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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.

Alzheimer’s disease (AD) is one of the most common neurodegenerative diseases with a complex pathological mechanism, and as the incidence of AD has increased recently, there is an urgent need to develop more effective prevention and treatment methods. Many studies have shown that plant polyphenols have great potential in the prevention and treatment of neurodegenerative diseases, but their bioavailability is poor, limiting their practical applications. The application of nanotechnology can be helpful for the delivery of plant polyphenols. This article aims to elaborate recent progress and challenges in the development of nano-delivery systems for plant polyphenols, and review the common plant polyphenols used for AD treatment and their action mechanisms.

Aiming at the problems of high-frequency detail loss and structural distortion in images reconstructed by existing super-resolution (SR) reconstruction algorithms, a new reconstruction algorithm is proposed by combining multi-scale hybrid attention network. Firstly, a multi-scale residual module (MRM) was designed to extract features of different scale information and fuse them to obtain shallow features containing more information. Secondly, the residual hybrid attention module (RHAM) was used to enhance the network feature extraction ability along two different dimensions of channel and space, and adaptive feature optimization was carried out to improve the reuse of high-frequency features. Finally, the extracted features were enhanced through the reconstruction module to obtain corresponding high-resolution images. Tested on a benchmark data set, the experimental results show that the proposed algorithm outperforms mainstream image SR algorithms by an average of 0.104, 0.224, 0.146 dB in peak signal to noise ratio (PSNR), and 0.034 9, 0.027 6, and 0.018 1 in structural similarity index measure (SSIM) at magnifications of two, three, and four times. This algorithm can more effectively utilize the original image information, and the reconstructed image has certain improvements in edge and texture details.

Cashmere and wool are common raw materials in the textile industry. The clothes made of cashmere are popular because of the excellent comfort. A system that can quickly and automatically classify the two will improve the efficiency of fiber recognition in the textile industry. We propose a classification method of cashmere and wool fibers based on feature fusion using the maximum inter-class variance. First, the fiber target area is obtained by the preprocessing algorithm. Second, the features of sub-images are extracted through the algorithm of the Discrete Wavelet Transform. It is linearly fused by introducing the weight in the approximate and detailed features. The maximum separability of the feature data can be achieved by the maximum inter-class variance. Finally, different classifiers are used to evaluate the performance of the proposed method. The support vector machine classifier can achieve the highest recognition rate, with an accuracy of 95.20%. The experimental results show that the recognition rate of the fused feature vectors is improved by 6.73% compared to the original feature vectors describing the image. It verifies that the proposed method provides an effective solution for the automatic recognition of cashmere and wool.

Adaptive nulling antenna is the main means of anti-jamming for navigation system, and its anti-jamming performance is affected by many factors. Firstly, the influence of the nulling of adaptive antenna, the fluctuation of antenna pattern and the constellation distribution on the anti-jamming performance of adaptive nulling navigation system is analyzed qualitatively in this paper. Secondly, the anti-jamming performance of adaptive nulling navigation system in typical scenes is analyzed quantitatively by mathematical simulation. Finally, the field experiment of the GPS receiver with adaptive nulling antenna is designed. Combined with the constellation distribution in the experimental period and the carrier-to-noise ratio of adaptive nulling navigation receiver under different incident pitch angles, the anti-jamming performance of the receiver is analyzed. The results of the theoretical analysis are verified by field experiment, and some conclusions related to the factors such as adaptive nulling antenna null, antenna pattern and constellation distribution are obtained.

