Explore the vast range of titles available.

To improve the high-temperature strength and decrease the density of NbTaWMo alloys, addition of light element Si producing the second-phase silicide is employed. Refractory NbTaWMoSix (x = 0, 0.25, 0.5, 0.75) high-entropy alloys are produced by spark plasma sintering. The phase evolution, microstructure, compressive mechanical properties, and high-temperature hardness are investigated in this study. It reveals that there is only a disordered body-centered cubic (BCC) phase in the matrix NbTaWMo alloy. After adding the Si element, NbTaWMoSix alloys demonstrate the presence of multiphase structure: disordered BCC phase and silicide phase. As the content of Si is increased, the proportion of silicide is increased, which improves the hardness and strength. The addition of Si at certain concentration (x = 0.25 and 0.5) has positive effect on ductility of the alloys. The alloys all demonstrate brittle fracture due to the brittle phase of BCC and silicide phase. The high-temperature hardness of NbTaWMoSix alloys is increased with the addition of Si, but the same alloy presents a slightly decreasing phenomenon as the temperature improves.

In this paper, graphene reinforced aluminum matrix composites are successfully prepared by high-energy ball milling. The results show that no graphene agglomeration is found in the mixed powder. The complex composites prepared by high energy ball milling and powder metallurgy have approximately 4–5 layers of graphene and the thickness of single-layer graphene is approximately 0.334 nm. The final experimental results confirm the formation of compound AlC 3 in the microstructure, and its diffraction spot index is ( 2 ¯ 00), ( 1 ¯ 1 1 ¯ ) and (11 1 ¯ ). The maximum friction coefficient is 0.126, and the average friction coefficient is 0.027, suggesting good wear resistance and corrosion resistance. Additionally, the friction corrosion mechanism of the material is deeply analyzed. The results of strengthening mechanism analysis show that the main strengthening mechanism of the materials designed in this experiment is thermal mismatch strengthening. It can be concluded that the yield strength of the material calculated by the modified model is 227.75 MPa. This value is slightly lower than the calculated value of the general shear lag model (237.68 MPa). However, it is closer to the yield strength value of the actual material (211 MPa).

Increased nitrogen deposition (N factor) and changes in precipitation patterns (W factor) can greatly impact soil microbial communities in tropical/subtropical forests. Although knowledge about the effects of a single factor on soil microbial communities is growing rapidly, little is understood about the interactive effects of these two environmental change factors. In this study, we investigated the responses of soil bacterial and fungal communities to the short-term simulated environmental changes (nitrogen addition, precipitation seasonality change, and their combination) in a subtropical forest in South China. The interaction between N and W factors was detected significant for affecting some soil physicochemical properties (such as pH, soil water, and NO3- contents). Fungi were more susceptible to treatment than bacteria in a variety of community traits (alpha, beta diversity, and network topological features). The N and W factors act antagonistically to affect fungal alpha diversity, and the interaction effect was detected significant for the dry season. The topological features of the meta-community (containing both bacteria and fungi) network overrode the alpha and beta diversity of bacterial or fungal communities in explaining the variation of soil enzyme activities. The associations between Ascomycota fungi and Gammaproteobacteria or Alphaproteobacteria might be important in mediating the inter-kingdom interactions. In summary, our results suggested that fungal communities were more sensitive to N and W factors (and their interaction) than bacterial communities, and the treatments’ effects were more prominent in the dry season, which may have great consequences in soil processes and ecosystem functions in subtropical forests.

This study investigated the optical properties (OPs) and Monte Carlo (MC) simulations of light propagation in Healthy Group (HG) and Blackhearted Group (BG) potatoes. The MC simulation of light propagation indicated that both the photon packet weight and the penetration depth were significantly lower in blackhearted tissues than in healthy tissues. The simulation revealed deeper light penetration in healthy tissues than in the blackhearted tissues, approximately 6.73 mm at 805 nm, whereas the penetration depth in blackhearted tissues was much shallower (1.30 mm at 805 nm). Additionally, the simulated absorption energy at both 490 nm and 805 nm was higher in blackhearted tissues, suggesting that these wavelengths effectively detect blackheart in potatoes. The absorption (μa) and reduced scattering (μ’s) coefficients were obtained using Vis-NIR spectroscopy, which represented a notable increase in μa in BH tissues, particularly around 550–850 nm, and an increase in μ’s across the Vis-NIR region. Based on transmittance (Tt), μa and μ’s, Support Vector Machine Discriminant Analysis (SVM-DA) models demonstrated exceptional performance, achieving 95.83–100.00% accuracy in Cross-Validation sets, thereby confirming the robustness and reliability of the optical features for accurate blackheart detection. These findings provide valuable theoretical insights into the accuracy and robustness of predictive models for detecting blackhearted potatoes.

