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Dimocarpus longan Lour. is an evergreen tree of the genus Longan in the Sapindaceae family, native to tropical and subtropical regions. Longan embryonic development is closely related to fruit set and fruit quality. An in-depth study of the mechanism of longan embryonic development could therefore contribute to the development of the longan industry. DIMBOA is the principal compound representing benzoxazinoids (BXs), and is closely linked to auxin biosynthesis and signal transduction. Auxin is one of the crucial hormones for inducing somatic embryogenesis (SE) in plants. Previous research has shown that DIMBOA promotes morphogenesis in the early somatic embryogenesis of longan, but the specific regulatory mechanism has not yet been clarified. To elucidate the molecular mechanism by which DIMBOA affects early somatic embryogenesis in longan, we chose longan embryogenic cultures grown under 0 mg/L DIMBOA as the control group (the check, CK), and longan embryogenic cultures grown under 0.1 mg/L DIMBOA as the treatment group (D) to be analyzed by transcriptomic sequencing. A total of 478 differentially expressed genes (DEGs) are detected in check vs. D, of which 193 are upregulated and 285 are downregulated. These DEGs are significantly enriched in the biosynthetic and metabolic functions of various substances such as vitamin B6 (VB6) biosynthesis, phenylpropanoid pathways, and carbohydrate metabolism. DIMBOA affects SE processes in longan via TFs, including MYB, ZF, bHLH, LBD, NAC, WRKY, etc. After DIMBOA treatment, the expression of most of the key genes for IAA synthesis was significantly downregulated, VB6 content was significantly reduced, and H2O2 content was significantly increased. Therefore, it is suggested that DIMBOA directly or indirectly affects the H2O2 content through the VB6 metabolic pathway, thereby regulating the endogenous IAA level to modulate the early SE morphogenesis of longan.

The pinhole plate is a key component of the point diffraction interferometer (PDI). The reasonable improvement and simulation of this device would enhance the application of point diffraction interferometry technology during the measurement of wavefronts. The traditional point diffraction interferometry measurement method is easily disturbed by environmental noise, making it difficult to obtain high-precision dynamic measurements. This paper introduces a four-step phase-shift PDI that can be employed in a common optical path. By using the principle of the finite-difference time-domain method (FDTD), a simulation model of the orthogonal polarization point diffraction pinhole plate (OP-PDPP) structure is established. The results show that when Cr is used as the film material in the pinhole plate, the parameters include a film thickness of 150 nm, a pinhole diameter of 2 μm, a wire grid period of 150 nm, and a wire grid width of 100 nm; in addition, the comprehensive extinction ratio of the pinhole plate is the greatest and the diffraction wavefront error is the smallest. Finally, the constructed experimental system is used to test the wavefront of a flat sample with a 25.4 mm aperture, and the test results are compared with those of the ZYGO interferometer. The difference in the peak-to-valley (PV) value between the OP-PDI and the ZYGO interferometer measurement is 0.0028λ, with an RMS value difference of 0.0011λ; this verifies the feasibility of the scheme proposed in this paper. The experimental results show that the proposed OP-PDPP is an effective tool for high-precision dynamic measurement.

Benzoxazinoids (BXs) are tryptophan-derived indole metabolites and play a role in various physiological processes, such as auxin metabolism. Auxin is essential in the process of somatic embryogenesis (SE) in plants. In this study, we used bioinformatics, transcriptome data, exogenous treatment experiments, and qPCR analysis to study the evolutionary pattern of Bx genes in green plants, the regulatory mechanism of DlBx genes during early SE, and the effect of 2,4-dihydroxy-7-methoxy-1,4-benzoxazine-3-one (DIMBOA) on the early SE in Dimocarpus longan Lour. The results showed that 27 putative DlBxs were identified in the longan genome; the Bx genes evolved independently in monocots and dicots, and the main way of gene duplication for the DlBx was tandem duplication (TD) and the DlBx were strongly constrained by purification selection during evolution. The transcriptome data indicated varying expression levels of DlBx during longan early SE, and most DlBxs responded to light, temperature, drought stress, and 2,4-dichlorophenoxyacetic acid (2,4-D) treatment; qRT-PCR results showed DlBx1, DlBx6g and DlBx6h were responsive to auxin, and treatment with 0.1mg/L DIMBOA for 9 days significantly upregulated the expression levels of DlBx1, DlBx3g, DlBx6c, DlBx6f, DlB6h, DlBx7d, DlBx8, and DlBx9b. The correlation analysis showed a significantly negative correlation between the expression level of DlBx1 and the endogenous IAA contents; DIMBOA significantly promoted the early SE and significantly changed the endogenous IAA content, and the IAA content increased significantly at the 9th day and decreased significantly at the 13th day. Therefore, the results suggested that DIMBOA indirectly promote the early SE by changing the endogenous IAA content via affecting the expression level of DlBx1 and hydrogen peroxide (H2O2) content in longan.