The extraction of mining resources, as well as processing processes such as ore beneficiation and smelting, generate large amounts of tailings that are difficult to directly utilize. Meanwhile, substantial filling materials are required for the voids formed after mining operations, and the environmental issues and safety hazards brought on by massive solid waste disposal cannot be ignored. By utilizing solid waste with alkaline and pozzolanic activity as the binder component and gold tailings as filler aggregate to prepare filler material to fill up the void areas, the purpose of waste treatment can be achieved. In this study, salt sludge, steel slag, ground granulated blast furnace slag, and gold tailings were used to prepare all-solid waste fluidized filling material for filling mine void areas, which not only solves the engineering safety problem of easy collapse of the mine airspace in the mining process but also ensures a backfill effect with high strength, which continuously guarantees the uninterrupted progress of the mining project. At the same time, the preparation of fluidized materials can consume a large amount of tailings and other solid waste, solving the problem of their stockpiling. The components of the solid wastes used are all general industrial solid wastes, so the preparation of the fluidized materials will not have an impact on the surrounding environment. The effects of binder ratios on the workability of the filling materials were investigated by means of the slump and slump flow tests. Combined with the unconfined compressive strength test, the change in backfill material strength with curing age and the water–binder ratio was studied. The experimental results showed that the slump and slump flow value of the filling material were positively correlated with the water–binder ratio. The water–binder ratio range satisfying a slump value of 180~260 mm and a slump flow value not less than 400 mm was 0.95~1.106. However, the strength decreased with the increase in the water–binder ratio, conforming to a hyperbolic relationship. The all-solid waste fluidized filling material had strengths not less than 0.22, 1.09, and 1.95 MPa at 3, 7, and 28 d, respectively, meeting the workability requirements. Finally, a method for determining the optimal range of water–binder ratio considering both workability performance and strength is proposed based on the relationship between slump value, slump flow value, unconfined compressive strength, and the water–binder ratio. [ABSTRACT FROM AUTHOR]

Cryptococcal meningitis (CM) is a well-recognized fungal infection, with substantial mortality in individuals infected with the human immunodeficiency virus (HIV). However, the incidence, risk factors, and outcomes in non-HIV adults remain poorly understood. This study aims to investigate the characteristics and prognostic indicators of CM in non-HIV adult patients, integrating a novel predictive model to guide clinical decision-making. A retrospective cohort of 64 non-HIV adult CM patients, including 51 patients from previous studies and 13 from the First Hospital of Shanxi Medical University, was analyzed. We assessed demographic features, underlying diseases, intracranial pressure, cerebrospinal fluid characteristics, and brain imaging. Using the least absolute shrinkage and selection operator (LASSO) method, and multivariate logistic regression, we identified significant variables and constructed a Nomogram prediction model. The model's calibration, discrimination, and clinical value were evaluated using the Bootstrap method, calibration curve, C index, goodness-of-fit test, receiver operating characteristic (ROC) analysis, and decision curve analysis. Age, brain imaging showing parenchymal involvement, meningeal and ventricular involvement, and previous use of immunosuppressive agents were identified as significant variables. The Nomogram prediction model displayed satisfactory performance with an akaike information criterion (AIC) value of 72.326, C index of 0.723 (0.592–0.854), and area under the curve (AUC) of 0.723, goodness-of-fit test P = 0.995. This study summarizes the clinical and imaging features of adult non-HIV CM and introduces a tailored Nomogram prediction model to aid in patient management. The identification of predictive factors and the development of the nomogram enhance our understanding and capacity to treat this patient population. The insights derived have potential clinical implications, contributing to personalized care and improved patient outcomes. [ABSTRACT FROM AUTHOR]

Traditional text representation methods have inaccurate expression of text information and high sparse dimensions. A word vector text representation model based on feature weights was thus proposed. The word vector was obtained through the Glove model, and then combined with the TF-IDF and N-Gram models, which not only considers the global information of the text, but also solves the problem of high sparse dimensionality in traditional representation methods. This can better capture the local information such as text semantics and word order and improve the ability of text feature expression. Finally, through the 20NewsGroup and 5AbstractsGroup tests, the classification accuracy rates reached 85.93% and 87.02%, respectively, verifying the effectiveness of the text representation model.

The accurate prediction of scallion prices can not only optimize supply chain management and help related practitioners and consumers to make more reasonable purchasing decisions, but also provide guidance for farmers' planting choices, thus enhancing market efficiency and promoting the sustainable development of the whole industry. This study adopts the idea of decomposition–denoising–aggregation, using three decomposition and denoising techniques combined with three single prediction models to form a base model. Various base models are divided into different combinations based on whether the computational structure is the same or not, and the optimal weights of the combinations are determined by using the improved particle swarm optimization–genetic algorithm (PSO-GA) optimization algorithm in different combinations. The experimental results show that the scallion price in Shandong Province from 2014 to 2023 shows an overall upward trend, and there is a cyclical and seasonal fluctuation pattern of "high in winter and low in summer"; the semi-heterogeneous-PSO-GA model reduces the MAPE by 49.03% and improves the directional accuracy by 41.52%, compared to the optimal single prediction model, ARIMA. In summary, the combined model has the most accurate prediction and strong robustness, which can provide ideas and references for the difficult problem of determining the optimal weights of the combined model in the field of predicting the prices of agricultural products. [ABSTRACT FROM AUTHOR]

