Search

Rayleigh waves are highly sensitive to surface and subsurface defects. However, the varying surface profiles of complex structures induce constant changes in the Rayleigh waveform. Furthermore, Rayleigh waves encompass multi-dimensional characteristics of subsurface defects, significantly increasing the difficulty of quantitatively detecting the sizes of subsurface defects in different dimensions. This paper presents a machine learning (ML)-based method for quantitatively detecting subsurface defect depth and width in curved structures using laser-generated Rayleigh waves. An experimentally validated finite element model is first established to construct a dataset of Rayleigh wave signals labeled with multidimensional subsurface defect sizes. The dataset is augmented from 188 to 368 samples using the Mix-up algorithm, with additional experimental signals incorporated. Feature parameters of Rayleigh waves are extracted in both time and frequency domains through waveform analysis and sliding window wavelet transform (SWT). These features are used to train ML models: Gaussian Process (GP), Extreme Gradient Boosting (XGBoost), and Random Forest (RF). Comparative results demonstrate GP achieves the highest prediction accuracy for subsurface defect depth and width on the test set, reaching 98.64% and 97.73%, respectively. The study highlights the integration of experimental and simulated data in training, which reduces costs while enhancing model robustness in practical applications. [ABSTRACT FROM AUTHOR]

Tropical cyclones (TCs) are powerful weather systems that can cause extreme disasters. The International Best Track Archive for Climate Stewardship (IBTrACS) dataset has been used extensively to estimate TC climatology. However, it has low data coverage, lacking intensity and outer size data for more than half of all recorded storms, and is therefore insufficient as a reference for researchers and decision makers. To fill this data gap, we reconstructed a long-term TC dataset by integrating IBTrACS and European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5) data. This new dataset covers the period 1959–2022, with 3 h temporal resolution. Compared to the IBTrACS dataset, it contains approximately 3–4 times more data points per characteristic. We established machine learning models to estimate the maximum sustained wind speed (Vmax) and radius to maximum wind speed (Rmax) in six basins for which TCs were generated using ERA5-derived 10 m azimuthal median azimuthal wind profiles as input, with Vmax and Rmax data from the IBTrACS dataset used as training data. An empirical wind–pressure relationship and six wind profile models were employed to estimate the minimum central pressure (Pmin) and outer size of the TCs, respectively. Overall, this high-resolution TC reconstruction dataset demonstrated global consistency with observations, exhibiting mean biases of <1 % for Vmax and 3 % for Rmax and Pmin in almost all basins. The new dataset is publicly available from https://doi.org/10.5281/zenodo.12740372 (Xu et al., 2024) and significantly advances our understanding of TC climatology, thereby facilitating risk assessments and defenses against TC-related disasters. [ABSTRACT FROM AUTHOR]

Background: Chronological age (CA) is an imperfect proxy for the true biological aging state of the body. As novel measures of biological aging, Phenotypic age (PhenoAge) and Phenotypic age acceleration (PhenoAgeAccel), have been shown to identify morbidity and mortality risks in the general population. Hypothesis: PhenoAge and PhenoAgeAccel might be associated with mortality in heart failure (HF) patients. Methods: This cohort study extracted adult data from the National Health and Nutrition Examination Survey (NHANES) databases. Weighted univariable and multivariable Cox models were performed to analyze the effect of PhenoAge and PhenoAgeAccel on all‐cause mortality in HF patients, and hazard ratio (HR) with 95% confidence intervals (CI) was calculated. Results: In total, 845 HF patients were identified, with 626 all‐cause mortality patients. The findings suggested that (1) each 1‐ and 10‐year increase in PhenoAge were associated with a 3% (HR = 1.03, 95% CI: 1.03–1.04) and 41% (HR = 1.41, 95% CI: 1.29–1.54) increased risk of all‐cause mortality, respectively; (2) when the PhenoAgeAccel < 0 as reference, the ≥ 0 group was associated with higher risk of all‐cause mortality (HR = 1.91, 95% CI = 1.49–2.45). Subgroup analyses showed that (1) older PhenoAge was associated with an increased risk of all‐cause mortality in all subgroups; (2) when the PhenoAgeAccel < 0 as a reference, PhenoAgeAccel ≥ 0 was associated with a higher risk of all‐cause mortality in all subgroups. Conclusion: Older PhenoAge was associated with an increased risk of all‐cause mortality in HF patients. PhenoAge and PhenoAgeAccel can be used as convenient tools to facilitate the identification of at‐risk individuals with HF and the evaluation of intervention efficacy. [ABSTRACT FROM AUTHOR]

