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The rocket sled, as a ground dynamic test system, combines the characteristics of the wind tunnel test and the flight test. However, some practical factors, such as shock wave interference, ground effect, and high-intensity aerodynamic noise will cause serious interference and even failure of the uniformly distributed sensors during horizontal sliding in a wide speed range. The AGARD HB-2 standard model is employed as the payload to simulate the aerodynamic and aeroacoustic characteristics during the variable acceleration period, aiming to optimize the test sensors layout. It is observed that in the high Mach number flow fields, strong coupling behaviors among complex waves will occur. The peak of wake vortex strength will appear at 1.5 s and gradually diminish over time. In addition, when the vortex between the load and the booster is monitored, its position shifts forward in the subsonic stage, then gradually moves backward and expands in the supersonic stage. Acoustic directivity is pronounced at subsonic and transonic speeds, pointing towards 75° and 135° relative to the sliding speed, respectively. These results can provide technical support for sensor layout and high-precision testing in rocket sled tests. [ABSTRACT FROM AUTHOR]

In this paper, we study the dynamic meal delivery routing problem (MDRP) with time-sensitive customers. The multi-objective MDRP optimization model is developed to maximize customer satisfaction and minimize delay penalty cost and riding cost. To solve the dynamic MDRP, a novel waiting strategy is proposed to divide the dynamic problem into a series of static subproblems. This waiting strategy utilizes the decision threshold to determine rerouting points based on the number of dynamic meal orders. Meanwhile, time-sensitive priority is introduced to accelerate assignment and routing decisions for orders from customers with high time sensitivity. For each static subproblem, a hybrid AGA–ALNS algorithm that incorporates the adaptive genetic algorithm and adaptive large neighborhood search is developed to improve both the global and local search capabilities of the genetic algorithm. We validate the performance of the proposed waiting strategy and the AGA–ALNS algorithm through numerical instances. In addition, managerial insights are obtained from sensitivity analysis experiments. [ABSTRACT FROM AUTHOR]

The escalation of jellyfish stings has drawn attention to severe skin reactions, underscoring the necessity for novel treatments. This investigation assesses the potential of hydroxybenzoic acid derivatives, specifically protocatechuic acid (PCA) and gentisic acid (DHB), for alleviating Nemopilema nomurai Nematocyst Venom (NnNV)-induced injuries. By employing an in vivo mouse model, the study delves into the therapeutic efficacy of these compounds. Through a combination of ELISA and Western blot analyses, histological examinations, and molecular assays, the study scrutinizes the inflammatory response, assesses skin damage and repair mechanisms, and investigates the compounds' ability to counteract venom effects. Our findings indicate that PCA and DHB significantly mitigate inflammation by modulating critical cytokines and pathways, altering collagen ratios through topical application, and enhancing VEGF and bFGF levels. Furthermore, both compounds demonstrate potential in neutralizing NnNV toxicity by inhibiting metalloproteinases and phospholipase-A2, showcasing the viability of small-molecule compounds in managing toxin-induced injuries. [ABSTRACT FROM AUTHOR]

High temperatures have adverse effects on the yield and quality of vegetables. Bok choy, a popular vegetable, shows varying resistance to heat. However, the mechanism underlying the thermotolerance of bok choy remains unclear. In this study, 26 bok choy varieties were identified in screening as being heat-resistant at the seedling stage; at 43 °C, it was possible to observe obvious heat damage in different bok choy varieties. The physiological and biochemical reactions of a heat-tolerant cultivar, Jinmei (J7), and a heat-sensitive cultivar, Sanyueman (S16), were analyzed in terms of the growth index, peroxide, and photosynthetic parameters. The results show that Jinmei has lower relative conductivity, lower peroxide content, and higher total antioxidant capacity after heat stress. We performed transcriptome analysis of the two bok choy varieties under heat stress and normal temperatures. Under heat stress, some key genes involved in sulfur metabolism, glutathione metabolism, and the ribosome pathway were found to be significantly upregulated in the heat-tolerant cultivar. The key genes of each pathway were screened according to their fold-change values. In terms of sulfur metabolism, genes related to protease activity were significantly upregulated. Glutathione synthetase (GSH2) in the glutathione metabolism pathway and the L3e, L23, and S19 genes in the ribosomal pathway were significantly upregulated in heat-stressed cultivars. These results suggest that the total antioxidant capacity and heat injury repair capacity are higher in Jinmei than in the heat-sensitive variety, which might be related to the specific upregulation of genes in certain metabolic pathways after heat stress. [ABSTRACT FROM AUTHOR]

With the continuous warming of the global climate, expanding the use of renewable energy has become one of the main social responsibilities. However, as the number of installed wind turbines and their physical dimensions continue to increase, the issue of generated noise has become increasingly significant in influencing the acceptance and endorsement of wind power projects by neighboring communities. In this paper, we investigated the noise generated by two wind turbine units with rated powers of 1.5 MW and 4.5 MW and analyzed the variations in low-frequency noise during their operation and shutdown periods. This research shows that the power of a single unit has a significant impact on the low-frequency noise emitted into the environment. Compared with 1.5 MW wind turbines, 4.5 MW wind turbines generate more low-frequency noise when operating at the same wind speed. Further analysis of the narrowband frequency spectra and one-third octave spectra of the measured noise indicates that the low-frequency noise from the 4.5 MW wind turbine increases significantly in the range of approximately 80 Hz to 300 Hz, with more pronounced variations below 250 Hz corresponding to changes in wind speed. However, the overall variations in low-frequency noise with wind speed are not as notable as those observed for the 1.5 MW wind turbine. Due to the relatively weak attenuation of low-frequency noise in the atmosphere, the higher low-frequency content of large wind turbines may cause more distress to residents near wind farms. The result of this study emphasizes that in the planning and design of wind power projects, in addition to considering the efficiency of single-unit power generation and the contribution of renewable energy, it is also necessary to pay full attention to noise emission issues to ensure that the project is widely supported and accepted in the community. [ABSTRACT FROM AUTHOR]

To solve various reworking and repair problems caused by unqualified bearing product quality inspections, this paper introduces a green re-entrant scheduling optimization method for bearing production shops considering job reworking. By taking into account quality inspection constraints, this paper establishes an integrated scheduling mathematical model based on the entire processing–transportation–assembly process of bearing production shops with the goals for minimizing the makespan, total carbon emissions, and waste emissions. To solve these problems, the concepts of the set of the longest common machine routes (SLCMR) and the set of the shortest recombination machine combinations (SSRMC) were used to propose the re-entrant scheduling optimization method, based on system reconfiguration, to enhance the system stability and production scheduling efficiency. Then, a multi-objective hybrid optimization algorithm, based on a neighborhood local search (MOOA-LS), is proposed to improve the search scope and optimization ability by constructing a multi-level neighborhood search structure. Finally, this paper takes a bearing production shop as an example to carry out the case study and designs a series of experimental analyses and comparative tests. The final results show that in the bearing production process, the proposed model and algorithm can effectively realize green and energy-saving re-entrant manufacturing scheduling. [ABSTRACT FROM AUTHOR]