With the increase in the number of Global Navigation Satellite System (GNSS) satellites and their operating frequencies, richer observation data are provided for the tightly coupled Global Navigation Satellite System/Inertial Navigation System (GNSS/INS). In this paper, we propose an efficient and robust combined navigation scheme to address the key issues of system accuracy, robustness, and computational efficiency. The tightly combined system fuses multi-source data such as the pseudo-range, the pseudo-range rate, and dual-antenna observations from the GNSS and the horizontal attitude angle from the vertical gyro (VG) in order to realize robust navigation in a sparse satellite observation environment. In addition, to cope with the high computational load faced by the system when the satellite observation conditions are good, we propose a weighted quasi-optimal satellite selection algorithm that reduces the computational burden of the navigation system by screening the observable satellites while ensuring the accuracy of the observation data. Finally, we comprehensively evaluate the proposed system through simulation experiments. The results show that, compared with the loosely coupled navigation system, our system has a significant improvement in state estimation accuracy and still provides reliable attitude estimation in regions with poor satellite observation conditions. In addition, in comparison experiments with the optimal satellite selection algorithm, our proposed satellite selection algorithm demonstrates greater advantages in terms of computational efficiency and engineering practicability.

Cyanobacteria are photosynthetic prokaryotic organisms which represent a significant source of novel, bioactive, secondary metabolites, and they are also considered an abundant source of bioactive compounds/drugs, such as dolastatin, cryptophycin 1, curacin toyocamycin, phytoalexin, cyanovirin-N and phycocyanin. Some of these compounds have displayed promising results in successful Phase I, II, III and IV clinical trials. Additionally, the cyanobacterial compounds applied to medical research have demonstrated an exciting future with great potential to be developed into new medicines. Most of these compounds have exhibited strong pharmacological activities, including neurotoxicity, cytotoxicity and antiviral activity against HCMV, HSV-1, HHV-6 and HIV-1, so these metabolites could be promising candidates for COVID-19 treatment. Therefore, the effective large-scale production of natural marine products through synthesis is important for resolving the existing issues associated with chemical isolation, including small yields, and may be necessary to better investigate their biological activities. Herein, we highlight the total synthesized and stereochemical determinations of the cyanobacterial bioactive compounds. Furthermore, this review primarily focuses on the biotechnological applications of cyanobacteria, including applications as cosmetics, food supplements, and the nanobiotechnological applications of cyanobacterial bioactive compounds in potential medicinal applications for various human diseases are discussed.

A super-resolution (SR) reconstruction of remote sensing images is becoming a highly active area of research. With increasing upscaling factors, richer and more abundant details can progressively be obtained. However, in comparison with natural images, the complex spatial distribution of remote sensing data increases the difficulty in its reconstruction. Furthermore, most SR reconstruction methods suffer from low feature information utilization and equal processing of all spatial regions of an image. To improve the performance of SR reconstruction of remote sensing images, this paper proposes a deep convolutional neural network (DCNN)-based approach, named the deep residual dual-attention network (DRDAN), which achieves the fusion of global and local information. Specifically, we have developed a residual dual-attention block (RDAB) as a building block in DRDAN. In the RDAB, we firstly use the local multi-level fusion module to fully extract and deeply fuse the features of the different convolution layers. This module can facilitate the flow of information in the network. After this, a dual-attention mechanism (DAM), which includes both a channel attention mechanism and a spatial attention mechanism, enables the network to adaptively allocate more attention to regions carrying high-frequency information. Extensive experiments indicate that the DRDAN outperforms other comparable DCNN-based approaches in both objective evaluation indexes and subjective visual quality.

Fast nondestructive detection technology in food quality and safety evaluation is a powerful support tool that fosters informatization and intelligence in the food industry, characterized by its rapid processing, convenient operation, and seamless online inspection [...]

It is crucial for the efficacy of the apple storage to apply methods like electronic nose systems for detection and prediction of spoilage or infection by Penicillium expansum. Based on the acquisition of electronic nose signals, selected sensitive feature sensors of spoilage apple and all sensors were analyzed and compared by the recognition effect. Principal component analysis (PCA), principle component analysis-discriminant analysis (PCA-DA), linear discriminant analysis (LDA), partial least squares discriminate analysis (PLS-DA) and K-nearest neighbor (KNN) were used to establish the classification model of apple with different degrees of corruption. PCA-DA has the best prediction, the accuracy of training set and prediction set was 100% and 97.22%, respectively. synergy interval (SI), genetic algorithm (GA) and competitive adaptive reweighted sampling (CARS) are three selection methods used to accurately and quickly extract appropriate feature variables, while constructing a PLS model to predict plaque area. Among them, the PLS model with unique variables was optimized by CARS method, and the best prediction result of the area of the rotten apple was obtained. The best results are as follows: Rc = 0.953, root mean square error of calibration (RMSEC) = 1.28, Rp = 0.972, root mean square error of prediction (RMSEP) = 1.01. The results demonstrated that the electronic nose has a potential application in the classification of rotten apples and the quantitative detection of spoilage area.

Improvement of the sense of gain as an internal driving force is the key factor to improve the training quality of graduate students in food science. Utilizing Jiangsu University graduate students majoring in food science as research samples, this study analyzed the present situation of the “sense of gain” demand. We analyzed the reasonable appeals of graduate students during their study based on fully respecting and advocating students’ right of speech, listened to their opinions and suggestions on higher education, analyzed the main contradictions, and further put forward a series of countermeasures. For improving the graduate students’ sense of gain during the period of study, it is necessary to improve the training quality from the following five aspects: constructing high-quality courses, cultivating people’s responsibilities, implementing “soft elimination” of training links, carrying out diversified extracurricular activities, and developing comprehensive quality. This research is significant in improving the training quality of food science graduate students.