series InSAR across different weather scenarios are developed, with the aim of enhancing the accuracy of time-series InSAR-based deformation monitoring. Results show that residual effects persist in the time-series InSAR deformation results after GACOS-based atmospheric correction under rainfall, haze, and snowfall conditions. Rainfall in summer induces the most significant atmospheric residual error on InSAR deformation accuracy, with impacts of up to 8 mm. In contrast, haze and snowfall in autumn, winter, and spring have smaller effects on deformation accuracy than those during the rainy season, approximately 2 mm. A significant negative correlation exists between the atmospheric residual error of time-series InSAR during rainfall, haze, and snowfall events and the ΔPWV over the same periods. Moreover, the correlation coefficients are all greater than −0.85. Subsequently, using the 11 GNSS and ERA5 Δ PWV data, a time-series InSAR atmospheric residual error model was constructed via the linear regression method. Then, based on the results of the 4 GNSS and the precise levelling measurements of the two engineering projects, the reliability of the model was verified. After model correction, the deformation accuracy of time-series InSAR is better than 1 mm.

ABSTRACT Systems thinking (ST), a key component of higher‐order thinking, is important in all areas of science education to support scientifically literate citizens who make informed decisions based on science in a global society. Computational modeling (CM) is the practical process of constructing, applying, debugging, and evaluating models by using computational tools. The close mapping of CM processes to ST processes helps students develop ST skills. Yet, there is an absence of research examining the overall impact of CM on students' ST in science education. To fill this gap in the literature, the study used meta‐analysis to analyze 62 effect sizes from 25 studies between 2009 and 2024. The results of the random effects model revealed that CM had a significant positive effect on K‐16 students' ST ( g = 0.470), with the largest effect on applying and evaluating systems ( g = 0.617), followed by identifying system structure ( g = 0.447), and a smaller effect on analyzing system behavior ( g = 0.318). The main findings of the moderator analysis were that CM had a larger impact (a) in biology and ecology disciplines; (b) when using low‐complexity CM tools such as agent‐based block modeling and window‐based applications; (c) when English was the language of instruction; and (d) for the non‐control group and the control group with normative instruction compared to the control group for complex instruction. The study presents specific suggestions for designers of instruction/technology, science teachers, and researchers to better improve students' ST through CM.

In wet clutch systems, the drag torque is generated by the relative motion between rotating disks in the presence of the oil film. This study aims to optimize the geometry of radially grooved wet clutches to minimize drag torque. A recently developed analytical model was used to efficiently generate the required dataset in both the single- and multiphase flow conditions, as numerical simulations are computationally expensive. The accuracy of the model was first validated with CFD, showing strong agreement with a maximum deviation of 8%. Two artificial neural networks were then trained on the generated dataset and exhibited reliable performance. In the following, these data-driven models were used in an optimization study across four distinct design cases using the genetic algorithm. Numerical simulation of the fluid flow in optimized geometries confirmed significant drag torque reductions of at least 70% across all cases, with the most substantial improvement observed in Case 3 where peak drag torque decreased from 0.92 to 0.022, representing a 97% reduction. Ultimately, a parametric study was conducted to interpret the optimization results. The results showed that changing the distance between the two disks had the most significant impact on drag torque, reducing the peak value by 86%, while the groove angle had the smallest effect, with only a 2% reduction.

Recycling agricultural residues is a promising strategy to enhance soil organic carbon (SOC) and improve soil quality. This study investigated the effects of exogenous organic carbon (EOC) amendments—straw and hydrochar—on SOC, its labile fractions, and the carbon pool management index (CPMI, an indicator of soil carbon quality and management efficiency) under flooding (FI) and controlled irrigation (CI) in a two-year pot experiment using paddy soil under field conditions. CI improved the soil average readily oxidizable organic carbon (ROC), dissolved organic carbon (DOC), and microbial biomass carbon (MBC) by 6.37–12.19%, 18.70–26.00% (p &lt; 0.05), and 11.95–17.97% (p &lt; 0.05), compared to FI. Similarly, EOC addition increased average ROC, DOC, and MBC during the entire rice growth period by 12.33–22.95%, 4.50–24.35%, and 6.24–21.51%, respectively, compared to the unamended controls. Additionally, CI increased soil carbon lability (L), carbon pool activity index (LI), carbon pool index (CPI), and CPMI by 3.39–14.01%, 3.65–8.84%, 1.75–2.58%, and 6.19–16.01%, respectively, although some of these increases were not statistically significant. Notably, the combination of CI and EOC application significantly increased CPMI by 19.45–20.29% (p &lt; 0.05), with the highest values observed in CI treatments amended with either straw or hydrochar. Hydrochar application had a smaller effect on increasing soil active OC fractions compared to straw incorporation, but demonstrated a greater potential for long-term SOC sequestration. These findings demonstrate the potential of hydrochar as a waste-derived amendment for long-term carbon sequestration and provide insights for optimizing water–carbon management strategies in sustainable rice cultivation.

