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The transition characteristics of a modified RAE5243 airfoil were investigated using a wind tunnel test and numerical simulations. Transition detection is of great significance for the assessment of drag reduction. In wind tunnel tests, transition location can be detected by infrared thermography. However, in subsonic and transonic wind tunnel tests, the temperature difference between the laminar flow region and turbulent flow region is small. Moreover, the test models are usually made of metals, which make the transition location hard to identify. Combined with infrared thermography, a carbon nanotube heating coating powered by electricity was used to detect the transition location of a modified RAE5243 airfoil wing. The effects of heating power, angle of attack (AOA), and Mach number were studied. The results show that heating power has no impact on transition location. As the AOA increases, the transition location moves forward. With an increase in Mach number, the transition location moves forward first and then backward, and it reaches its most forward point at Ma = 0.75. The results of our numerical simulations indicate that, at Ma ≥ 0.75, a shock wave appears on the wing, and the transition is closely related to the shock wave rather than the adverse pressure gradient. [ABSTRACT FROM AUTHOR]

Spider silk protein, renowned for its excellent mechanical properties, biodegradability, chemical stability, and low immune and inflammatory response activation, consists of a core domain with a repeat sequence and non-repeating sequences at the N-terminal and C-terminal. In this review, we focus on the relationship between the silk structure and its mechanical properties, exploring the potential applications of spider silk materials in the detection of energetic materials. [ABSTRACT FROM AUTHOR]

In the realm of automated industry and smart production, the deployment of fault warning systems is crucial for ensuring equipment reliability and enhancing operational efficiency. Although there are a multitude of existing methodologies for fault warning, the proficiency of these systems in processing and analysing data is increasingly challenged by the progression of industrial apparatus and the escalating magnitude and intricacy of the data involved. To address these challenges, this research outlines an innovative fault warning methodology that combines a bi-directional long short-term memory (Bi-LSTM) network with an enhanced hunter–prey optimisation (EHPO) algorithm. The Bi-LSTM network is strategically utilised to outline complex temporal patterns in machinery operational data, while the EHPO algorithm is employed to meticulously fine-tune the hyperparameters of the Bi-LSTM, aiming to enhance the accuracy and generalisability of fault warning. The EHPO algorithm, building upon the foundational hunter–prey optimisation (HPO) framework, introduces an advanced population initialisation process, integrates a range of strategic exploration methodologies, and strengthens its search paradigms through the incorporation of the differential evolution (DE) algorithm. This comprehensive enhancement aims to boost the global search efficiency and accelerate the convergence speed of the algorithm. Empirical analyses, conducted using datasets from real-world industrial scenarios, have validated the improved warning performance of this proposed methodology against some benchmark techniques, as evidenced by superior metrics such as root mean square error (RMSE) and mean absolute error (MAE), albeit with a slight increase in computational resource requirements. This study not only proposes a novel paradigm for fault warning within complex industrial frameworks but also contributes to the discourse on hyperparameter optimisation within the field of machine learning algorithms. [ABSTRACT FROM AUTHOR]

Branching is an important agronomic trait that is conducive to plant architecture and yield in flowering Chinese cabbage. Plant branching is regulated by a complex network mediated by hormones; gibberellin (GA) is one of the important hormones which is involved in the formation of shoot branching. Research on the regulatory mechanism of GA influencing rosette branch numbers is limited for flowering Chinese cabbage. In this study, the exogenous application of 600 mg/L GA3 effectively inhibited rosette branching and promoted internode elongation in flowering Chinese cabbage. RNA-Seq analysis further found that these DEGs were significantly enriched in 'the plant hormone signal transduction' pathways, and auxin-related genes were significantly differentially expressed between MB and MB_GA. The upregulation of auxin (AUX) and the upregulation of auxin/indole-3-acetic acid (AUX/IAA), as well as the downregulation of SMALL AUXIN-UPREGULATED RNA (SAUR), were found in the negative regulation of the rosette branching. The qRT-PCR results showed that the expression of AUX/IAA and SAUR from IAA gene family members were consistent with the results of transcriptome data. Phytohormone profiling by targeted metabolism revealed that endogenous auxin contents were significantly increased in MB_GA. Transcriptome and metabolome analysis clarified the main plant hormones and genes underlying the rosette branching in flowering Chinese cabbage, confirming that auxin could inhibit rosette branching. In this regard, the results present a novel angle for revealing the mechanism of gibberellin acting on the branching architecture in flowering Chinese cabbage. [ABSTRACT FROM AUTHOR]

Kaposi's sarcoma-associated herpesvirus (KSHV) can cause a variety of malignancies. Ganciclovir (GCV) is one of the most efficient drugs against KSHV, but its non-specificity can cause other side effects in patients. Nucleic acid miR-34a-5p can inhibit the transcription of KSHV RNA and has great potential in anti-KSHV therapy, but there are still problems such as easy degradation and low delivery efficiency. Here, we constructed a co-loaded dual-drug nanocomplex (GCV@ZIF-8/PEI-FA+miR-34a-5p) that contains GCV internally and adsorbs miR-34a-5p externally. The folic acid (FA)-coupled polyethyleneimine (PEI) coating layer (PEI-FA) was shown to increase the cellular uptake of the nanocomplex, which is conducive to the enrichment of drugs at the KSHV infection site. GCV and miR-34a-5p are released at the site of the KSHV infection through the acid hydrolysis characteristics of ZIF-8 and the "proton sponge effect" of PEI. The co-loaded dual-drug nanocomplex not only inhibits the proliferation and migration of KSHV-positive cells but also decreases the mRNA expression level of KSHV lytic and latent genes. In conclusion, this co-loaded dual-drug nanocomplex may provide an attractive strategy for antiviral drug delivery and anti-KSHV therapy. [ABSTRACT FROM AUTHOR]

We present a decade of observations of pulse arrival times for 85 pulsars using the Nanshan radio telescope from July 2002 to March 2014. The Cholesky method can accurately estimate the covariance function of the timing residuals, significantly improving the parameter's estimation accuracy when red noise is prominent. We utilize the Cholesky method to determine positions and basic timing parameters of these pulsars, as well as to obtain timing residuals. Most of these sources showed evidence of significant timing irregularities, which are described. The spectral analyses of timing residuals are presented for pulsars showing obvious red noise. Our results show that timing residuals in half of these pulsars are attributed to rotational irregularities. The red noise in normal pulsars may originate from a random walk in spin frequency or spin-down rate. [ABSTRACT FROM AUTHOR]

Hydration plays a crucial role in cement composites, but the traditional methods for measuring hydration heat face several limitations. In this study, we propose a machine learning-based approach to predict hydration heat at specific time points for three types of cement composites: ordinary Portland cement pastes, fly ash cement pastes, and fly ash–metakaolin cement composites. By adjusting the model architecture and analyzing the datasets, we demonstrate that the optimized artificial neural network model not only performs well during the learning process but also accurately predicts hydration heat for various cement composites from an extra dataset. This approach offers a more efficient way to measure hydration heat for cement composites, reducing the need for labor- and time-intensive sample preparation and testing. Furthermore, it opens up possibilities for applying similar machine learning approaches to predict other properties of cement composites, contributing to efficient cement research and production. [ABSTRACT FROM AUTHOR]