Angelica sinensis (Oliv.) Diels, a member of the Umbelliferae family, is commonly known as Danggui (Angelica sinensis, AS). AS has the functions of blood tonic, menstrual pain relief, and laxatives. Accurate classification of AS grades is crucial for efficient market management and consumer health. The commonly used method to classify AS grades depends on the evaluator's observation and experience. However, this method has issues such as unquantifiable parameters and inconsistent identification results among different evaluators, resulting in a relatively chaotic classification of AS in the market. To address these issues, this study introduced a computer vision-based approach to intelligently grade AS. Images of AS at five grades were acquired, denoised, and segmented, followed by extraction of shape, color, and texture features. Thirteen feature parameters were selected based on difference and correlation analysis, including tail area, whole body area, head diameter, G average, B average, R variances, G variances, B variances, R skewness, G skewness, B skewness, S average, and V average, which exhibited significant differences and correlated with grades. These parameters were then used to train and test both the traditional back propagation neural network (BPNN) and the BPNN model improved with a growing optimizer (GOBPNN). Results showed that the GOBPNN model achieved significantly higher average testing precision, recall, F-score, and accuracy (97.1%, 95.9%, 96.5%, and 95.0%, respectively) compared to the BPNN model. The method combining machine vision technology with GOBPNN enabled efficient, objective, rapid, non-destructive, and cost effective AS grading. [ABSTRACT FROM AUTHOR]

This paper presents the design and development of a new type of planetary traction drive bearing-type reducer. In this design, the transmission outer ring is replaced with an elastic ring. The design constructs a circular arc at the axial end of the rolling body's contour line. This ensures that the contact point of this arc with the reducer's outer ring and the inner ring's axial end face is maintained on the radial traction contact line. As a result, it can replace the thrust bearing and provide an axial support function. It has the advantages of simple structure, easy processing, smooth transmission, and low noise. This paper first introduces the design and development process of this bearing-type reducer and presents systematic research on its transmission principle and dynamics. Subsequently, in response to the edge effect phenomenon of the outer ring contact line, the contour line of the outer ring is refined by adopting the shaping method used for bearing rollers, establishing a full circular arc profile shaping method, which significantly improves its edge effect. Finally, in our investigations, combined with experimental tests, a prototype of the bearing-type reducer was fabricated, and the speed ratio, torque, and transmission efficiency of the reducer were studied. The results demonstrate that the bearing-type reducer can achieve high transmission accuracy and efficiency. The transmission performance varies significantly under different lubrication conditions, with the peak efficiency reaching as high as 99.97% when using Santotrac 50 traction oil. The results verify the feasibility of the proposed design method and have the potential to be applied in wheel hub motors and robot joints. [ABSTRACT FROM AUTHOR]

Natural products derived from medicinal plants offer convenience and therapeutic potential and have inspired the development of antimicrobial agents. Thus, it is worth exploring the combination of nanotechnology and natural products. In this study, silver nanoparticles (AgNPs) were synthesized from the leaf extract of Ginkgo biloba (Gb), having abundant flavonoid compounds. The reaction conditions and the colloidal stability were assessed using ultraviolet–visible spectroscopy. X-ray diffraction, transmission electron microscopy, and Fourier transform infrared spectroscopy (FTIR) were used to characterize the AgNPs. AgNPs exhibited a spherical morphology, uniform dispersion, and diameter ranging from ~8 to 9 nm. The FTIR data indicated that phytoconstituents, such as polyphenols, flavonoids, and terpenoids, could potentially serve as reducing and capping agents. The antibacterial activity of the synthesized AgNPs was assessed using broth dilution and agar well diffusion assays. The results demonstrate antibacterial effects against both Gram-positive and Gram-negative strains at low AgNP concentrations. The cytotoxicity of AgNPs was examined in vitro using the CCK-8 method, which showed that low concentrations of AgNPs are noncytotoxic to normal cells and promote cell growth. In conclusion, an environmentally friendly approach for synthesizing AgNPs from Gb leaves yielded antibacterial AgNPs with minimal toxicity, holding promise for future applications in the field of biomedicine. [ABSTRACT FROM AUTHOR]

Identifying influential nodes, with pivotal roles in practical domains like epidemic management, social information dissemination optimization, and transportation network security enhancement, is a critical research focus in complex network analysis. Researchers have long strived for rapid and precise identification approaches for these influential nodes that are significantly shaping network structures and functions. The recently developed SPON (sum of proportion of neighbors) method integrates information from the three-hop neighborhood of each node, proving more efficient and accurate in identifying influential nodes than traditional methods. However, SPON overlooks the heterogeneity of neighbor information, derived from the asymmetry properties of natural networks, leading to its lower accuracy in identifying essential nodes. To sustain the efficiency of the SPON method pertaining to the local method, as opposed to global approaches, we propose an improved local approach, called the SSPN (sum of the structural proportion of neighbors), adapted from the SPON method. The SSPN method classifies neighbors based on the h-index values of nodes, emphasizing the diversity of asymmetric neighbor structure information by considering the local clustering coefficient and addressing the accuracy limitations of the SPON method. To test the performance of the SSPN, we conducted simulation experiments on six real networks using the Susceptible–Infected–Removed (SIR) model. Our method demonstrates superior monotonicity, ranking accuracy, and robustness compared to seven benchmarks. These findings are valuable for developing effective methods to discover and safeguard influential nodes within complex networked systems. [ABSTRACT FROM AUTHOR]

Exogenous nitrogen and carbon can affect plant cell walls, which are composed of structural carbon. Sucrose synthase (SUS), invertase (INV), hexokinase (HXK), phosphoglucomutase (PGM), and UDP-glucose pyrophosphorylase (UGP) are the key enzymes of sucrose metabolism involved in cell wall synthesis. To understand whether these genes are regulated by carbon and nitrogen to participate in structural carbon biosynthesis, we performed genome-wide identification, analyzed their expression patterns under different carbon and nitrogen treatments, and conducted preliminary functional verification. Different concentrations of nitrogen and carbon were applied to poplar (Populus trichocarpa Torr. and Gray), which caused changes in cellulose, lignin, and hemicellulose contents. In poplar, 6 SUSs, 20 INVs, 6 HXKs, 4 PGMs, and 2 UGPs were identified. Moreover, the physicochemical properties, collinearity, and tissue specificity were analyzed. The correlation analysis showed that the expression levels of PtrSUS3/5, PtrNINV1/2/3/5/12, PtrCWINV3, PtrVINV2, PtrHXK5/6, PtrPGM1/2, and PtrUGP1 were positively correlated with the cellulose content. Meanwhile, the knockout of PtrNINV12 significantly reduced the cellulose content. This study could lay the foundation for revealing the functions of SUSs, INVs, HXKs, PGMs, and UGPs, which affected structural carbon synthesis regulated by nitrogen and carbon, proving that PtrNINV12 is involved in cell wall synthesis. [ABSTRACT FROM AUTHOR]

The low ionic conductivity, narrow electrochemical window, poor interfacial stability with lithium metal, and non-degradability of raw materials are the main problems of solid polymer electrolytes, restricting the development of lithium solid-state batteries. In this paper, a biodegradable poly (2,3-butanediol/1,3-propanediol/succinic acid/sebacic acid/itaconic acid) ester was designed and used as a substrate to prepare biodegradable polyester solid polymer electrolytes for solid-state lithium batteries using a simple solution-casting method. A large number of ester-based polar groups in the amorphous polymer become a high-speed channel for carrying lithium ions as a weak coordination site. The biodegradable polyester solid polymer electrolyte exhibits a wide electrochemical window of 5.08 V (vs. Li/Li+), high ionic conductivity of 1.03 mS cm−1 (25 °C), and a large Li+ transference number of 0.56. The electrolyte exhibits good interfacial stability with lithium, with stable Li plating/stripping behavior at room temperature over 2100 h. This design strategy for biodegradable polyester solid polymer electrolytes offers new possibilities for the development of matrix materials for environmentally friendly lithium metal solid-state batteries. [ABSTRACT FROM AUTHOR]