It is very crucial to investigate key molecules that are involved in myelination to gain an understanding of brain development and injury. We have reported for the first time that pathogenic variants p.R477H and p.P505S in KARS, which encodes lysyl‐tRNA synthetase (LysRS), cause leukoencephalopathy with progressive cognitive impairment in humans. The role and action mechanisms of KARS in brain myelination during development are unknown. Here, we first generated Kars knock‐in mouse models through the CRISPR‐Cas9 system. Kars knock‐in mice displayed significant cognitive deficits. These mice also showed significantly reduced myelin density and content, as well as significantly decreased myelin thickness during development. In addition, Kars mutations significantly induced oligodendrocyte differentiation arrest and reduction in the brain white matter of mice. Mechanically, oligodendrocytes' significantly imbalanced expression of differentiation regulators and increased capase‐3‐mediated apoptosis were observed in the brain white matter of Kars knock‐in mice. Furthermore, Kars mutations significantly reduced the aminoacylation and steady‐state level of mitochondrial tRNALys and decreased the protein expression of subunits of oxidative phosphorylation complexes in the brain white matter. Kars knock‐in mice showed decreased activity of complex IV and significantly reduced ATP production and increased reactive oxygen species in the brain white matter. Significantly increased percentages of abnormal mitochondria and mitochondrion area were observed in the oligodendrocytes of Kars knock‐in mouse brain. Finally, melatonin (a mitochondrion protectant) significantly attenuated mitochondrion and oligodendrocyte deficiency in the brain white matter of KarsR504H/P532S mice. The mice treated with melatonin also showed significantly restored myelination and cognitive function. Our study first establishes Kars knock‐in mammal models of leukoencephalopathy and cognitive impairment and indicates important roles of KARS in the regulation of mitochondria, oligodendrocyte differentiation and survival, and myelination during brain development and application prospects of melatonin in KARS (or even aaRS)‐related diseases. [ABSTRACT FROM AUTHOR]

Simple Summary: In the present meta-analysis of randomized controlled trials, probiotic fermented milk supplementation appeared to be beneficial in lowering the levels of fasting plasma glucose, HbA1c, total cholesterol, and C-reactive protein. While these findings are encouraging, they should be interpreted cautiously, as considerable limitations of the included trials and analyses precluded solid conclusions. Modulating gut microbiota composition through probiotic administration has been proposed as a novel therapy for type 2 diabetes mellitus (T2DM), and fermented milk is arguably the most common and ideal probiotic carrier. The present meta-analysis was performed to assess the effects of probiotic fermented milk supplementation on glucose and lipid metabolism parameters and inflammatory markers in patients with T2DM using published data from randomized controlled trials (RCTs). The PubMed, Web of Science, and Cochrane Library databases were searched for relevant RCTs. A random-effects model was used to generate the weighted mean difference (WMD) and 95% confidence interval (95% CI). Probiotic fermented milk supplementation reduced the levels of fasting plasma glucose (MD = −17.01, 95% CI −26.43, −7.58 mg/dL; n = 7), hemoglobin A1c (MD = −0.47, 95% CI −0.74, −0.21%; n = 7), total cholesterol (MD = −5.15, 95% CI −9.52, −0.78 mg/dL; n = 7), and C-reactive protein (MD = −0.25, 95% CI −0.43, −0.08; n = 3) but did not significantly affect the levels of HOMA-IR (MD = −0.89, 95% CI −2.55, 0.78; n = 3), triglyceride (MD = −4.69, 95% CI −14.67, 5.30 mg/dL; n = 6), low-density lipoprotein cholesterol (MD = −4.25, 95% CI −8.63, 0.13 mg/dL; n = 7), high-density lipoprotein cholesterol (MD = 1.20, 95% CI −0.96, 3.36 mg/dL; n = 7), and tumor necrosis factor-alpha (MD: −0.58, 95% CI −1.47, 0.32 pg/mL; n = 2). In summary, the present findings provide a crude indication of the potential benefits of probiotic fermented milk supplementation in improving glucose and lipid metabolism and inflammation in patients with T2DM. However, more robust evidence is needed to determine the clinical significance of probiotic fermented milk in the management of T2DM. [ABSTRACT FROM AUTHOR]