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This paper presents a review of research progress on photovoltaic direct current arc detection based on VOSviewer bibliometric analysis. This study begins by introducing the basic concept and hazards of photovoltaic DC arcing faults, followed by a summary of commonly used arc detection techniques. Utilizing VOSviewer, the relevant literature is subjected to clustering and visualization analysis, offering insights into research hotspots, trends, and interconnections among different fields. Based on the bibliometric analysis method of VOSviewer software, this paper analyzes the articles published in the last 10 years (2014–2023) on photovoltaic DC fault diagnosis. We analyzed the specific characteristics of 2195 articles on arc failures, including year of publication, author, institution, country, references, and keywords. This study reveals the development trend, global cooperation model, basic knowledge, research hotspots, and emerging frontier of PV DC arc. Future research directions and development trends for photovoltaic DC arc detection are proposed which provides valuable references for further studies and applications in this domain. This comprehensive analysis indicates that photovoltaic DC arc detection technology is expected to find broader applications and greater promotion in the future. [ABSTRACT FROM AUTHOR]

The decipherment of ancient Chinese scripts, such as oracle bone and bronze inscriptions, holds immense significance for understanding ancient Chinese history, culture, and civilization. Despite substantial progress in recognizing oracle bone script, research on the overall recognition of ancient Chinese characters remains somewhat lacking. To tackle this issue, we pioneered the construction of a large-scale image dataset comprising 9233 distinct ancient Chinese characters sourced from images obtained through archaeological excavations. We propose the first model for recognizing the common ancient Chinese characters. This model consists of four stages with Linear Embedding and Swin-Transformer blocks, each supplemented by a CoT Block to enhance local feature extraction. We also advocate for an enhancement strategy, which involves two steps: firstly, conducting adaptive data enhancement on the original data, and secondly, randomly resampling the data. The experimental results, with a top-one accuracy of 87.25% and a top-five accuracy of 95.81%, demonstrate that our proposed method achieves remarkable performance. Furthermore, through the visualizing of model attention, it can be observed that the proposed model, trained on a large number of images, is able to capture the morphological characteristics of ancient Chinese characters to a certain extent. [ABSTRACT FROM AUTHOR]

With the rapid growth of the photovoltaic industry, fire incidents in photovoltaic systems are becoming increasingly concerning as they pose a serious threat to their normal operation. Research findings indicate that direct current (DC) fault arcs are the primary cause of these fires. DC arcs are characterized by high temperature, intense heat, and short duration, and they lack zero crossing or periodicity features. Detecting DC fault arcs in intricate photovoltaic systems is challenging. Hence, researching DC fault arcs in photovoltaic systems is of crucial significance. This paper discusses the application of mathematical morphology for detecting DC fault arcs. The system utilizes a multi-stage mathematical morphology filter, and experimental results have shown its effective extraction of fault arc features. Subsequently, we propose a method for detecting DC fault arcs in photovoltaic systems using a cyclic neural network, which is well-suited for time series processing tasks. By combining multiple features extracted from experiments, we trained the neural network and achieved high accuracy. This experiment demonstrates that our recurrent neural network (RNN) based scheme for DC fault arc recognition has significant reference value and implications for future research. The ROC curve on the test set approaches 1 from the initial state, and the accuracy on the test set remains at 98.24%, indicating the strong robustness of the proposed model. [ABSTRACT FROM AUTHOR]

Defect detection in power scenarios is a critical task that plays a significant role in ensuring the safety, reliability, and efficiency of power systems. The existing technology requires enhancement in its learning ability from large volumes of data to achieve ideal detection effect results. Power scene data involve privacy and security issues, and there is an imbalance in the number of samples across different defect categories, all of which will affect the performance of defect detection models. With the emergence of the Internet of Things (IoT), the integration of IoT with machine learning offers a new direction for defect detection in power equipment. Meanwhile, a generative adversarial network based on multi-view fusion and self-attention is proposed for few-shot image generation, named MVSA-GAN. The IoT devices capture real-time data from the power scene, which are then used to train the MVSA-GAN model, enabling it to generate realistic and diverse defect data. The designed self-attention encoder focuses on the relevant features of different parts of the image to capture the contextual information of the input image and improve the authenticity and coherence of the image. A multi-view feature fusion module is proposed to capture the complex structure and texture of the power scene through the selective fusion of global and local features, and improve the authenticity and diversity of generated images. Experiments show that the few-shot image generation method proposed in this paper can generate real and diverse defect data for power scene defects. The proposed method achieved FID and LPIPS scores of 67.87 and 0.179, surpassing SOTA methods, such as FIGR and DAWSON. [ABSTRACT FROM AUTHOR]