As an advanced interaction mode, gestures have been widely used for human–computer interaction (HCI). This paper proposes a multi-objective optimization method based on the objective function J C P to solve the inconsistency between the gesture comfort J C S and measurement precision J P H in the gesture interaction. The proposed comfort model C S takes seventeen muscles and six degrees of freedom into consideration based on the data from muscles and joints, and is capable of simulating the energy expenditure of the gesture motion. The C S can provide an intuitive indicator to predict which act has the higher risk of fatigue or injury for joints and muscles. The measurement precision model ∆ P H is calculated from the measurement error (∆ X H , ∆ Y H , ∆ Z H) caused by calibration, that provides a means to evaluate the efficiency of the gesture interaction. The modeling and simulation are implemented to analyze the effectiveness of the multi-objective optimization method proposed in this paper. According to the result of the comparison between the objective function J C S , based on the comfort model C S , and the objective function J P H , based on the measurement precision models ∆ P H , the consistency and the difference can be found due to the variation of the radius r B _ R H O and the center coordinates P B _ R H O x B _ R H O , y B _ R H O , z B _ R H O . The proposed objective function J C P compromises the inconsistency between the objective function J C S and J P H . Therefore, the multi-objective optimization method proposed in this paper is applied to the gesture design to improve the ergonomics and operation efficiency of the gesture, and the effectiveness is verified through usability testing. [ABSTRACT FROM AUTHOR]

Vibration isolation and wave manipulation play a significant role in helicopter applications, from small elastic devices for information processing to large solid structures for vibration control. The emergence of topological elastic metamaterials has opened up new possibilities for modulating vibrations and waves in solids. However, most designs are limited to laboratory settings due to complex manufacturing or low structural strength. In this study, we propose a topological elastic metamaterial with a stamping triangle whose orientation θ can be rotated to achieve topological phase transitions between −30° and 30°. Unlike traditional manufacturing methods, the utilization of stamping technology provides a cost-effective means of production, allowing for wider application across diverse industries. Moreover, the metamaterial has the capability to manipulate elastic waves along specific trajectories and effectively decrease common vibrations (more than 50 dB), typically occurring at a frequency of 24 Hz in helicopters. Our approach also enables the control of elastic wave propagation along interfaces through the emergence of topological edge states. The practical implementation of this design will enhance the confidence in using topological elastic metamaterials for vibration isolation and wave manipulation in helicopters. [ABSTRACT FROM AUTHOR]

Aims: Microbial residue deposition is considered an important part of soil carbon sequestration. However, there is still a lack of understanding of the link between tree species composition and diversity and microbial carbon deposition, which hampers the rational selection and allocation of tree species for artificial carbon sequestration afforestation in northern China. Methods: In this study, plots from temperate planting forests (>60 years) were examined for the importance values of tree species, mycorrhizal types, tree diversity, and soil properties. Soil amino sugar was used as the biomarker to indicate the accumulation of fungi- and bacteria-derived carbon. Results: We found that tree species diversity and the importance values of tree species and mycorrhizal types were significantly positively correlated with soil microbial residual carbon. Hierarchical partitioning modeling showed that three groups of variables significantly affected soil microbial residual carbon, accounting for a total of 26.75% of the variation. Among them, tree species diversity accounted for the largest proportion (11.5%), and tree species diversity and importance values had a high joint impact (9.74%). The importance values of all AM-associated species constituted one of the most significant individual factors and could independently account for 10.9% of the variation in microbial residues. The findings of piecewise structural equation modeling showed that the importance of tree species had a large direct impact on GluN, GalN, and the GluN/MurN ratio. By influencing soil properties, the importance values of tree species also had indirect effects on soil microbial residual carbon. Conclusions: We suggest that an increase in the importance values of AM-associated tree species, such as Acer negundo L., will be accompanied by an increase in the total importance value of AM-associated tree species, which can significantly increase soil microbial residual carbon. [ABSTRACT FROM AUTHOR]

The accurate identification of the origin of Chinese medicinal materials is crucial for the orderly management of the market and clinical drug usage. In this study, a deep learning-based algorithm combined with machine vision was developed to automatically identify the origin of Angelica sinensis (A. sinensis) from eight areas including 1859 samples. The effects of different datasets, learning rates, solver algorithms, training epochs and batch sizes on the performance of the deep learning model were evaluated. The optimized hyperparameters of the model were the dataset 4, learning rate of 0.001, solver algorithm of rmsprop, training epochs of 6, and batch sizes of 20, which showed the highest accuracy in the training process. Compared to support vector machine (SVM), K-nearest neighbors (KNN) and decision tree, the deep learning-based algorithm could significantly improve the prediction performance and show better robustness and generalization performance. The deep learning-based model achieved the highest accuracy, precision, recall rate and F1_Score values, which were 99.55%, 99.41%, 99.49% and 99.44%, respectively. These results showed that deep learning combined with machine vision can effectively identify the origin of A. sinensis. [ABSTRACT FROM AUTHOR]

Photocatalytic elimination of antibiotics from the environment and drinking water is of great significance for human health. However, the efficiency of photoremoval of antibiotics such as tetracycline is severely limited by the prompt recombination of electron holes and slow charge migration efficacy. Fabrication of low-dimensional heterojunction composites is an efficient method for shortening charge carrier migration distance and enhancing charge transfer efficiency. Herein, 2D/2D mesoporous WO3/CeO2 laminated Z-scheme heterojunctions were successfully prepared using a two-step hydrothermal process. The mesoporous structure of the composites was proved by nitrogen sorption isotherms, in which sorption-desorption hysteresis was observed. The intimate contact and charge transfer mechanism between WO3 nanoplates and CeO2 nanosheets was investigated using high-resolution transmission electron microscopy and X-ray photoelectron spectroscopy measurements, respectively. Photocatalytic tetracycline degradation efficiency was noticeably promoted by the formation of 2D/2D laminated heterojunctions. The improved photocatalytic activity could be attributed to the formation of Z-scheme laminated heterostructure and 2D morphology favoring spatial charge separation, confirmed by various characterizations. The optimized 5WO3/CeO2 (5 wt.% WO3) composites can degrade more than 99% of tetracycline in 80 min, achieving a peak TC photodegradation efficiency of 0.0482 min−1, which is approximately 3.4 times that of pristine CeO2. A Z-scheme mechanism is proposed for photocatalytic tetracycline by from WO3/CeO2 Z-scheme laminated heterojunctions based on the experimental results. [ABSTRACT FROM AUTHOR]

Multidrug resistance (Mdr) proteins are critical proteins for maintenance of drug resistance in fungi. Mdr1 has been extensively studied in Candida albicans; its role in other fungi is largely unknown. In this study, we identified a homologous protein of Mdr (AoMdr1) in the nematode-trapping (NT) fungus Arthrobotrys oligospora. It was found that the deletion of Aomdr1 resulted in a significant reduction in the number of hyphal septa and nuclei as well as increased sensitivity to fluconazole and resistance to hyperosmotic stress and SDS. The deletion of Aomdr1 also led to a remarkable increase in the numbers of traps and mycelial loops in the traps. Notably, AoMdr1 was able to regulate mycelial fusion under low-nutrient conditions, but not under nutrient-rich conditions. AoMdr1 was also involved in secondary metabolism, and its deletion caused an increase in arthrobotrisins (specific compounds produced by NT fungi). These results suggest that AoMdr1 plays a crucial role in the fluconazole resistance, mycelial fusion, conidiation, trap formation, and secondary metabolism of A. oligospora. Our study contributes to the understanding of the critical role of Mdr proteins in mycelial growth and the development of NT fungi. [ABSTRACT FROM AUTHOR]

