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The quantitative determination of wool and cashmere mixed fiber is an indispensable quality control link in the textile industry, crucial for improving international trade status, ensuring product quality, and safeguarding consumer rights. Therefore, the goal of this study is to develop a reliable method for estimating fiber contents in wool–cashmere blends based on near-infrared (NIR) spectroscopy. A total of 210 mixed samples of 21 different proportions of cashmere and wool are prepared in the experiment, and data are collected in the NIR spectral band of 1,000–2,500 nm. Convolution Savitzky–Golay (S–G) combined with the second-order derivative is then used for spectral preprocessing. The variable iterative space shrinkage approach (VISSA) optimizes the characteristic wavelengths, and 339 wavelength points are selected. The prediction model of the least squares support vector machine (LSSVM) is established by particle swarm optimization (PSO), fast positioning, and analysis of key information related to the target in complex spectral data. Finally, the training set and the prediction set are divided according to the ratio of 8 : 2. Experiments show that in terms of modeling and prediction, the PSO-LSSVM model based on the wavelength selected by VISSA has a prediction determination coefficient R-squared of 0.9821, a prediction root mean square error of 1.1263, and an mean absolute error of 0.6527. The hybrid modeling method of VISSA, PSO, and LSSVM based on NIR spectroscopy (VISSA–PSO–LSSVM) can provide a more accurate and stable method for the non-destructive detection of cashmere and wool blended fiber content.

Ambient energy harvesting has great potential to contribute to sustainable development and address growing environmental challenges. Converting waste energy from energy-intensive processes and systems (e.g. combustion engines and furnaces) is crucial to reducing their environmental impact and achieving net-zero emissions. Compact energy harvesters will also be key to powering the exponentially growing smart devices ecosystem that is part of the Internet of Things, thus enabling futuristic applications that can improve our quality of life (e.g. smart homes, smart cities, smart manufacturing, and smart healthcare). To achieve these goals, innovative materials are needed to efficiently convert ambient energy into electricity through various physical mechanisms, such as the photovoltaic effect, thermoelectricity, piezoelectricity, triboelectricity, and radiofrequency wireless power transfer. By bringing together the perspectives of experts in various types of energy harvesting materials, this Roadmap provides extensive insights into recent advances and present challenges in the field. Additionally, the Roadmap analyses the key performance metrics of these technologies in relation to their ultimate energy conversion limits. Building on these insights, the Roadmap outlines promising directions for future research to fully harness the potential of energy harvesting materials for green energy anytime, anywhere.

Objective To investigate the related influence factors of neonatal transfusion-associated necrotizing enterocolitis （TANEC）. Methods Clinical data of neonates with necrotizing enterocolitis （NEC） treated by surgery were retrospectively analyzed. All participants were divided into the TANEC and non-TANEC groups. Clinical characteristics， maternal conditions， laboratory test results， intraoperative conditions and clinical prognosis were subject to univariate and multivariate Logistic regression analyses. Related factors of TANEC were identified. The predictive values of these factors were determined by the receiver operating characteristic （ROC） curve and the area under the ROC curve （AUC）. Results A total of 51 infants were enrolled in this study， including 13 cases in the TANEC group and 38 in the non-TANEC group. Univariate analysis showed statistically significant differences in birth weight， Apgar-1 score， gestational age， number of transfusions， patent ductus arteriosus and hemoglobin level between two groups （all P < 0.05）. Multivariate Logistic regression analysis revealed that the number of transfusions and hemoglobin level were the related factors for the incidence of TANEC （both P < 0.05）. ROC curve verified that the AUC of the number of transfusions was 0.882 （95% CI 0.794-0.969）， 0.857 （95% CI 0.747-0.968） for hemoglobin level， and 0.907 （95% CI 0.822-0.992） for the two combined， respectively. Conclusion The number of blood transfusion and hemoglobin level are associated with the occurrence of TANEC， and the combination of these two can better predict the incidence of TANEC.

Refractory high-entropy alloys (RHEAs) have attracted increasing interest due to their excellent mechanical properties under extreme conditions. However, the lattice thermal conductivity is still not well studied. In this paper, we calculate the lattice thermal conductivity of the NbMoTaW RHEA using the equilibrium molecular dynamics method with a machine learning-based interatomic potential. We find that with increasing Mo concentration, the lattice thermal conductivity increased from 1.72 to 2.16 W/mK, an increase of 25.6%. The underlying mechanism is explained by the phonon density of states and phonon mode participation. Increasing the Mo concentration can induce a blueshift in both the low-frequency and high-frequency phonons. Moreover, we find that at the frequency corresponding to the phonon density of states peak, the NbMo1.5TaW RHEA has the largest mode participation rate, which is the main reason for the anomalous lattice thermal conductivity. In addition, we investigate the effect of temperature on the lattice thermal conductivity and the results show that anharmonicity has a dominant effect. Finally, the effect of compressive strain on the lattice thermal conductivity is explored. Our work discloses that the phonon density of states associated with phonon mode participation plays critical roles in the thermal conductivity of the RHEA, rather than the previously recognized conformational entropy. This contributes to the understanding of the thermal behavior of RHEA and provides an effective route to tune its thermal conductivity. [ABSTRACT FROM AUTHOR]