Carbonic anhydrase modulates photosynthetic responses to UV radiation in diatoms across temperature gradients.

This study investigates the influence of Fused Filament Fabrication (FFF) parameters on the properties of polyamide (PA, Nylon™) parts, which are valued for their excellent mechanical properties in additive manufacturing. The parameters examined include infill structure (diagonal and honeycomb), infill density (60%, 80%, and 100%), and sample orientation (0°, 45°, and 90°) relative to the build plate. Filaments from five manufacturers were tested, with injection-molded samples serving as references. Standard tensile strength tests were performed. The results indicate that the 0° orientation yielded the highest tensile strength, while the 45° and 90° orientations exhibited distinct behaviors associated with the geometry of additive manufacturing. The highest Young's modulus was obtained for solid infill at 0° orientation. Although infill structure had a smaller effect, the honeycomb pattern provided more stable and superior mechanical properties at higher infill densities. The study compared filaments from different manufacturers, identifying two that met the tensile strength requirements for telerehabilitation device case prototypes.

MXenes, with tunable compositions and rich surface chemistry, enable precise control of electronic, optical, and mechanical properties, making them promising materials in electronics and energy-related applications. In particular, the work function plays a critical role in determining their physicochemical properties. However, the accurate prediction of the work function of MXenes with machine learning (ML) remains challenging due to the lack of robust models with high accuracy and interpretability. To this end, we propose a stacked model and introduce high-quality descriptors constructed via Sure Independence Screening and Sparsifying Operator method to improve the prediction accuracy of the work function of MXenes in this work. The stacked model initially generates predictions from multiple base models, and then employs these predictions as inputs to a meta-model for secondary learning, thereby enhancing both predictive performance and generalization capability. The results show that by integrating the high-quality descriptors, the model’s performance improves significantly, yielding a coefficient of determination of 0.95 and mean absolute error of 0.2, respectively. Last but not least, we demonstrate that MXenes’ work functions are predominantly governed by their surface functional groups, where SHapley Additive exPlanations value analysis quantitatively resolves the structure–property relationship between surface functional groups and the work function of MXenes. Specifically, O terminations can lead to the highest work functions, while OH terminations result in the lowest value (over 50% reduction), and transition metals or C/N elements have a relatively smaller effect. This work achieves an optimal balance between accuracy and interpretability in ML predictions of MXenes’ work functions, providing both fundamental insights and practical tools for materials discovery.

This study investigates the relationships among psychological capital, educational environment and entrepreneurial decision-making among Vietnamese economics students. Structural equation modeling (SEM) was employed to assess both the measurement and structural models. The measurement model showed strong reliability and convergent validity with Cronbach’s alpha values ranging from 0.785 to 0.919 and all average variance extracted (AVE) values exceeding 0.50. The results from the structural model indicate that entrepreneurial tendency (ET) is significantly affected by hope (HO), self-efficacy (SE) and opportunity perception (OP). Resilience (RE) and educational environment (EE) also have positive but smaller effects. Among these predictors, HO has the strongest influence on ET (β = 0.431, p &lt; 0.001). Moreover, ET has a significant and positive impact on entrepreneurial decision (ED) (β = 0.416, p &lt; 0.001). The model accounts for 34.6 percent of the variance in ET and 17.3 percent of the variance in ED, reflecting moderate explanatory power. The findings emphasize the importance of psychological capital, especially hope and self-efficacy, together with a supportive educational environment, in fostering students’ entrepreneurial intention and decision-making. These results provide useful insights for universities and policymakers seeking to strengthen entrepreneurship education and promote entrepreneurial activities among young Vietnamese learners.

