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Purpose The purpose of this study is to clarify the mechanism of ambient and transient temperature effects on piezoelectric pressure sensors, and to propose corresponding compensation measures. The temperature of the explosion field has a significant influence on the piezoelectric sensor used to measure the shock wave pressure. For accurate shock wave pressure measurement, based on the actual piezoelectric pressure sensors used in the explosion field, the effects of ambient and transient temperatures on the sensor should be studied. Design/methodology/approach The compensation method of the ambient temperature is discussed according to the sensor size and material. The theoretical analysis method of the transient temperature is proposed. For the transient temperature conduction problem of the sensor, the finite element simulation method of structure-temperature coupling is used to solve the temperature distribution of the sensor and the change in the contact force on the quartz crystal surface under the step and triangle temperatures. The simulation results are highly consistent with the theory. Findings Based on the analysis results, a transient temperature control method is proposed, in which 0.5 mm thick lubricating silicone grease is applied to the sensor diaphragm, and 0.2 mm thick fiberglass cloth is wrapped around the sensor side. Simulation experiments are carried out to verify the feasibility of the control method, and the results show that the control method effectively suppresses the output of the thermal parasitic. Originality/value The above thermal protection methods can effectively improve the measurement accuracy of shock wave pressure and provide technical support for the evaluation of the power of explosion damage.

N 6-methyladenosine (m6A), the most prevalent mRNA modification in eukaryotes, is an emerging player of gene regulation at transcriptional and translational levels. Here, we explored the role of m6A modification in response to low temperature in Arabidopsis (Arabidopsis thaliana). Knocking down mRNA adenosine methylase A (MTA), a key component of the modification complex, by RNA interference (RNAi) led to drastically reduced growth at low temperature, indicating a critical role of m6A modification in the chilling response. Cold treatment reduced the overall m6A modification level of mRNAs especially at the 3′ untranslated region. Joint analysis of the m6A methylome, transcriptome and translatome of the wild type (WT) and the MTA RNAi line revealed that m6A-containing mRNAs generally had higher abundance and translation efficiency than non-m6A-containing mRNAs under normal and low temperatures. In addition, reduction of m6A modification by MTA RNAi only moderately altered the gene expression response to low temperature but led to dysregulation of translation efficiencies of one third of the genes of the genome in response to cold. We tested the function of the m6A-modified cold-responsive gene ACYL-COA:DIACYLGLYCEROL ACYLTRANSFERASE 1 (DGAT1) whose translation efficiency but not transcript level was reduced in the chilling-susceptible MTA RNAi plant. The dgat1 loss-of-function mutant exhibited reduced growth under cold stress. These results reveal a critical role of m6A modification in regulating growth under low temperature and suggest an involvement of translational control in chilling responses in Arabidopsis.

It is challenging to fool an image classifier based on deep neural networks under the black-box setting where the target model can only be queried. Among existing black-box attacks, transfer-based methods tend to overfit the substitute model on parameter settings. Decision-based methods have low query efficiency due to fixed sampling and greedy search strategy. To alleviate the above problems, we present a new framework for query-efficient black-box adversarial attack by bridging transfer-based and decision-based attacks. We reveal the relationship between current noise and variance of sampling, the monotonicity of noise compression, and the influence of transition function. Guided by the new framework, we propose a black-box adversarial attack named Customized Iteration and Sampling Attack (CISA). CISA estimates the distance from nearby decision boundary to set the stepsize, and uses a dual-direction iterative trajectory to find the intermediate adversarial example. Based on the intermediate adversarial example, CISA conducts customized sampling according to the noise sensitivity of each pixel to further compress noise, and relaxes the state transition function to achieve higher query efficiency. We embed and benchmark existing adversarial attack methods under the new framework. Extensive experiments demonstrate CISA's advantage in query efficiency of black-box adversarial attacks.