Downed woody debris (DWD) can alter the chemical and biological properties of forest soils, and this process is affected by the degree of DWD decay. Our aim was to assess the effects of the entire decay process of DWD on the associated soil microbial communities. Here, we examined the differences in soil microbial community size and composition among five decay stages (DC Ⅰ–Ⅴ) of Masson pine (Pinus massoniana Lamb.) at two soil depths in a climate transitional forest and then linked these differences to soil chemical properties. The decay of DWD increased soil total PLFAs, and the complexity of soil microbial networks was highest in stage Ⅱ and then decreased with the decay stage. The relative abundance of fungi increased with the decay stage, while the relative abundance of gram-positive bacteria decreased with the decay stage. The microbial community composition at a soil depth of 0–10 cm was mainly driven by soil pH and organic C (SOC), and at a depth of 10–20 cm, soil pH was the primary determinant of microbial community composition. Our findings suggest that DWD decomposition reduces microbial physiological stress, promotes fungal growth, and stimulates microbial biomass, highlighting the positive impact of DWD on forest soils. Future research is needed to elucidate the universal effects of DWD decomposition on soil properties, especially focusing on the response of soil microbial processes to the decomposition time of DWD. [ABSTRACT FROM AUTHOR]

Simple Summary: Blue oat mite species mainly include Penthaleus major, P. falcatus, P. tectus, and P. minor. Among them, P. major, P. falcatus and P. tectus are important pest mites on gramineous crops, which often occur in low-temperature and high-humidity environments. These three mites are difficult to distinguish in the field due to their small size and similar shape. Furthermore, they exhibit different resistance to chemical pesticides, making it challenging to control their outbreak. Worryingly, we only saw reports of frequent occurrence of P. major in winter wheat regions in China, and no other species of blue oat mites were found. Understanding the distribution pattern, genetic diversity, and genetic differentiation among populations of blue oat mite species in winter wheat regions of China is essential for developing effective control programs. In this study, we evaluated the distribution of blue oat mites in the major wheat regions in China and assessed the level of genetic diversity and genetic structure of blue oat mites from winter wheat regions of China using mitochondrial cytochrome c oxidase I (COI) sequences. Blue oat mite species, including Penthaleus major and P. tectus, are pests widely distributed across China that cause damage to winter wheat. This study evaluated the genetic diversity of P. major and P. tectus on Triticum hosts collected from 23 geographic locations based on mitochondrial cytochrome c oxidase subunit I (COI) sequences. We identified nine haplotypes in 438 P. major individuals from 21 geographic locations and five haplotypes in 139 P. tectus individuals from 11 geographic locations. Meanwhile, P. major exhibits high values of haplotype diversity (Hd) and nucleotide diversity (Pi) (Hd = 0.534 > 0.5 and Pi = 0.012 > 0.005), representing a large stable population with a long evolutionary history. P. tectus shows low values of Hd and Pi (Hd = 0.112 < 0.5 and Pi = 0 < 0.005), which suggest recent founder events. Moreover, demographic analysis suggested that P. major and P. tectus have not undergone a recent population expansion. The lowest genetic variation was observed in Xiangzhou (XZ-HB), Zaoyang (ZY-HB), Siyang (SY-JS), and Rongxian (RX-SC), with only one species and one haplotype identified in over 30 individuals. Robust genetic differentiation was found in P. major compared to P. tectus, which provides a theoretical basis for the widespread distribution of P. major in China. [ABSTRACT FROM AUTHOR]

Malate dehydrogenase (MDH) is a key enzyme in the tricarboxylic acid (TCA) cycle and is essential for energy balance, growth, and tolerance to cold and salt stresses in plants. However, the role of MDH in filamentous fungi is still largely unknown. In this study, we characterized an ortholog of MDH (AoMae1) in a representative nematode-trapping (NT) fungus Arthrobotrys oligospora via gene disruption, phenotypic analysis, and nontargeted metabolomics. We found that the loss of Aomae1 led to a weakening of MDH activity and ATP content, a remarkable decrease in conidia yield, and a considerable increase in the number of traps and mycelial loops. In addition, the absence of Aomae1 also caused an obvious reduction in the number of septa and nuclei. In particular, AoMae1 regulates hyphal fusion under low nutrient conditions but not in nutrient-rich conditions, and the volumes and sizes of the lipid droplets dynamically changed during trap formation and nematode predation. AoMae1 is also involved in the regulation of secondary metabolites such as arthrobotrisins. These results suggest that Aomae1 has an important role in hyphal fusion, sporulation, energy production, trap formation, and pathogenicity in A. oligospora. Our results enhance the understanding of the crucial role that enzymes involved in the TCA cycle play in the growth, development, and pathogenicity of NT fungi. [ABSTRACT FROM AUTHOR]

As one of the world's largest ecological rehabilitation programs, the three-north (Northern China, Northeastern China, and Northwestern China) shelterbelts program in China were not well evaluated on its effects on multiple soil properties. This paper aims to quantify this. Seven hundred twenty soils from paired plots of farmlands and neighbor shelterbelts were sampled from six regions of Songnen Plain in northeastern China. Multivariate analysis of variance and regression analysis were used to detect the impacts of shelterbelt plantations. For the overall 1 m soil profiles, shelterbelt plantations had a 4.3% and 7.4% decreases in soil bulk density and soil moisture (p = 0.000), a 4.8% increase in soil porosity (p = 0.003). It also evidently recovered soil fertility with a 40% increase in total P, a 4.4% increase in total K, and a 15.1% increase in available K (p < 0.05). However, without overall changes were in SOC and N (p > 0.05). Compared with farmland, shelterbelt plantations produced a 7.8% SOC increase in 20–40 cm soil and much more minor changes in surface soil (0–20 cm). Compared with the younger plantation, mature shelterbelts tended to sequestrate more SOC in soils (from a 0.11% decrease to a 3.31% increase) and recover total K from a 2.24% decline to a 16.5% increase. Correlation analysis manifested that there is a significant relationship between SOC sequestration and the changes in bulk density, porosity, soil moisture, pH, EC, total N, total P, and alkaline hydrolyzed N. In contrast, the strongest relationship was observed between total N and SOC (r > 0.50, p < 0.001). The increase in total N was accompanied by 1.01–1.67-fold higher SOC sequestration in deep soils >20 cm in poplar forests. Our results highlight that the over-40-year shelterbelts afforestation on farmland in northeastern China could strongly affect soil physics, soil water, and nutrient of P and K. The effects on SOC sequestration were dependent on soil depths, growth stages, and regions. Our data support the precise soil evaluation of agroforestry projects in the black soil region in the high-latitude northern hemisphere. [ABSTRACT FROM AUTHOR]

Deep learning models have been widely used in data-driven bridge structural damage diagnosis methods in recent years. However, these methods require training and test datasets to satisfy the same distribution, which is difficult to satisfy in practice. Domain adaptation transfer learning is an efficient method to solve this problem. Most of the current domain adaptation methods focus on close-set scenarios with the same classes in the source and target domains. However, in practical applications, new damage caused by long-term degradation often makes the target and source domains dissimilar in the class space. For such challenging open-set scenarios, existing domain adaptation methods will be powerless. To effectively solve the above problems, an adversarial auxiliary weighted subdomain adaptation algorithm is proposed for open-set scenarios. Adversarial learning is introduced to proposed an adversarial auxiliary weighting scheme to reflect the similarity of target samples with source classes. It effectively distinguishes unknown damage from known states. This paper further proposes a multi-channel multi-kernel weighted local maximum mean discrepancy metric (MCMK-WLMMD) to capture the fine-grained transferable information for conditional distribution alignment (sub-domain alignment). Extensive experiments on transfer tasks between three bridges verify the effectiveness of the algorithm in open-set scenarios. [ABSTRACT FROM AUTHOR]