Using a relativistic mean-field model calibrated to finite-nucleus observables and bulk properties of dense nuclear matter, we investigate hyperonic neutron-star matter within an SU(3) flavor symmetry scheme. To retain SU(6)-based couplings within SU(3) flavor symmetry, we introduce a quartic ϕ self-interaction and ϕ-ρ mixing. We demonstrate the roles of αv (F/(F+D) ratio), θv (mixing angle), and zv (singlet-to-octet coupling ratio) in SU(3)-invariant vector-meson couplings. It is found that zv predominantly controls the maximum mass of a neutron star, and 2M⊙ neutron stars can be supported for zv≤0.15. The αv also helps sustain large masses, whereas θv has a smaller effect on neutron-star properties. This SU(3) framework reconciles nuclear and astrophysical constraints and offers a plausible resolution of the hyperon puzzle.

Atypical visual exploration is widely reported in autism spectrum disorder (ASD), yet little is known about how chromatic properties modulate oculomotor behaviour. This study aims to address this problem. We recorded eye movements from 34 children with ASD (6-16 y) and 40 age-matched typically developing (TD) peers while they viewed six picture sets that independently manipulated hue, luminance and saturation. Nine eye-movement metrics were extracted per task and analysed with mixed-effects ANOVAs; false-discovery-rate control (q = .05) and Holm-Bonferroni corrections were applied across 54 planned tests. Across all tasks, the ASD group showed a 29% reduction in total fixation time (F = 11.13, q < .001; pHolm < .001†), a 19% reduction in fixation count (F = 5.88, q = .027), and 42% fewer saccades (F = 16.06, q < .001; pHolm < .001†). Individual fixations were shorter (F = 8.40, q < .001; pHolm < .001†). These differences generalised across all areas of interest. Stimulus-specific analyses revealed that highly saturated images amplified group differences in fixation metrics, whereas hue and luminance manipulations produced parallel but smaller effects. Children with ASD allocate less dwell time and execute fewer, briefer fixations and saccades, especially when stimuli are highly saturated. Because effects persisted after stringent multiple-comparison correction and were independent of pupil size, they likely reflect how brightness changes guide eye movements and moment-to-moment arousal.

Background: Anomalous aortic origin of a coronary artery (AAOCA) is associated with myocardial ischemia (MI) and sudden cardiac death. Symptoms, positive stress tests, and presence of “high-risk” morphologic features guide management. However, the optimal stress-testing strategy and the extent to which anatomic features cause MI remain unclear. We sought to assess the effect of coronary morphology on the presence of a positive result in different stress test modalities. Methods: We retrospectively studied 548 adults with AAOCA at our institution (July 2015 - March 2023). Coronary morphology, defined from operative and imaging reports, included the affected coronary (right [RCA], left main [LMCA], left anterior descending, left circumflex) and course type (intramural, interarterial-only, transeptal, and other [prepulmonic and retroaortic]). Exercise and pharmacologic stress tests were available in 397 (72%) of patients, comprising 701 ECGs, 198 echocardiograms, 288 SPECTs, 135 PETs, and 102 dobutamine iFR catheterizations (positive if iFR &lt;0.86). Since tests were repeated (n = 1,424), we used mixed-effect logistic regression to model the probability of a positive result based on coronary morphology, age, sex, comorbidities (e.g. coronary artery disease, myocardial bridge), and modality. For patients with iFR, random forest regression assessed associations between iFR as a continuous variable and the same predictors. Results: Mean age at AAOCA diagnosis was 50.4 ± 16.9 years (SD), and 67% had chest pain. Compared to anomalous RCA, anomalous LMCA was more likely to have a positive stress test (OR 2.4, p = 0.02). Intramural course trended toward positive result (OR 1.9, p = 0.14), while the transeptal and interarterial had smaller effects. Compared to ECG, iFR was most likely to be positive (OR 27, p &lt; 0.001), followed by PET (OR 8.4, p &lt; 0.001). In iFR-only analysis, course type was most associated with a positive result: transeptal had the lowest mean stress iFR (0.77), followed by intramural (0.83), and interarterial (0.85). Conclusions: In our large adult AAOCA cohort, coronary morphology correlated with a positive stress test, but this relationship was inconsistent across modalities. High-risk morphologic features alone were not reliably predictive of a positive result, and integrating functional testing is essential for risk stratification that guides management. Long-term follow-up is needed to determine the best ischemia testing strategy.

Quantifying the impact of vegetation greenness change on surface urban heat islands across 133 major cities of China.

Craving for opioid and cannabis use among adults with chronic pain: Insights from a 30-day ecological momentary assessment study.