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The accuracy of luminescence thermometry is seriously hindered by thermal-induced luminescence quenching as well as traditional single spectral parameter-based analytical methods, which rely on subjective experience of humans and fail to effectively utilize the spectral features. Herein, thermally intensified luminescence of Cr3+ is successfully achieved in Gd3Ga5O12:Yb3+–Er3+–Cr3+ under 980 nm laser excitation and about 10-fold enhancement is observed at 853 K compared with that at 303 K. The reabsorption of Er3+ luminescence by Cr3+ and the phonon-assisted energy transfer from Er3+ to Cr3+ are responsible for the Cr3+ luminescence, and the latter is the key to the enhanced luminescence, which guarantees good signal-to-noise ratio of emissions at high temperatures. A convolutional neural network (CNN) is subsequently proposed to extract thermal information from the UC (upconversion) emissions, and the maximum error is just about 0.63 K in the temperature range of 303–853 K, along with an average error of only 0.15 K, much better than those obtained with conventional ratiometric approaches. Additionally, luminescence thermometry driven by CNNs can effectively resist the interference of background light and ensure measurement accuracy, further demonstrating the excellent robustness of the proposed thermometry strategy. [ABSTRACT FROM AUTHOR]

Attribute reduction is a part of the most basic and significant research contents in rough set theory. The so-called attribute reduction is the smallest independent subset that keeps certain properties of information table unchanged. In this paper, four interval-valued intuitionistic fuzzy probabilistic rough set models and their natures are given on the basis of the dominant-, inferior- and interval-valued intuitionistic fuzzy probabilistic rough set models. At the same time, the interval-valued intuitionistic fuzzy numbers are transformed by fuzzy degree, and the approximate accuracy and approximate classification quality under the dominance relation are used for reduction, at last, the feasibility is verified by an example. [ABSTRACT FROM AUTHOR]

In this paper, we consider the weak approximate solutions to constrained vector optimization problems with variable ordering structures. In terms of abstract subdifferentials, normal cones and coderivatives, we establish Lagrange rules for this kind of solutions. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is one of the most common diseases causing cognitive impairment in middle-aged and elderly people, and the high cost of the disease poses a challenge for health systems to cope with the expected increasing number of cases in the future. With the advance of aging of the society, China has the largest number of Alzheimer's disease patients in the world. Therefore, how to diagnose Alzheimer's disease early and accurately and intervene positively is an urgent problem. In this paper, the improved MultiRes + UNet network is used to effectively segment the brain tissue in the preprocessing. This method expands the convolutional field by null convolution to integrate the global information, mitigates the differences between encoder–decoder features by using MultiRes block and Res path structure, greatly reducing the memory requirement, and improving its accuracy, applicability, and robustness. The non-local means the attention model is introduced to make the mapped organization categories free from noise interference. In the classification problem, this paper adopts the improved VoxCNN network model for binary classification of AD, EMCI, LMCI, and NC. Experiments showed that the model classification performance and the accuracy rate improved significantly with the combined effect of the improved MultiRes + UNet network and VoxCNN network, the binary classification accuracy was 98.35% for AD vs. NC, 89.46% for AD vs. LMCI, 83.95% for LMCI vs. EMCI, and 88.27% for EMCI vs. NC. [ABSTRACT FROM AUTHOR]

With the development and maturity of nanofabrication technology, many nanostructures with specific electromagnetic characteristics and optical functions have been designed and demonstrated. Artificial materials with these special optical properties are known as metamaterials. In this paper, a metamaterial lens is proposed and characterized. After using the space transformation approach based on the coordinate transformation principles, the lens becomes anisotropic and inhomogeneous, thus realizing the regulation of electromagnetic waves. The electromagnetic wave transformation characteristics of the superlens are numerically analysed by irradiating a plane transverse wave with a wavelength of 300 nm onto a rectangular slab. The results show that the waveform of the incident light changes from plane waves to cylindrical after passing through the superlens. The validity of the superlens is verified by finite element method calculations. [ABSTRACT FROM AUTHOR]

In this paper, the notions of fully stable well-posedness and fully stable minimum of optimization problems with an extended real-valued objective function in an Asplund space setting are considered, where the objective function undergoes both tilt perturbations and general parameter perturbations. Special cases of the first notion reduce to the stable Hölder minimizers by Zheng and Ng [SIAM J Optim. 2015;25:416–438] and the fully stable Hölder minimizers introduced by Zheng, Zhu and Ng [SIAM J Optim. 2018;28:2601–2624], respectively. By using techniques of variational analysis and generalized differentiation, the results presented in this paper provide insight into necessary or sufficient conditions for tilt-stable minimizers of nonlinear programmes in Asplund spaces and therefore generalize some existing ones in the recent literature. [ABSTRACT FROM AUTHOR]

