Few-shot RUL prediction for engines based on CNN-GRU model.

In the realm of prognosticating the remaining useful life (RUL) of pivotal components, such as aircraft engines, a prevalent challenge persists where the available historical life data often proves insufficient. This insufficiency engenders obstacles such as impediments in performance degradation feature extraction, inadequacies in capturing temporal relationships comprehensively, and diminished predictive accuracy. To address this issue, a 1D CNN-GRU prediction model for few-shot conditions is proposed in this paper. In pursuit of more comprehensive data feature extraction and enhanced RUL prognostication precision, the Convolutional Neural Network (CNN) is selected for its capacity to discern high-dimensional features amid the intricate dynamics of the data. Concurrently, the Gated Recurrent Unit (GRU) network is leveraged for its robust capability in extracting temporal features inherent within the data. We combine the two to construct a CNN-GRU hybrid network. Moreover, the integration of data distribution alongside correlation and monotonicity indices is employed to winnow the input of multi-sensor monitoring parameters into the CNN-GRU network. Finally, the engine RULs are predicted by the trained model. In this paper, experiments are conducted on a sub-dataset of the National Aeronautics and Space Administration (NASA) C-MAPSS multi-constraint dataset to validate the effectiveness of the method. Experimental results have demonstrated that this method has high accuracy in RUL prediction tasks, which can powerfully demonstrate its effectiveness.

A dew-responsive pectin-based herbicide for enhanced photodynamic inactivation.

5-aminolevulinic acid (5-ALA) has been fully demonstrated as a biodegradable, without resistance, and pollution-free pesticide. However, the lack of targeting and the poor adhesion result in a low utilization rate, limiting its practical application. Herein, a dew-responsive polymer pro-pesticide Pec-hyd-ALA was successfully synthesized by grafting 5-ALA onto the pectin (PEC) backbone via acid-sensitive acylhydrazone bonds. When the pro-pesticide is exposed to acid dew on plant surfaces at night, 5-ALA is released and subsequently converted to photosensitize (Protoporphyrin IX, PpIX)in plant cells, leading to its accumulation and promoting photodynamic inactivation (PDI). An inverted fluorescence microscope has verified the accumulation of tetrapyrrole in plant cells. In addition, the highly bio-adhesive PEC backbone effectively improved the wetting and retention of 5-ALA on leaves. The pot experiment also demonstrated the system's control effect on barnyard grass. This work provides a promising approach to improving the herbicidal efficacy of 5-ALA.

Non-peptidic inhibitors targeting SARS-CoV-2 main protease: A review.

The COVID-19 pandemic continues to pose a threat to global health, and sounds the alarm for research & development of effective anti-coronavirus drugs, which are crucial for the patients and urgently needed for the current epidemic and future crisis. The main protease (Mpro) stands as an essential enzyme in the maturation process of SARS-CoV-2, playing an irreplaceable role in regulating viral RNA replication and transcription. It has emerged as an ideal target for developing antiviral agents against SARS-CoV-2 due to its high conservation and the absence of homologous proteases in the human body. Among the SARS-CoV-2 Mpro inhibitors, non-peptidic compounds hold promising prospects owing to their excellent antiviral activity and improved metabolic stability. In this review, we offer an overview of research progress concerning non-peptidic SARS-CoV-2 Mpro inhibitors since 2020. The efforts delved into molecular structures, structure-activity relationships (SARs), biological activity, and binding modes of these inhibitors with Mpro. This review aims to provide valuable clues and insights for the development of anti-SARS-CoV-2 agents as well as broad-spectrum coronavirus Mpro inhibitors.

Bearing Fault Diagnosis Method Based on Improved Singular Value Decomposition Package.

The singular value decomposition package (SVDP) is often used for signal decomposition and feature extraction. At present, the general SVDP has insufficient feature extraction ability due to the two-row structure of the Hankel matrix, which leads to mode mixing. In this paper, an improved singular value decomposition packet (ISVDP) algorithm is proposed: the feature extraction ability is improved by changing the structure of the Hankel matrix, and similar signal sub-components are selected by similarity to avoid having the same frequency component signals being decomposed into different sub-signals. In this paper, the effectiveness of ISVDP is illustrated by a set of simulation signals, and it is utilized in fault diagnosis of bearing data. The results show that ISVDP can effectively suppress the model-mixing phenomenon and can extract the fault features in bearing vibration signals more accurately.

Hybrids of delavirdine and piperdin-4-yl-aminopyrimidines (DPAPYs) as potent HIV-1 NNRTIs: Design, synthesis and biological activities.

The hybrids of delavirdine and piperdin-4-yl-aminopyrimidine (DPAPYs) were designed from two excellent HIV-1 NNRTIs delavirdine and piperidin-4-yl-aminopyrimidine via molecular hybridization. The target compounds 4a-r were prepared and evaluated for their cellular anti-HIV activities and cytotoxicities as well as the inhibitory activities against HIV-1 reverse transcriptase (RT). All the newly synthesized compounds demonstrated moderate to excellent potency against wild-type (WT) HIV-1 with EC50 values in a range of 5.7 to 0.0086 µM and against RT with IC50 values ranging from 12.0 to 0.11 µM, indicating that the DPAPYs were specific RT inhibitors. Among all, 4d displayed the most potent activity against WT HIV-1 (EC50 = 8.6 nM, SI = 2151). Gratifyingly, it exhibited good to excellent potency against the single HIV-1 mutants L100I, K103N, Y181C, Y188L, E138K, as well as the double mutant F227L + V106A. Furthermore, the preliminary structure-activity relationships were summarized, molecular modeling was conducted to explore the binding mode of DPAPYs and HIV-1 RT, and their physicochemical properties were also predicted.

H2O2/Glucose Sensor Based on a Pyrroloquinoline Skeleton-Containing Molecule Modified Gold Cavity Array Electrode.

H2O2-related metabolites are essential indicators in clinical diagnosis because the accumulation of such reactive oxygen species could cause the risk of cardiovascular disease. Herein, we reported an electrochemical sensor to determine H2O2 and glucose. The pyrroloquinoline skeleton containing molecules (PQT) were used as the electrocatalyst and the gold cavity array (GCA) electrodes as the supporting electrode. The GCA electrode was fabricated by electrodeposition using high-ordered two-dimensional polystyrene spheres as the template. The strong absorbability of iodide ions (I-) displaced adventitious materials from the metal surface and the I- monolayer was subsequently removed by electrochemical oxidation to get a clean electrode surface. PQT molecules were firmly immobilized on the GCA electrode and performed an excellent electrocatalytic effect on H2O2/glucose detection, manifested by a small overpotential and a significantly increased reduction current. A good linear correlation was observed over a wide range of 0.2 µmol/L-1.0 mmol/L with the limit of detection of 0.05 µmol/L. Moreover, the sensor can realize sensitive, accurate, and the highly selective detection of actual samples, proving its application prospect in clinical diagnosis.