A low-voltage circuit breaker thermal trip plays a role in power systems, including opening and closing, control, protection, and more. Their reliability directly affects the security and stability of the power distribution system. The Wiener process model is established according to the accelerated degradation data of thermal trips, and remaining life prediction is carried out. In this paper, firstly, the constant stress accelerated degradation test is carried out on a thermal trip with temperature as the accelerated stress and specific thermal deflection as the degradation eigenquantity to characterise the degradation trajectory according to the degradation data and analyse the degradation law; then, it is verified by the test of normal distribution that the degradation data of the thermal trip conform to the Wiener process. The Wiener process, using the great likelihood estimation method to estimate the parameters of the remaining life, calculates the remaining life probability density function and reliability function under different temperature stresses and obtains the remaining life of the thermal trip under each accelerated stress conditions; finally, the remaining life of the thermal trip at the initial moment is taken as the pseudo-failure life, and the Arrhenius accelerated model is utilised to launch the external thermal trip under normal stress. Finally, the Arrhenius acceleration model is used to extrapolate the lifespan of the thermal trip under normal stress.

Dwarfing is one of the common phenotypic variations in asexually reproduced progeny of banana, and dwarfed banana is not only windproof and anti-fallout but also effective in increasing acreage yield. As a key gene in the strigolactone signalling pathway, DWARF53 (D53) plays an important role in the regulation of the height of plants. In order to gain insight into the function of the banana D53 gene, this study conducted genome-wide identification of banana D53 gene based on the M. acuminata, M. balbisiana and M. itinerans genome database. Analysis of MaD53 gene expression under high temperature, low temperature and osmotic stress based on transcriptome data and RT-qPCR was used to analyse MaD53 gene expression in different tissues as well as in different concentrations of GA and SL treatments. In this study, we identified three MaD53, three MbD53 and two MiD53 genes in banana. Phylogenetic tree analysis showed that D53 Musa are equally related to D53 Asparagales and Poales. Both high and low-temperature stresses substantially reduced the expression of the MaD53 gene, but osmotic stress treatments had less effect on the expression of the MaD53 gene. GR24 treatment did not significantly promote the height of the banana, but the expression of the MaD53 gene was significantly reduced in roots and leaves. GA treatment at 100 mg/L significantly promoted the expression of the MaD53 gene in roots, but the expression of this gene was significantly reduced in leaves. In this study, we concluded that MaD53 responds to GA and SL treatments, but “Yinniaijiao” dwarf banana may not be sensitive to GA and SL.

The DC circuit breaker is a crucial equipment for eliminating faults in DC transmission lines. The electromagnetic release functions as a critical component that restricts the circuit breaker’s lifespan. It is essential to prioritize its safety and reliability during usage and predicting its remaining useful life (RUL) is paramount. This paper proposes a new prediction technique based on particle filtering (PF) and the whale optimization algorithm (WOA) for the remaining useful life of electromagnetic release. The particle filtering algorithm is a commonly used technique in practical engineering fields such as target tracking and RUL prediction. It is a mainstream method for solving the parameter estimation of non-linear non-Gaussian systems. The WOA is introduced to improve the PF algorithm in order to ensure the diversity of particles and lessen the effect of particle degradation. The WOA replaces the traditional resampling process, and after each computation is finished, the weights of the particles in the particle set are reassigned in order to improve the particle distribution and increase algorithm accuracy. The rate of loss of the spring reaction force and the striker counterforce are chosen as the degradation characteristics after the degradation factors of electromagnetic release are analyzed. The degradation curves at different temperatures of electromagnetic release are obtained using the accelerated life test, and the full-life data during normal operation are derived using the Arrhenius equation. Finally, the RUL is predicted by comparing this paper’s method with the conventional PF method. The experimental results demonstrate that the method presented in this paper can more accurately predict the RUL of the electromagnetic release and has a higher prediction accuracy.

This paper contributes to the classification of flag-transitive symmetric 2-(v,k,λ) designs with λ prime. We investigate the structure of flag-transitive, point-quasiprimitive automorphism groups (G) of such 2-designs by applying the classification of quasiprimitive permutation groups. It is shown that the automorphism groups (G) have either an abelian socle or a non-abelian simple socle. Moreover, according to the classification of finite simple groups, we demonstrate that point-quasiprimitivity implies point-primitivity of G, except when the socle of G is PSLn(q).