• A novel NIR application for determination of alcohols-diesel is presented. • 1D NIR spectra are encoded into GAF images. • GAF image coding can enhance NIR spectral features through bidirectional mapping. • 5 models are compared for qualitative and quantitative analysis of alcohols-diesel. • The proposed model achieves better performances than other models. Alcohols blended with diesel, as a renewable and clean substitute fuel of diesel engine, is a potential solution to alleviate the scarcity of fossil fuels and the worsening environmental pollution in transportation and industry. In this study, a novel near infrared (NIR) application for determination of alcohols-diesel is presented. Using the strategy of combining NIR spectroscopy with gramian angular field (GAF) image coding and deep convolution neural network (CNN), the proposed approach successfully realized the qualitative classification of different diesel (methanol diesel, ethanol diesel and pure diesel) and alcohols content detection despite the spectra were highly similar and collinear. To further verify the practical performance of the proposed method, it was briefly compared with 1DCNN, support vector machine (SVM) and BP neural network based on the identical data. The proposed GAF-CNN method can accurately distinguish diesel, methanol diesel and ethanol diesel. In addition, satisfactory results have been achieved with the smallest MSE and MAE and the highest R2 in the quantitative detection of methanol and ethanol content. This study explores the conversion of 1D spectra into 2D images through the mathematical of GAF, which not only opens up a new perspective for more intuitive display of spectral features, but also makes it possible to introduce the powerful advantages of deep learning image processing into the field of NIR analysis. The proposed method provides a new idea for the intelligent determination of alcohols-diesel. [ABSTRACT FROM AUTHOR]

Sustainable environmental policies and energy crises have led to a trend of blending different alcohols into diesel to partly replace the decreasing fossil fuels. To improve the rapidity and accuracy of determining alcohols exist in methanol and ethanol diesel, optimal chemical factors (OCF) feature selection schemes were presented based on different near infrared (NIR) characteristic absorption bands generated by different chemical structure information utilizing support vector machine (SVM). Through comparative analysis with SVM based on entire spectra, Monte Carlo uninformative variable elimination (MC-UVE) spectra and competitive adaptive reweighted sampling (CARS) spectra, the proposed OCF-SVM not only achieved 100 % accuracy, precision, recall and F 1 -score in classification, but also exhibited the best performance in prediction analysis with the smallest sum of squares due to error (SSE), root mean squared error (RMSE), mean absolute percentage error (MAPE) and the highest R-square. The overall outcomes indicate that the OCF method based on molecular chemical structures can select more pertinent and interpretable spectral features, thereby making the classification and prediction of alcohol-based diesels more exact and credible. Further, the developed OCF strategy together with SVM could supplement existing methods for analysis of alcohol-based diesels and is expected to be recommended for detecting the chemical composition of other fuels., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Introduction: The hemodynamic effects of withholding vs. continuing angiotensin II receptor blockers (ARBs) before surgery in elderly patients undergoing spinal surgery in a prone position during anesthesia induction to skin incision are still unknown., Methods: In this prospective study, 80 patients undergoing spinal surgery in a prone position with general anesthesia, aged 60-79 years, American Society of Anesthesiologists (ASA) II or III, were enrolled. Patients who had ARBs only in their preoperative medication list were randomly divided into two groups at a 1:1 ratio: In Group A, ARBs were continued on the morning of surgery, while in Group B, they were withhold. Norepinephrine was infused to maintain the blood pressure at the baseline level of ±20% during anesthesia induction in all patients. The primary outcome was the consumption of norepinephrine in each group from anesthesia induction to skin incision. The secondary outcomes include changes in invasive arterial blood pressure and heart rate, the fluid infusion volumes, the amounts of anesthetic drugs, and the total time from induction to skin incision., Results: There were no significant differences in the demographics, the fluid infusion volumes, the amounts of anesthetic drugs, the total time from induction to skin incision, and hemodynamics at different time points ( p  > 0.05), while significant differences were found in norepinephrine consumption between the two groups ( p  < 0.001). Compared with Group B, the consumption of norepinephrine increased significantly in Group A (93.3 ± 29.8 vs. 124.1 ± 38.7 μg, p  = 0.000). In addition, the same trend was illustrated in the pumping rate of norepinephrine between Group B (0.04 ± 0.01 μg·kg -1 ·min -1 ) and Group A (0.06 ± 0.02 μg·kg -1 ·min -1 ) ( p  = 0.004)., Conclusion: Our study conducted in elderly patients with hypotension undergoing prone spinal surgery demonstrated a greater pumping rate of norepinephrine during anesthesia induction in patients with ARBs continuing before surgery than those withholding, indicating that it was more difficult to maintain hemodynamic stability. Clinical Trial Registration : https://www.chictr.org.cn/showproj.html?proj=141081, ChiCTR2100053583., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Yuan, Xu, Hu, Ma, Meng, Ren and Wen.)

Luminescence thermometry is a promising non-contact temperature measurement technique, but improving the precision and reliability of this method remains a challenge. Herein, we propose a thermal sensing strategy based on a machine learning. By using Gd 3 Ga 5 O 12 : Er 3+ -Yb 3+ as the sensing medium, a support vector machine (SVM) is preliminarily adopted to establish the relationship between temperature and upconversion emission spectra, and the sensing properties are discussed through the comparison with luminescence intensity ratio (LIR) and multiple linear regression (MLR) methods. Within a wide operating temperature range (303-853 K), the maximum and the mean measurement errors actualized by the SVM are just about 0.38 and 0.12 K, respectively, much better than the other two methods (3.75 and 1.37 K for LIR and 1.82 and 0.43 K for MLR). Besides, the luminescence thermometry driven by the SVM presents a high robustness, although the spectral profiles are distorted by the interferences within the testing environment, where, however, LIR and MLR approaches become ineffective. Results demonstrate that the SVM would be a powerful tool to be applied on the luminescence thermometry for achieving a high sensing performance.