The development of urea oxidation reaction (UOR) and oxygen evolution reaction (OER) bifunctional electrocatalysts has dual significance in promoting hydrogen energy production and urea-rich wastewater treatment. Herein, a carboxylated multi-walled carbon nanotube (MWCNT–COOH)–ferrocene carboxylic acid (Fc–COOH) modulated NiMOF hybrid material (MWCNT–NiMOF(Fc)) has been synthesized for dual electrocatalysis of the UOR and OER. The material characterization results indicated that MWCNT–COOH and Fc–COOH were integrated into the framework structure of the NiMOF. The direct interaction between the NiMOF and MWCNT/Fc facilitated electron transfer in the hybrid material and led to lattice strain, which improved the charge transfer kinetics, promoted the exposure of more unsaturated Ni sites, and increased the electrochemically active surface area. These factors together enhanced the electrocatalytic activity of MWCNT–NiMOF(Fc) towards the UOR and OER. Using a glassy carbon electrode as the substrate, MWCNT–NiMOF(Fc) exhibited low potential requirements, low Tafel slopes, and high stability. In overall urea and water splitting electrolysis cells, the excellent UOR and OER dual functional catalytic ability and enormous practical application potential of the MWCNT–NiMOF(Fc) modified foam nickel electrode were further demonstrated. On the basis of the above research, the influence of a KOH environment on urea electrolysis was further studied, and the urea electrolysis products were analyzed, promoting a more comprehensive understanding of the catalytic performance of MWCNT–NiMOF(Fc) for urea oxidation. This study provides a new approach for developing high-performance NiMOF-based electrocatalysts for challenging bifunctional UOR/OER applications, and has potential application value in hydrogen production from urea-containing wastewater electrolysis. [ABSTRACT FROM AUTHOR]

Optimal plant growth in many species is achieved when the two major forms of N are supplied at a particular ratio. This study investigated optimal nitrogen forms and ratios for tomato growth using the 'Jingfan 502' tomato variety. Thirteen treatments were applied with varying proportions of nitrate nitrogen (NN), ammonium nitrogen (AN), and urea nitrogen (UN). Results revealed that the combination of AN and UN inhibited tomato growth and photosynthetic capacity. Conversely, the joint application of NN and UN or NN and AN led to a significant enhancement in tomato plant growth. Notably, the T12 (75%UN:25%NN) and T4 (75%NN:25%AN) treatments significantly increased the gas exchange and chlorophyll fluorescence parameters, thereby promoting the accumulation of photosynthetic products. The contents of fructose, glucose, and sucrose were significantly increased by 121.07%, 206.26%, and 94.64% and by 104.39%, 156.42%, and 61.40%, respectively, compared with those in the control. Additionally, AN favored starch accumulation, while NN and UN favored fructose, sucrose, and glucose accumulation. Gene expression related to nitrogen and sugar metabolism increased significantly in T12 and T4, with T12 showing greater upregulation. Key enzyme activity in metabolism also increased notably. In summary, T12 enhanced tomato growth by upregulating gene expression, increasing enzyme activity, and boosting photosynthesis and sugar accumulation. Growers should consider using NN and UN to reduce AN application in tomato fertilization. [ABSTRACT FROM AUTHOR]

Using non-accidental mortality records from 195 sites and 11 model simulations from the Coupled Model Intercomparison Project phase 6, we assessed future avoidable heat-related deaths associated with emission mitigation after accounting for population changes in China. Without human-induced climate change in the 21st century, China's heat-related mortality would drop by 48–72% (95% confidence interval (CI): 40–76%), especially at high latitudes and altitudes (up to 80%). Nationally, 15,576–87,612 (95% CI: 6605–106,736) heat-related deaths per year would potentially be avoided by reducing emissions. For most of China, temperature changes dominate the variation in heat-related deaths, while in Central and South China, population changes will have a greater influence. These not only reinforce the necessity of mitigating emissions but also suggest that adjusting population structure and spatial distribution could be applied in some areas to minimize warming impacts on public health. Notably, given that abatement will hardly bring immediate success within the next twenty years, more healthcare infrastructures are urgently needed to manage potential growing heat risks. Particularly in Northwest and South China, where future heat-related deaths would still be 110–140% of current levels (1995–2014) even without anthropogenic warming. We also found that without historical human-induced emissions, China's low level of technology (medical and economic) might increase current heat-related mortality by 57%, even though warming could be mitigated. Consequently, we believe that mitigating climate change risks also requires consideration of the economic/medical losses from lowering emissions. [ABSTRACT FROM AUTHOR]