Strain sensors based on conducting polymer hydrogels are considered highly promising candidates for wearable electronic devices. However, existing conducting polymer hydrogels are susceptible to aging, damage, and failure, which can greatly deteriorate the sensing performance of strain sensors based on these substances and the accuracy of data collection under large deformation. Developing conductive polymer hydrogels with concurrent high sensing performance and self-healing capability is a critical yet challenging task to improve the stability and lifetime of strain sensors. Herein, we design a self-healable conducting polymer hydrogel by compositing poly(3,4-ethylenedioxythiophene):polystyrene sulfonate (PEDOT:PSS) nanofibers and poly(vinyl alcohol) (PVA) via both physical and chemical crosslinking. This PEDOT:PSS-PVA nanocomposite hydrogel strain sensor displays an excellent strain monitoring range (>200%), low hysteresis (83.5%). Electronic skins based on such hydrogel strain sensors can perform the accurate monitoring of various physiological signals, including swallowing, finger bending, and knee bending. This work presents a novel conducting polymer hydrogel strain sensor demonstrating both high sensing performance and self-healability, which can satisfy broad application scenarios, such as wearable electronics, health monitoring, etc.

The volatile metabolites of Salvia sclarea, Rosmarinus officinalis, Thymus serpyllum, Mentha spicata, Melissa officinalis, Origanum majorana, Mentha piperita, Ocimum basilicum and Lavandula angustifolia were determined by gas chromatography–mass spectrometry. The vapor insecticidal properties of the analyzed essential oils and their compounds were screened using Reticulitermes dabieshanensis workers. The most effective oils were S. sclarea (major constituent linalyl acetate, 65.93%), R. officinalis (1,8-cineole, 45.56%), T. serpyllum (thymol, 33.59%), M. spicata (carvone, 58.68%), M. officinalis (citronellal, 36.99%), O. majorana (1,8-cineole, 62.29%), M. piperita (menthol, 46.04%), O. basilicum (eugenol, 71.08%) and L. angustifolia (linalool, 39.58%), which exhibited LC50 values ranging from 0.036 to 1.670 μL/L. The lowest LC50 values were recorded for eugenol (0.060 μL/L), followed by thymol (0.062 μL/L), carvone (0.074 μL/L), menthol (0.242 μL/L), linalool (0.250 μL/L), citronellal (0.330 μL/L), linalyl acetate (0.712 μL/L) and 1,8-cineole (1.478 μL/L). The increased activity of esterases (ESTs) and glutathione S-transferase (GST) were observed but only alongside the decreased activity of acetylcholinesterase (AChE) in eight main components. Our results indicate that S. sclarea, R. officinalis, T. serpyllum, M. spicata, M. officinalis, O. marjorana, M. piperita, O. basilicum and L. angustifolia essential oils (EOs) and their compounds, linalyl acetate, 1,8-cineole, thymol, carvone, citronellal, menthol, eugenol and linalool could be developed as control agents against termites.

Recently, advanced electrodes have been developed, such as semi-dry, dry contact, dry non-contact, and microneedle array electrodes. They can overcome the issues of wet electrodes and maintain high signal quality. However, the variations in these electrodes are still unclear and not explained, and there is still confusion regarding the feasibility of electrodes for different application scenarios. In this review, the physical features and electroencephalogram (EEG) signal performances of these advanced EEG electrodes are introduced in view of the differences in contact between the skin and electrodes. Specifically, contact features, biofeatures, impedance, signal quality, and artifacts are discussed. The application scenarios and prospects of different types of EEG electrodes are also elucidated.

Botanical pesticides are considered the most promising alternative to synthetic pesticides, considering their less negative impacts on the environment and human health. Here, we analyzed the components of lemongrass Cymbopogon citratus essential oil (EO) and evaluated its vapor activity against Reticulitermes flaviceps, in terms of the walking and gripping abilities of workers. In addition, the effects of lemongrass EO and its major component on the cuticular content and structure of chitin in termites were also observed. Our results indicate that cis-citral (36.51%) was the main constituent of lemongrass. In the vapor toxicity assay, the LC50 values of lemongrass EO and citral were 0.328 and 0.177 μL/L, respectively. When worker antennae were treated with lemongrass EO and citral, their walking and gripping capabilities were significantly inhibited. In addition, the cuticular content, thermal stability, and crystallinity of chitin in the termites were decreased after treatment with citral. Collectively, this study provides a basis for developing and utilizing lemongrass and citral as a new environment-friendly insecticide resource to control R. flaviceps.