Mitigating heavy metals (HMs) contamination and ensuring the safe production of crops is of paramount importance for sustainable agriculture development. The purpose of the current field plot study was to select maize varieties with low HMs and metalloids in their edible parts but high accumulation in other parts. The cadmium (Cd), arsenic (As), lead (Pb), and chromium (Cr) contents of 11 maize varieties were measured by atomic absorption spectrometry, and the plant growth and bioconcentration factors (BFs) were examined. Furthermore, the average daily intake (ADDi) of HMs in maize grains was calculated to assess the associated health risks. The results revealed that the growth of variety TZ23 was minimally impacted HMs and metalloids. The grains of all of the tested maize varieties contained Cr, As, and Pb contents in accordance with National Food Safety Standards (NFSSs, GB2762-2017, ≤0.1 mg·kg−1), while the Cd concentration in grains of varieties QJN1, LSCR, and JN20 were 0.084 mg·kg−1, 0.094 mg·kg−1, and 0.077 mg·kg−1, respectively, in accordance with NFSSs. The translocation factor (TF) of As, Pb and Cr in the grains of 11 maize varieties were found to be less than 1. However, the TF of grain Cd in varieties LYN9, JYN9, and QJN3 exceeded 1. For varieties HNY21, TZ23, and LYN9, the TF of Cd, As, Pb, and Cr in the stems/leaves was less than 1. Cluster analysis revealed that the grains of variety HNY21 had the lowest accumulation capacity of all four HMs. Importantly, the variety JN20 exhibited a high accumulation capacity for Pb and a low capacity for As, while both varieties SKN11 and QJN3 had high accumulation capacities for Cd and low capacities for As. Health risk (HR) indices of the different age groups displayed an overall trend of children > elderly > young adult. Among the HMs and metalloids, Cd and Cr pose the greatest health risks of maize intake. Variety QJN3 posed a significant HR due to chronic toxicity. This study provides a scientific basis for multi-element pollution control and screening of maize varieties suitable for cultivation in mining areas and the remediation of HMs-contaminated soils. [ABSTRACT FROM AUTHOR]

The camellia seed cake proteins (CP) used in this study were individually hydrolyzed with neutral protease, alkaline protease, papain, and trypsin. The results showed that the hydrolysate had the highest ACE inhibitory activity at 67.36 ± 0.80% after four hours of neutral protease hydrolysis. Val-Val-Val-Pro-Gln-Asn (VVVPQN) was then obtained through ultrafiltration, Sephadex G-25 gel chromatography separation, LC-MS/MS analysis, and in silico screening. VVVPQN had ACE inhibitory activity with an IC50 value of 0.13 mg/mL (198.66 μmol/L), and it inhibited ACE in a non-competitive manner. The molecular docking indicated that VVVPQN can combine with ACE to form eight hydrogen bonds. The results of the stability study showed that VVVPQN maintained high ACE-inhibitory activity in weakly acidic and neutral environments and that heat treatment (20–80 °C) and Na+, Mg2+, as well as Fe3+ metal ions had little effect on the activity of VVVPQN. Moreover, it remained relatively stable after in vitro simulated gastrointestinal digestion. These results revealed that VVVPQN identified in camellia seed cake has the potential to be applied in functional food or antihypertensive drugs. [ABSTRACT FROM AUTHOR]