The rapid development of the aviation industry has put forward higher and higher requirements for material properties, and the research on smart material structure has also received widespread attention. Smart materials (e.g., piezoelectric materials, shape memory materials, and giant magnetostrictive materials) have unique physical properties and excellent integration properties, and they perform well as sensors or actuators in the aviation industry, providing a solid material foundation for various intelligent applications in the aviation industry. As a popular smart material, piezoelectric materials have a large number of application research in structural health monitoring, energy harvest, vibration and noise control, damage control, and other fields. As a unique material with deformation ability, shape memory materials have their own outstanding performance in the field of shape control, low-shock release, vibration control, and impact absorption. At the same time, as a material to assist other structures, it also has important applications in the fields of sealing connection and structural self-healing. Giant magnetostrictive material is a representative advanced material, which has unique application advantages in guided wave monitoring, vibration control, energy harvest, and other directions. In addition, giant magnetostrictive materials themselves have high-resolution output, and there are many studies in the direction of high-precision actuators. Some smart materials are summarized and discussed in the above application directions, aiming at providing a reference for the initial development of follow-up related research. [ABSTRACT FROM AUTHOR]

Late embryogenesis abundant (LEA) proteins are important developmental proteins in the response of plants to abiotic stress. In our previous study, BcLEA73 was differentially expressed under low-temperature stress. Herein, we combined bioinformatics analysis, subcellular localization, expression assays, and stress experiments (including salt, drought, and osmotic stress) to identify and analyze the BcLEA gene family. Gene cloning and functional analysis of BcLEA73 were performed in tobacco and Arabidopsis. Based on the sequence homology and the available conservative motif, 82 BrLEA gene family members were identified and were divided into eight subfamilies in the genome-wide database of Chinese cabbage. The analysis showed that the BrLEA73 gene was located on chromosome A09 and belonged to the LEA_6 subfamily. Quantitative real-time PCR analysis indicated that the BcLEA genes were differentially expressed to varying degrees in the roots, stems, leaves, and petioles of Wucai. The overexpressed BcLEA73 transgenic plants exhibited no significant differences in root length and seed germination rates compared to the wild-type (WT) plants under control conditions. Under salt and osmotic stress treatment, the root length and seed germination rates of the BcLEA73-OE strain were significantly greater than those of WT plants. Under salt stress, the total antioxidant capacity (T-AOC) of the BcLEA73-OE lines increased significantly, and the relative conductivity, (REL), hydrogen peroxide (H2O2) content, and superoxide anion (O2−) production rate decreased significantly. Under drought treatment, the survival rate of the BcLEA73-OE lines was significantly higher than that of WT plants. These results showed that the BcLEA73 gene of Wucai functions in enhancing the tolerance of plants to salt, drought, and osmotic stress. This study provides a theoretical basis to explore the relevant functions of the BcLEA gene family members of Wucai. [ABSTRACT FROM AUTHOR]

Mycotoxin contamination in food poses health hazards to humans. Current methods of controlling mycotoxins still have limitations and more effective approaches are needed. During the past decades of years, variable environmental factors have been tested for their influence on mycotoxin production leading to elucidation of a complex regulatory network involved in mycotoxin biosynthesis. These regulators are putative targets for screening molecules that could inhibit mycotoxin synthesis. Here, we summarize the regulatory mechanisms of hierarchical regulators, including pathway-specific regulators, global regulators and epigenetic regulators, on the production of the most critical mycotoxins (aflatoxins, patulin, citrinin, trichothecenes and fumonisins). Future studies on regulation of mycotoxins will provide valuable knowledge for exploring novel methods to inhibit mycotoxin biosynthesis in a more efficient way. [ABSTRACT FROM AUTHOR]

The applications of sensors in the aerospace industry are mostly concentrated in the middle and high frequencies, and low-frequency sensors often face the problems of low power and short working bandwidth. A lightweight, thin, high-power, low-frequency broadband transducer based on giant magnetostrictive material is designed. The design and optimization processes of the core components are introduced and analyzed emphatically. The finite element simulation results are validated by the PSV-100 laser vibration meter. Three basic configurations of the work panel are proposed, and the optimal configuration is determined by modal, acoustic, and vibration coupling analyses. Compared with the original configuration, it is found that the lowest resonant frequency of the optimal configuration is reduced by 24.6% and the highest resonant frequency within 2000 Hz is 1744.9 Hz, which is 54.2% higher than that of the original configuration. This greatly improves the vibration power and operating frequency range of the transducer. Then, the honeycomb structure is innovatively applied to the work panel, and it is verified that the honeycomb structure has a great effect on the vibration performance of the work panel. By optimizing the size of the honeycomb structure, it is determined that the honeycomb structure can improve the vibration power of the work panel to its maximum value when the distance between the half-opposite sides of the hexagon is H = 3.5 mm. It can reduce the resonant frequency of the work panel; the lowest resonant frequency is reduced by 12.8%. At the same time, the application of a honeycomb panel structure can reduce the weight of the transducer. [ABSTRACT FROM AUTHOR]

"Lvwen stone" is a yellow-green carbonate jade gemstone. In this study, the gemological characteristics and color genesis of Lvwen stone were investigated using conventional gemological testing methods and analytical techniques, such as X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), ultraviolet–visible spectroscopy (UV–VIS), laser ablation plasma mass spectrometry (LA-ICP-MS), and scanning electron microscopy (SEM). The chemical composition of Lvwen stone is mainly Ca, with lesser amounts of Mg, Mn, Cu, Zn, Fe, and other trace elements. The rare earth element distribution pattern indicates that Lvwen stone is characterized by MREE depletion and a positive Ce anomaly. The mineralogic composition of Lvwen stone is calcite, and trace-element- and crystal-size-induced colors result in its characteristic banded appearance. The white (or light green) bands consist of comparatively coarse calcite crystals (~100 μm) that are oriented perpendicularly to the band plane, accounting for their poor light transmittance. In contrast, the dark green matrix is composed of cryptocrystalline calcite crystals that are uniform in size (~10 μm) and tightly packed, resulting in superior light transmittance. Lvwen stone has a 6A1→4E(4D)d-d intra-ion electronic transition absorption band of Fe3+ at ~380–450 nm and a 2E→2T2(2D)d-d intra-ion electronic transition absorption band of Cu2+ at ~580–780 nm. This indicates that both the intra-ion electronic transitions of Fe3+ and Cu2+ give rise to the unique yellow-green color of the material. Lvwen stone is produced by ultra-high-pressure tectonic fluids in a relatively closed, reducing environment, and the green matrix was formed earlier than the white bands. [ABSTRACT FROM AUTHOR]

Epithelial ovarian cancer (EOC) is the main cause of mortality among gynecological malignancies worldwide. Although patients with EOC undergo aggregate treatment, the prognosis is often poor. Peritoneal malignant ascites is a distinguishable clinical feature in EOC patients and plays a pivotal role in tumor progression and recurrence. The mechanisms of the tumor microenvironment (TME) in ascites in the regulation of tumor progression need to be explored. We comprehensively analyzed the transcriptomes of 4680 single cells from five EOC patients (three diagnostic samples and two recurrent samples) derived from Gene Expression Omnibus (GEO) databases. Batch effects between different samples were removed using an unsupervised deep embedding single-cell cluster algorithm. Subcluster analysis identified the different phenotypes of cells. The transition of a malignant cell state was confirmed using pseudotime analysis. The landscape of TME in malignant ascites was profiled during EOC progression. The transformation of epithelial cancer cells into mesenchymal cells was observed to lead to the emergence of related anti-chemotherapy and immune escape phenotypes. We found the activation of multiple biological pathways with the transition of tumor-associated macrophages and fibroblasts, and we identified the infiltration of CD4+CD25+ T regulatory cells in recurrent samples. The cell adhesion molecules mediated by integrin might be associated with the formation of the tumorsphere. Our study provides novel insights into the remodeling of the TME heterogeneity in malignant ascites during EOC progression, which provides evidence for identifying novel therapeutic targets and promotes the development of ovarian cancer treatment. [ABSTRACT FROM AUTHOR]