Metallic cylinders are widely used in various fields of industrial production, and the automatic detection of surface microcracks is of great significance to the subsequent grinding process. In this paper, laser-excited surface acoustic waves (SAW) are used to detect surface microcracks. Due to the dispersion of SAWs on the cylinder surface, the SAWs exhibit different polarities at different positions. In order to improve the consistency of signals and the accuracy of the modeling, the angle at which the polarity is completely reversed is selected as the detection point. A laser ultrasonic automatic detection system is established to obtain signals, and the B-scan image is drawn to determine the location of the microcrack. By comparing the time–frequency diagrams of the reflected SAWs and transmitted SAWs, the transmitted wave is chosen to establish the microcrack depth prediction model. In addition, according to the trajectory of the grinding wheel, a prediction model based on the absolute depth of the microcracks is established, and the influence of the orientation of the microcracks on the signal energy is considered. The method proposed in this paper can provide a reference for the rapid grinding of microcracks on the surface of metallic cylinders; it has the characteristics of visualization and high efficiency, and overcomes the shortcomings of the currently used eddy current testing that provides information on the depth of microcracks with difficulty. [ABSTRACT FROM AUTHOR]

The Turpan Basin is one of the hottest regions in China, with high fire potential. The occurrence of gales could roll over trains as well as spread and expand the fire rapidly, posing adverse effects on traffic and fire protection. Therefore, it is important to discuss the frequency and mechanism of gales in Turpan. Based on the observational data of seven stations and ERA5 reanalysis data from 2015 to 2021, this study uses the t-mode principal component analysis using the oblique rotation (T-PCA) method to explore the seasonal differences and related synoptic patterns of gales in the Turpan Basin. The synoptic circulations are divided into nine categories. In types 1, 2, 3, 5, 7 and 9, there are a high-pressure center to the west and a lower-pressure center to the south of Turpan, while in types 4, 6 and 8, there is a strong high-pressure center to the south or northeast of Turpan. When the high-pressure system is located to the west of Turpan, gales are prone to occur, while to the south or northeast, gales seem to be less likely to occur, which is closely related to synoptic patterns and terrain. To the best of our knowledge, this study pioneered the frequency and mechanism of gales in Turpan, which could facilitate gale prevention in the area. [ABSTRACT FROM AUTHOR]

Organic aerosols are harmful to the environment because of their impact on air quality and visibility. They have serious effects not only on living beings and ecosystems because of their biological toxicity, but they also have an indirect effect on regional climate change as cloud condensation nuclei and radiation force. Many measures have been applied to decrease air pollution. Although the air quality has greatly improved, the standard of the World Health Organization (WHO) is far from being met at present. In this study, fine particulates were collected in Nanjing throughout 2019, and high-performance liquid chromatography–electrospray ion–mass spectrometry/mass spectrometry (HPLC-ESI-MS/MS) was carried out to determine 14 organic acids, 10 nitrated phenols, 1 aldehyde, and 1 ketone in aerosol samples. In this study, we further determined the changes in the pollutants in Nanjing in recent years compared to previous studies and characterized more kinds of species in the air. We found that different kinds of nitrated phenols showed similar trends of being abundant in winter and substituted in spring, autumn, and summer. 4-Nitrophenol was the most abundant species (2.83 ng m−3) among the nitrated phenols. p-Coumaric acid presented the highest level in summer with an average concentration of 1.55 ng m−3, indicating that grass burning was significant in summer, possibly due to wheat stalk and perennial ryegrass burning. The positive matrix fraction (PMF) model was applied to identify the sources of aerosols in Nanjing, including coal burning, grass burning, softwood burning, hardwood burning, anthropogenic secondary organic aerosols (SOAs), and biogenic SOAs. Coal burning and softwood burning contributed much more to the total determined species with values of 20.3% and 18.2%, respectively. Anthropogenic SOAs contributed 17.1%, and hardwood burning contributed 16.7%. The contribution of biogenic SOAs was 15%, and the grass-burning source contribution was the lowest, with 12.6%. With consideration of the large contribution from anthropogenic combustion activities, more strict measures are required to reduce emission pollutants in the future. [ABSTRACT FROM AUTHOR]