N6-methyldeoxyadenosine (6mA) is a recently discovered DNA modification involved in regulating plant adaptation to abiotic stresses. However, the mechanisms and changes of 6mA under cold stress in plants are not yet fully understood. Here, we conducted a genome-wide analysis of 6mA and observed that 6mA peaks were predominantly present within the gene body regions under both normal and cold conditions. In addition, the global level of 6mA increased both in Arabidopsis and rice after the cold treatment. The genes that exhibited an up-methylation showed enrichment in various biological processes, whereas there was no significant enrichment observed among the down-methylated genes. The association analysis revealed a positive correlation between the 6mA level and the gene expression level. Joint analysis of the 6mA methylome and transcriptome of Arabidopsis and rice unraveled that fluctuations in 6mA levels caused by cold exposure were not correlated to changes in transcript levels. Furthermore, we discovered that orthologous genes modified by 6mA showed high expression levels; however, only a minor amount of differentially 6mA-methylated orthologous genes were shared between Arabidopsis and rice under low-temperature conditions. In conclusion, our study provides information on the role of 6mA in response to cold stress and reveals its potential for regulating the expression of stress-related genes.

The protection of earthen sites plays an important role in the context of preservation of cultural heritage, especially in the inheritance and promotion of history and culture. The aim of the paper is to present the essential results of an ongoing research on a reinforced rammed earthen wall in Suoyang City (Guazhou, China). The wall vibrations caused by ambient actions were analyzed using the stochastic subspace algorithm to estimate the modal parameters of the wall. The frequencies of the first three orders are 3.566 Hz, 5.003 Hz, and 6.250 Hz, and the corresponding modes are first-order transverse bending, second-order left and right torsion, and third-order vertical bending, respectively. Then, according to the data of elastic modulus obtained in the lab, the finite element calculation is carried out, and referring to the results of field measurement, the revised elastic modulus value is 205.90 MPa. It is worth mentioning that the revised value is significantly improved from the original laboratory value, and it is also indicated that the seismic performance of the reinforced wall has been significantly improved. The present work is expected to provide a theoretical basis for reinforcement, protection, and seismic control of earthen ruins.

Fault diagnosis is crucial for photovoltaic (PV) power generation, but the output uncertainty characteristics of PV arrays caused by different failures and their fluctuations make them facing new challenges. The parameter estimation (PE) method is widely used for uncertainty analysis, but there exist big differences between the PE results and the real output distributions. To solve this problem, this paper proposes a method for acquiring the fault diagnosis threshold based on non-parametric kernel density estimation (NKDE). First, the distribution characteristics of PV arrays output are statistically analysed, it is found that current and power are mainly affected by the solar irradiance, resulting in strong volatility and uncertainty, which makes it hard to get the threshold for fault diagnosis, thus we propose new three status indicators to finish it. Then, the probability models of the three indicators are built based on the kernel density estimation (KDE) method, by assigning the confidence values of the models, the fault diagnosis threshold intervals can be obtained. Verification shows that NKDE method performed better than traditional PE method in fitting the distribution of the PV arrays output, it does not need prior knowledge of the probability density function. And the proposed fault diagnosis method proves it is feasible to apply the uncertainty analysis method to PV array fault diagnosis. The paper provides a new idea for setting the dynamic threshold for PVarray fault diagnosis.