Network intrusion detection has the problems of large amounts of data, numerous attributes, and different levels of importance for each attribute in detection. However, in random forests, the detection results have large deviations due to the random selection of attributes. Therefore, aiming at the current problems, considering increasing the probability of essential features being selected, a network intrusion detection model based on three-way selected random forest (IDTSRF) is proposed, which integrates three decision branches and random forest. Firstly, according to the characteristics of attributes, it is proposed to evaluate the importance of attributes by combining decision boundary entropy, and using three decision rules to divide attributes; secondly, to keep the randomness of attributes, three attribute random selection rules based on attribute randomness are established, and a certain number of attributes are randomly selected from three candidate fields according to conditions; finally, the training sample set is formed by using autonomous sampling method to select samples and combining three randomly selected attribute sets randomly, and multiple decision trees are trained to form a random forest. The experimental results show that the model has high precision and recall. [ABSTRACT FROM AUTHOR]

Deep learning has been widely used in various fields because of its accuracy and efficiency. At present, the improvement of image semantic segmentation accuracy has become the area of most concern. In terms of increasing accuracy, improved semantic segmentation models have attracted more attention. In this paper, a hybrid model is proposed to solve the problems of edge splitting and small objects disappearing from complex scene images. The hybrid model consists of three parts: (1) an improved HED network, (2) an improved PSP-Net, (3) an AFF attention mechanism. Continuous edges can be obtained by combining the improved HED network with an improved PSP-Net. The AFF attention mechanism can improve the segmentation effect of small target objects by enhancing its response recognition ability for specific semantic scenes. The experiments were carried out on Cityspaces, SIFT Flow, NYU-V2 and CamVid datasets, and the experimental results show that the segmentation accuracy of our method is improved by 2% for small target objects, and by 3% for scenes with complex object edges. [ABSTRACT FROM AUTHOR]

Flax fibre has been used to reinforce concrete composite, but its dynamic properties have not been thoroughly studied. This study investigates the dynamic splitting tensile properties of plain concrete (PC) confined by flax-fibre-reinforced polymer (FFRP) and glass-fibre-reinforced polymer (GFRP). The dynamic splitting tensile tests were carried out on PC, FFRP-PC, and GFRP-PC cylinder specimens by the high-speed servo-hydraulic machine, with the impact-induced strain rates ranging from 0.1 to 58 s−1. The effect of the FRP confinement, FRP thickness and strain rate on the dynamic splitting tensile behaviour were assessed. The results indicated that similar confinement effectiveness of FFRP and GFRP is observed. The dynamic tensile strength of 1- and 2-layer FFRP-PC increased by 29% and 67%, and the one- and two-layer GFRP-PC increased by 32% and 84%, respectively. FFRP-PC and GFRP-PC cylinders showed less sensitivity to the strain rate compared with PC. The empirical relationship between the tensile DIF and strain rate for PC, FFRP-PC and GFRP-PC was proposed based on experimental data. The proposed model was developed to predict the dynamic splitting tensile strength. The results suggested the potential of FFRP composites applied into concrete structures under extreme dynamic loadings. [ABSTRACT FROM AUTHOR]

This study aimed to determine the distribution, stability, and soil organic carbon (SOC) of aggregates, and the contribution of soil aggregate proportion, stability index, and aggregate-associated SOC to the total SOC. Three hundred and sixty soil samples were gathered from shelterbelts and neighboring farmlands in five layers of 1 m profiles in Songnen Plain, northeastern China. The shelterbelt plantations were found to increase by 69.5% and 103.8% in >2 mm and 0.25–2 mm soil aggregates, respectively, and their R0.25, mean weight diameter (MWD), and geometric mean diameter (GMD) were enhanced by 96.3%, 33.2%, and 40.0%, respectively, compared to those of farmlands in soil layers at 0–20 cm depth (p < 0.05). The total SOC content increased by 13.3% at 0–20 cm soil depth, and the SOC content and stock in >2 mm aggregates increased by 21.5% and 18.7% in the 20–40 cm layer (p < 0.05), respectively. The SOC content and stock in total soil had a significantly positive relationship with the proportion of >2 mm soil aggregates and a negative relationship with the value of fractal dimension (D). The enhancement in the SOC of the total soil was dependent on the increase in aggregate-associated SOC, with larger-particle aggregates having a greater contribution. Based on the study results, afforestation improved soil stability and the structure of soil aggregates, and SOC accumulation in the total soil was not only governed by SOC concentration and stock within the aggregate size class, but also the proportion of >2 mm soil aggregates and the value of the fractal dimension. [ABSTRACT FROM AUTHOR]

The development of flow pattern identification technology for gas–liquid two-phase flow in porous media is of great significance to engineering research and production. In this paper, a high accuracy identification method for two-phase flow pattern in porous media is proposed with Machine learning techniques. The gas–liquid two-phase flow patterns and corresponding differential pressure signals in porous beds with particle diameters of 1.5 mm, 3 mm, and 6 mm are obtained through visual experiments. Three time domain characteristic parameters (Mean, Standard deviation, and Range) are calculated by a statistical method, while the EMD energy spectrum of the signal is obtained by empirical mode decomposition. Based on these parameters, machine learning technology, including support vector machine (SVM) and BP neural network, are employed to identify the flow pattern. Four flow pattern identification models are trained based on SVM and BP neural network, with accuracies of 94.77%, 93.4%, 96.08%, and 91.5%. Furthermore, the three models with good performance are integrated by integrated learning technology. An integrated identification model of gas–liquid two-phase flow pattern in porous media with an overall accuracy of 98.04% is finally obtained. [ABSTRACT FROM AUTHOR]

In the operation and maintenance of the South–North Water Transfer Project, monitoring and predicting the canal slope deformation quickly and efficiently is one of the urgent problems to be solved. To predict the slope deformation of the deep excavated canal section at the head of the canal. We propose a new idea of adopting the joint prediction of MT-InSAR and Fbprophet. Firstly, MT-InSAR monitoring technology was used to invert channel deformation using 88 Sentinel-1A orbit-raising image data with a time baseline from 2017 to 2019. The time-series deformation of nine monitoring points was also extracted, and it was found that the time-series curves of the cumulative deformation of the channel slope showed fluctuations. The Fbprophet algorithm was then used to train the prediction model in Python to predict the channel slope deformation over the next 365 days. Finally, the prediction results were compared with the MT-InSAR monitoring values to analyze the prediction accuracy and applicability of the Fbprophet algorithm for the slope deformation monitoring of the South–North Water Transfer Project. The results show that: the deformation rate of the slope of the deep excavation section is in the range of 10 mm/a to 25 mm/a, the maximum accumulated deformation is about 60 mm, and the slope of the excavation canal shows a lifting phenomenon; among the nine monitoring points, the minimum and maximum predicted values of deformation using the machine learning prediction model trained in this paper were 56 mm and 73 mm, respectively; comparing the predicted and monitored values, their correlation coefficients were 0.998 at the highest and 0.988 at the lowest, and the minimum and maximum values of RMSE (RootMean Square Error) were 0.72 mm and 2.87 mm, respectively. It shows that the prediction model trained by the Fbprophet algorithm in this paper applies to the prediction of slope deformation in the deep excavation section, and our prediction results can provide a data reference for disaster prevention and the sustainable development of the South–North Water Transfer Project. [ABSTRACT FROM AUTHOR]

