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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]