Featured Application: The findings of this study can be applied to warehouses, factories, or businesses that store large amounts of eggs to extend their shelf life at low costs. Egg preservation is an important factor during storage and transportation. Fresh eggs were stored in boxes in a controlled atmosphere with an O2 concentration of 0% O2 + 100% nitrogen (N2), 5% O2 + 95% N2, 10% O2 + 90% N2, 15% O2 + 85% N2, and 20% O2 + 80% N2, and the effects of these storage conditions on large quantities of eggs were studied. The respiratory intensity and quality of eggs during storage were measured. We chose the weight loss rate of eggs, Haugh unit, pH, and the egg white total plate count as the characteristic indices of egg quality. We compared the changes in egg quality during and after storage at different O2 concentrations versus that at 25 °C. The stages were evaluated using the TOPSIS method to sort egg quality, and the optimal O2 concentration was selected. FLUENT was used to simulate and control the atmospheric requirements. Our findings showed that eggs stored in an air-conditioning chamber with O2 concentration ≤10% exhibited weak respiratory intensity (0–1 mg/(kg·h)). The rates of decrease in loss of egg weight and Haugh units were smaller. There were significant differences in the pH of egg white stored in different O2 concentrations (p < 0.05). Reducing the O2 concentration in the egg-storage environment reduced the number of colonies in eggs and had a positive effect on egg preservation. Simulations using FLUENT revealed that only 1200 s were required to achieve the low-oxygen environment in the controlled atmosphere box (1.5 m × 1 m × 1 m). The storage environment of 5% O2 + 95% N2 had the best preservation effect on eggs. This approach is associated with low costs in practical application and can potentially be used for egg storage and transport. [ABSTRACT FROM AUTHOR]

Variational mode decomposition (VMD) with a non-recursive and narrow-band filtering nature is a promising time-frequency analysis tool, which can deal effectively with a non-stationary and complicated compound signal. Nevertheless, the factitious parameter setting in VMD is closely related to its decomposability. Moreover, VMD has a certain endpoint effect phenomenon. Hence, to overcome these drawbacks, this paper presents a novel time-frequency analysis algorithm termed as improved adaptive variational mode decomposition (IAVMD) for rotor fault diagnosis. First, a waveform matching extension is employed to preprocess the left and right boundaries of the raw compound signal instead of mirroring the extreme extension. Then, a grey wolf optimization (GWO) algorithm is employed to determine the inside parameters ( α ^ , K) of VMD, where the minimization of the mean of weighted sparseness kurtosis (WSK) is regarded as the optimized target. Meanwhile, VMD with the optimized parameters is used to decompose the preprocessed signal into several mono-component signals. Finally, a Teager energy operator (TEO) with a favorable demodulation performance is conducted to efficiently estimate the instantaneous characteristics of each mono-component signal, which is aimed at obtaining the ultimate time-frequency representation (TFR). The efficacy of the presented approach is verified by applying the simulated data and experimental rotor vibration data. The results indicate that our approach can provide a precise diagnosis result, and it exhibits the patterns of time-varying frequency more explicitly than some existing congeneric methods do (e.g., local mean decomposition (LMD), empirical mode decomposition (EMD) and wavelet transform (WT)). [ABSTRACT FROM AUTHOR]

Tetrastigma hemsleyanum Diels et Gilg is a rare and wild medicinal resource. Metabolites, especially secondary metabolites, have an important influence on T. hemsleyanum adaptability and its medicinal quality. The metabolite proanthocyanidin (PA) is a polyphenol compound widely distributed in land plants, which can be used as antioxidants and anticancer agents. Here, we discovered that three types of PA accumulated in large amounts in purple leaves (PL), but not in green leaves (RG), based on widely non-targeted metabolomics. In addition, we further found that catechins and their derivatives, which are the structural units of PA, are also enriched in PL. Afterwards, we screened and obtained five key genes, DNR1/2, ANS, ANR and LAR closely related to PA biosynthesis through transcriptome analysis and found they were all highly expressed in PL compared to RG. Therefore, observed the regulatory relationship between the main compounds and genes network, and the PA metabolism regulatory pathway was complicated, which may be different to other species. [ABSTRACT FROM AUTHOR]