With global warming and the depletion of fossil energy sources, renewable energy is gradually replacing non-renewable energy as the main energy in the future. As one of the fastest growing renewable energy sources, the safety and reliability of wind energy have been paid more and more attention. The size of modern wind turbines is becoming larger and larger. As the main component of wind turbines to capture energy, the blade is often damaged by various complex environments and irregular loads. Therefore, the health monitoring and damage identification of wind turbine blades have become a main research focus. At present, in addition to the overview of various detection methods of wind turbine blades, there is a lack of comprehensive classifications and overviews of the main damage types, damage-generation mechanisms, and basic principles of the damage-detection technology of wind turbine blades. In this paper, firstly, the common fault types of wind turbine blades, such as trailing edge cracking, lightning strike, leading edge corrosion pollution, icing, and delamination, as well as their generation mechanism, are comprehensively analyzed. Then, the basic principles and the latest research progress of the current main detection technologies, such as vision, ultrasonic, thermal imaging, vibration, acoustic emission, and so on, are comprehensively reviewed. The advantages and limitations of the various detection technologies for practical application are summarized. Finally, through a comparative analysis of the various damage-detection technologies, we try to find potential future research directions, and draw conclusions. This paper will provide a reference for understanding the mechanism behind the main damage types and the damage-detection methods of wind turbine blades. It has important reference value for further promoting practical research of wind turbine blade damage-detection technology and grasping this research direction. [ABSTRACT FROM AUTHOR]

In order to improve the operation performance of the multi-stage double-suction centrifugal pump and reduce the internal energy loss of the pump, this paper proposes a single-objective optimization design method based on the non-hierarchical response surface methodology (RSM) and the multi-island genetic algorithm (MIGA). Nine parameters, such as the blade outlet width and blade wrap angle, were used as design variables, and the optimization objective was efficiency under design conditions. In total, 149 sets of valid data were obtained under the Latin hypercube sampling method (LHS), the corresponding thresholds were set for efficiency and head, and 99 sets of valid data were obtained. A cross-validation analysis of the sieved data was carried out based on non-hierarchical RSM, global optimization of the efficiency was carried out using MIGA, and numerical verification was carried out via CFD. The research results show that compared with hierarchical RSM, non-hierarchical RSM can approximate the nonlinear relationship between the objective function and the design variables with higher accuracy, and the model fitting R2 value was 0.919. The efficiency was improved by 3.717% after optimization. The overall prewhirl of the impeller inlet after optimization decreased, the internal speed of the volute significantly improved, the large-area vortex at the volute and the outlet pipe was eliminated, the impact loss at the volute separating tongue disappeared, and the overall hydraulic performance of the pump was improved. The total entropy output value of the optimized pump was reduced by 4.79 (W/K), mainly concentrated on the reduction in the entropy output value of the double volute, and the overall energy dissipation of the pump was reduced. [ABSTRACT FROM AUTHOR]

Surgery is a compelling application field for collaborative control robots. This paper proposes a gesture recognition method applied to a medical assistant robot delivering instruments to collaborate with surgeons to complete surgeries. The key to assisting the surgeon in passing instruments in the operating room is the ability to recognize the surgeon's hand gestures accurately and quickly. Existing gesture recognition techniques suffer from poor recognition accuracy and low rate. To address the existing shortcomings, we propose an improved lightweight convolutional neural network called E-MobileNetv2. The ECA module is added to the original MobileNetv2 network model to obtain more useful features by computing the information interactions between the current channel and the adjacent channels and between the current channel and the distant channels in the feature map. We add R6-SELU activation function to enhance the network's ability to extract features. By adjusting the shrinkable hyper-parameters, the number of parameters of the network is reduced to improve the recognition speed. The improved network model achieves excellent performance on both the self-built dataset Gesture_II and the public dataset Jester. The recognition accuracy of the improved model is 96.82%, which is 3.17 % higher than that of the original model, achieving an increase in accuracy and recognition speed. [ABSTRACT FROM AUTHOR]

The rolling bearing is a crucial component of the rotating machine, and it is particularly vital to ensure its normal operation. In addition, the selection of different category features will add uncertainty and bias to the classification results. In order to decrease the interference of these factors to fault diagnosis, a new method that automatically learns the features of the data combined with Markov transition field (MTF) and convolutional neural network (CNN) is proposed in this paper, namely MTF-CNN. The MTF contributes to convert the original time series into corresponding figures, and the CNN is used to extract the deep feature information in the figure to complete the fault diagnosis. The effectiveness of the proposed method is verified by two public data sets. The experimental results show that MTF-CNN can classify different types of faults, and the highest accuracy rate can reach 100%. Likewise, the classification accuracy of this method is higher than some existing methods. [ABSTRACT FROM AUTHOR]

In order to enhance the hydraulic performance of the volute pump, the Kriging model and genetic algorithm (GA) were used to optimize the 3D diffuser of the volute pump, and the hydraulic performance of the optimized model was compared and analyzed with the original model. The volute pump diffuser model was parameterized by BladeGen software. A total of 14 parameters such as the distance between the leading and trailing edges and the central axis, and the inlet and outlet vane angle were selected as design variables, and the efficiency under the design condition was taken as the optimization objective. A total of 70 sets of sample data were randomly selected in the design space to train and test the Kriging model. The optimal solution was obtained by GA. The shape and inner flow of the optimized diffuser were compared with those of the original diffuser. The research results showed that the Kriging model can effectively establish the high-precision mathematical function between the design variables and the optimization objective, and the R2 value is 0.95356, which meets the engineering needs. The optimized geometry model demonstrated a significant change, the vane leading edge became thinner, and the wrap angle increased. After optimization, the hydraulic performance of the volute pump under design and part-load conditions were greatly improved, the efficiency under design conditions increased by 2.65%, and the head increased by 0.83 m. Furthermore, the inner flow condition improved, the large area of low-speed and vortex disappeared, the pressure distribution in the diffuser was more reasonable, and the pressure gradient variation decreased. [ABSTRACT FROM AUTHOR]

Tailoring electromagnetic properties by meta-devices has aroused great interest with respect to manipulating light. However, the uncertainty of angular dispersion introduced by the incident waves prevents their further applications. Here, we propose a general paradigm for achieving dual-transmission windows while simultaneously eliminating the corresponding angular dispersions by a dynamic manner. The strategy of loading varactor diodes into a plasmonic meta-atom is used. In this way, the blue shifts of angular dispersion can be dynamically compensated by the red shifts introduced by the varactor diodes when driven by bias voltage. As a proof-of-principle, an active meta-atom with varactor diodes is presented. The varactor diodes embedded can independently regulate dual-transmission windows. The test results are consistent with the simulation ones. The presented meta-device is used for intelligent radome, angle-multiplexed communications, and incident-angle-insensitive equipment while providing tunable angular dispersion properties. [ABSTRACT FROM AUTHOR]

Malondialdehyde (MDA) is one of the representative end products under lipid peroxidation, indicating the degree of lipid oxidation in foods. However, compounds in pickled products, especially the nitrite in salted lean pork can react with MDA under the acidic condition, leads to the loss of MDA and an underestimation on lipid oxidation through the conventional assay. In this study, the quantification for MDA in the sample containing sodium nitrite were found lacking accuracy by the thiobarbituric acid (TBA) assay and chromatography assay based on alkaline hydrolysis as the reaction between them were difficult to be completely inhibited. Among other trials, the improvement GC-MS analysis utilizing deuterium substituted MDA (MDA-d2) as an internal standard and applying perfluorophenylhydrazine (PFPH) as a derivative reagent can reduce the deviations from the presence of nitrite in the salted lean pork meat and the recovery is between 93.9% and 98.4% and coefficient of variation for the intermediate precision is between 1.1 and 3.5% using the method. The advanced gas chromatograph mass spectrometer (GC-MS) assay also has a very low detection limit (0.25 ng/mL) with both hydrolysis types. [ABSTRACT FROM AUTHOR]