Rheumatoid arthritis (RA) is an inflammatory autoimmune disease. Although significant progress has been made in clinical treatment, joint inflammation may continue or worsen, and may even progress to the end-stage that requires joint replacement. Traditional therapy using methotrexate (MTX) would cause serious off-target systemic toxicities. Therefore, it is crucial to effectively and specifically deliver MTX to targeted inflamed joints to decrease its adverse systemic toxicities and improve its therapeutic index. Herein, we develop multifunctional nanocarriers for diagnostic radioisotope (99mTc) labeling and therapeutic targeted drug (MTX) delivery by using PEGylated hyperbranched semiconducting polymer nanoparticles (HSP-PEG-NPs) as carriers. Upon intravenous administration, the nanoparticles can extravasate through the turbulent blood-joint barrier and access the inflamed joints. In vivo SPECT/CT imaging shows high accumulation in the inflamed joints of mice with RA after intravenous injection of HSP-PEG-NPs with 99mTc labeling (99mTc-HSP-PEG). In vivo therapeutic evaluations suggest that MTX@HSP-PEG-NPs significantly alleviate RA with a high therapeutic index and relatively low adverse systemic toxicities in comparison with free MTX at the same dose. Our study shows that HSP-PEG-NPs could serve as multifunctional vehicles to deliver radioisotopes for in vivo imaging, and MTX for RA treatment, highlighting the innovative development of the nanoparticle-based RA treatment strategy for clinical applications.

Photovoltaic array produces massive running data, and such data are time series of strong coupling features with each other. In addition, photovoltaic output data has a strong fluctuating and nonlinear feature; it brings extra difficulty to photovoltaic array fault feature extraction and its fault diagnosis. To solve these problems, this article proposes a fault diagnosis method using the time series features for photovoltaic array. The features of the photovoltaic array output are described in this article. From the perspective of distance analysis and similarity analysis, this article proposes a feature extraction method for photovoltaic array output time series, and features of output time series under different fault conditions are analyzed. Taking similarity index and distance index as input, the fuzzy system is built for identifying faults for photovoltaic array. The operational data analysis shows that the time series feature indexes proposed can successfully characterize different fault types, and this method can effectively diagnose typical faults of photovoltaic array.

Increasing forest soil organic carbon (SOC) storage is important for reducing carbon dioxide (CO₂) emissions from terrestrial ecosystems and mitigating global climate change. Although the effects of altitude, temperature and rainfall on organic carbon have been studied extensively, it is difficult to increase SOC storage by changing these factors in actual forest management. This study determined the SOC, soil physical and chemical properties, nutrient elements, heavy metal elements, soil minerals and microbial biomass in the 0–140‐cm soil layer of the monsoon broad‐leaved forest in the acid red soil region of southwestern China by stratification. We tried to identify the soil factors affecting the SOC storage of the forest in the acid red soil region and determine the weights of the factors affecting the SOC, with the aim of improving the SOC retention capacity in forest management by changing the main soil factors affecting SOC storage. The results showed that the soil factors affecting the forest SOC storage in this area are total nitrogen (N, 22.7%) > soil water content (19.9%) > active iron (including poorly crystalline iron, Feₒ, 15.5%) > pH (9.5%) > phosphorus (P, 9.4%) > aluminium (Al, 8.9%) > silicon (Si, 7.1%) > sulphur (S, 6.8%). Of these factors, N, the water content, Feₒ, and P are practical factors for forest management, whereas the pH, Al, Si and S are not. SOC was significantly positively correlated with the soil N concentration, water content, active iron content and P concentration (p

Due to the complex structure of liver tumors and the low contrast with normal tissues make it still a challenging task to accurately segment liver tumors from CT images. To address these problems, we propose an end-to-end segmentation method for liver tumors.The method uses a cascade structure to improve the network's extraction of information. First, the Side-output Feature Fusion Attention block is used to fuse features at different levels and combine with attention mechanism to focus on important information. Then, the Atrous Spatial Pyramid Pooling Attention block is used to extract multi-scale semantic features. Finally, the Multi-scale Prediction Fusion block is used to fully fused the features captured at each layer of the network.To verify the performance of the proposed model and the effectiveness of each module, we evaluate it on LiTS and 3DIRCADb datasets and obtained Dice per Case of 0.665 and 0.719, respectively, and Dice Global of 0.812 and 0.784, respectively.The proposed method is compared with the basic model 3D U-Net, as well as some mainstream methods based on U-Net variants, and our method achieves better performance on the liver tumor segmentation task and is superior to most segmentation algorithms.