Electrostatic abrasive implantation technology is a classical process based on electrostatic field to implant abrasive particles into base material. However, there is still not a quantitative model to ensure the implantation performance due to the fact that the electrostatic abrasive implantation is a complex multi-physics coupled process. To ensure the quality of sandpaper and elucidate the complex motion mechanism of Al2O3 based abrasive particles in a high voltage electrostatic field, a multi-physical field coupling simulation model is proposed. First, the mechanical model is constructed for the complex problem of the electrostatic abrasive implantation process. Then, the field model is established for the problem that the superposition of multi-physical fields leads to complicated environmental conditions. Finally, the evaluation model is established for the problems that the abrasive planting effect is difficult to evaluate and the planting parameters are difficult to adjust. Besides, a tailored electrostatic abrasive planting testing equipment is designed for the implantation performance analysis purpose. Single-parameter electrostatic abrasive planting experiments were conducted to analyze and verify the effect of different pole plate voltage and pole plate spacing on the abrasive implantation rate. To ensure the implantation performance, the applicable pole plate spacing ranges from 30 mm to 50 mm and the suitable voltage is 20–40 kV according to the simulation result in the proposed two models. Meanwhile, the implantation rate experimentation shows the coupling field model coincides with the experiments better. The key factors affecting the electrostatic abrasive planting process are identified, and a feasible multi-physical field coupled abrasive particle motion simulation model is proposed. [ABSTRACT FROM AUTHOR]

Recently, there is of significant interest in developing multi-criteria decision making (MCDM) techniques with large applications for real-life problems. Making a reasonable and accurate decision on MCDM problems can help develop enterprises better. The existing MCDM methods, such as the grey comprehensive evaluation (GCE) method and the technique for order preference by similarity to an ideal solution (TOPSIS), have their one-sidedness and shortcomings. They neither consider the difference of shape and the distance of the evaluation sequence of alternatives simultaneously nor deal with the interaction that universally exists among criteria. Furthermore, some enterprises cannot consult the best professional expert, which leads to inappropriate decisions. These reasons motivate us to contribute a novel hybrid MCDM technique called the grey fuzzy TOPSIS (FGT). It applies fuzzy measures and fuzzy integral to express and integrate the interaction among criteria, respectively. Fuzzy numbers are employed to help the experts to make more reasonable and accurate evaluations. The GCE method and the TOPSIS are combined to improve their one-sidedness. A case study of supplier evaluation of a collaborative manufacturing enterprise verifies the effectiveness of the hybrid method. The evaluation result of different methods shows that the proposed approach overcomes the shortcomings of GCE and TOPSIS. The proposed hybrid decision-making model provides a more accurate and reliable method for evaluating the fuzzy system MCDM problems with interaction criteria. [ABSTRACT FROM AUTHOR]

In order to explore the applicability of waste engine oil and waste cooking oil used in aged asphalt, the effect of waste engine oil and waste cooking oil on aged asphalt recycling was studied through the analysis of the improvement of its physical, chemical, and rheological properties. Six aged asphalt binders with different aging times were obtained by indoor test simulation using the Thin Film Oven Test at 163 °C. Then, waste engine oil and waste cooking oil with five different dosages were added to investigate improvement performances. The results clearly demonstrated that waste engine oil and waste cooking oil could soften and recover the work ability of aged asphalt effectively. Furthermore, the physical, chemical, and rheological performances of six aged asphalts could be improved to normal level of virgin asphalt if the content of waste engine oil or waste cooking oil was suitable. The rheological properties of aged asphalt with waste cooking oil had better improvement than that with waste engine oil. Overall, the good applicability would provide waste oil a much wider service range in asphalt pavement recycling field. It also provided a method of developing new rejuvenating agent with the two waste oils to achieve complex synergism effect. Moreover, it realized the waste cyclic utilization and environmental protection. [ABSTRACT FROM AUTHOR]

Moving nanosystems often rest on a medium exhibiting viscoelastic behavior in engineering applications. The moving velocity and viscoelastic parameters of the medium usually have an interacting impact on the mechanical properties of nanostructures. This paper investigates the dynamic stability of an axially-moving nanoplate resting on a viscoelastic foundation based on the nonlocal elasticity theory. Firstly, the governing partial equations subject to appropriate boundary conditions are derived through utilizing the Hamilton's principle with the axial velocity, viscoelastic foundation, nonlocal effect and biaxial loadings taken into consideration. Subsequently, the characteristic equation describing the dynamic characteristics is obtained by employing the Galerkin strip distributed transfer function method. Then, complex frequency curves for the nanoplate are displayed graphically and the effects of viscoelastic foundation parameters, small-scale parameters and axial forces on divergence instability and coupled-mode flutter are analyzed, which show that these parameters play a crucial role in affecting nanostructural instability. The presented results benefit the designation of axially-moving graphene nanosheets or other plate-like nanostructures resting on a viscoelastic foundation. [ABSTRACT FROM AUTHOR]

Featured Application: the work introduced in this paper can be used for wildlife conservation, health protection, and other engineering applications. Acoustic localization for a moving source plays a key role in engineering applications, such as wildlife conservation and health protection. Acoustic detection methods provide an alternative to traditional radar and infrared detection methods. Here, an acoustic locating method of array signal processing based on intersecting azimuth lines of two arrays is introduced. The locating algorithm and the precision simulation of a single array shows that such a single array has good azimuth precision and bad range estimation. Once another array of the same type is added, the moving acoustic source can be located precisely by intersecting azimuth lines. A low-speed vehicle is used as the simulated moving source for the locating experiments. The length selection of short correlation and moving path compensation are studied in the experiments. All results show that the proposed novel method locates the moving sound source with high precision (<5%), while requiring fewer instruments than current methods. [ABSTRACT FROM AUTHOR]

The results of unsteady-flow simulations and experiments are discussed to investigate active noise-reduction effects on the stator–rotor interaction in a single-stage low-speed compressor with nonuniform trailing edge blowing. It is found that for the investigated type of stator–rotor interaction noise, nonuniform trailing edge blowing has beneficial noise-reducing effects. The overall aim is to demonstrate that nonuniform trailing edge blowing can compensate momentum loss and reduce the axial thrust on rotor blades. The results illustrate how nonuniform trailing edge blowing influences the sound pressure level of the blade-passing frequencies and results in active noise reduction effects. The study was conducted using a trailing edge blowing system, a four-hole dynamic flow-field measurement system, and phase lock technology. The results obtained show that nonuniform trailing edge blowing leads to substantial noise-reduction effects, lowering sound levels by more than 10 dB with about 5‰ of inlet mass flow. [ABSTRACT FROM AUTHOR]

Depletion of water resources has threatened water security in the Beijing-Tianjin-Hebei urban agglomeration, China. However, the relative importance of precipitation and urbanization to water storage change has not been sufficiently studied. In this study, both terrestrial water storage (TWS) and groundwater storage (GWS) change in Jing-Jin-Ji from 1979 to the 2010s were investigated, based on the global land data assimilation system (GLDAS) and the EartH2Observe (E2O) outputs, and we used a night light index as an index of urbanization. The results showed that TWS anomaly varied in three stages: significant increase from 1981 to 1996, rapid decrease from 1996 to 2002 and increase from 2002 to the 2010s. Simultaneously, GWS has decreased with about 41.5 cm (500% of GWS in 1979). Both urbanization and precipitation change influenced urban water resource variability. Urbanization was a relatively important factor to the depletion of TWS (explains 83%) and GWS (explains 94%) since the 1980s and the precipitation deficit explains 72% and 64% of TWS and GWS variabilities. It indicates that urbanization coupled with precipitation deficit has been a more important factor that impacted depletion of both TWS and GWS than climate change only, in the Jing-Jin-Ji region. Moreover, we suggested that the cumulative effect should be considered when discussing the relationship between influence factors and water storage change. [ABSTRACT FROM AUTHOR]