The larch looper, Erannis ankeraria Staudinger (Lepidoptera: Geometridae), is one of the major insect pests of larch forests, widely distributed from southeastern Europe to East Asia. A naturally occurring baculovirus, Erannis ankeraria nucleopolyhedrovirus (EranNPV), was isolated from E. ankeraria larvae. This virus was characterized by electron microscopy and by sequencing the whole viral genome. The occlusion bodies (OBs) of EranNPV exhibited irregular polyhedral shapes containing multiple enveloped rod-shaped virions with a single nucleocapsid per virion. The EranNPV genome was 125,247 bp in length with a nucleotide distribution of 34.9% G+C. A total of 131 hypothetical open reading frames (ORFs) were identified, including the 38 baculovirus core genes and five multi-copy genes. Five homologous regions (hrs) were found in the EranNPV genome. Phylogeny and pairwise kimura 2-parameter analysis indicated that EranNPV was a novel group II alphabaculovirus and was most closely related to Apocheima cinerarium NPV (ApciNPV). Field trials showed that EranNPV was effective in controlling E. ankeraria in larch forests. The above results will be relevant to the functional research on EranNPV and promote the use of this virus as a biocontrol agent.

Object detection is a challenging computer vision task with numerous real-world applications. In recent years, the concept of the object relationship model has become helpful for object detection and has been verified and realized in deep learning. Nonetheless, most approaches to modeling object relations are limited to using the anchor-based algorithms; they cannot be directly migrated to the anchor-free frameworks. The reason is that the anchor-free algorithms are used to eliminate the complex design of anchors and predict heatmaps to represent the locations of keypoints of different object categories, without considering the relationship between keypoints. Therefore, to better fuse the information between the heatmap channels, it is important to model the visual relationship between keypoints. In this paper, we present a knowledge-driven network (KDNet)—a new architecture that can aggregate and model keypoint relations to augment object features for detection. Specifically, it processes a set of keypoints simultaneously through interactions between their local and geometric features, thereby allowing the modeling of their relationship. Finally, the updated heatmaps were used to obtain the corners of the objects and determine their positions. The experimental results conducted on the RIDER dataset confirm the effectiveness of the proposed KDNet, which significantly outperformed other state-of-the-art object detection methods.

Currently, soil-moisture data extracted from microwave data suffer from poor spatial resolution. To overcome this problem, this study proposes a method to downscale the soil moisture spatial resolution. The proposed method establishes a statistical relationship between low-spatial-resolution input data and soil-moisture data from a land-surface model based on a neural network (NN). This statistical relationship is then applied to high-spatial-resolution input data to obtain high-spatial-resolution soil-moisture data. The input data include passive microwave data (SMAP, AMSR2), active microwave data (ASCAT), MODIS data, and terrain data. The target soil moisture data were collected from CLDAS dataset. The results show that the addition of data such as the land-surface temperature (LST), the normalized difference vegetation index (NDVI), the normalized shortwave-infrared difference bare soil moisture indices (NSDSI), the digital elevation model (DEM), and calculated slope data (SLOPE) to active and passive microwave data improves the retrieval accuracy of the model. Taking the CLDAS soil moisture data as a benchmark, the spatial correlation increases from 0.597 to 0.669, the temporal correlation increases from 0.401 to 0.475, the root mean square error decreases from 0.051 to 0.046, and the mean absolute error decreases from 0.041 to 0.036. Triple collocation was applied in the form of [NN, FY3C, GEOS-5] based on the extracted retrieved soil-moisture data to obtain the error variance and correlation coefficient between each product and the actual soil-moisture data. Therefore, we conclude that NN data, which have the lowest error variance (0.00003) and the highest correlation coefficient (0.811), are the most applicable to Qinghai Province. The high-spatial-resolution data obtained from the NN, CLDAS data, SMAP data, and AMSR2 data were correlated with the ground-station data respectively, and the result of better NN data quality was obtained. This analysis demonstrates that the NN-based method is a promising approach for obtaining high-spatial-resolution soil-moisture data.

Kernel fuzzy c-means (KFCM) is a significantly improved version of fuzzy c-means (FCM) for processing linearly inseparable datasets. However, for fuzzification parameter m=1, the problem of KFCM (kernel fuzzy c-means) cannot be solved by Lagrangian optimization. To solve this problem, an equivalent model, called kernel probabilistic k-means (KPKM), is proposed here. The novel model relates KFCM to kernel k-means (KKM) in a unified mathematic framework. Moreover, the proposed KPKM can be addressed by the active gradient projection (AGP) method, which is a nonlinear programming technique with constraints of linear equalities and linear inequalities. To accelerate the AGP method, a fast AGP (FAGP) algorithm was designed. The proposed FAGP uses a maximum-step strategy to estimate the step length, and uses an iterative method to update the projection matrix. Experiments demonstrated the effectiveness of the proposed method through a performance comparison of KPKM with KFCM, KKM, FCM and k-means. Experiments showed that the proposed KPKM is able to find nonlinearly separable structures in synthetic datasets. Ten real UCI datasets were used in this study, and KPKM had better clustering performance on at least six datsets. The proposed fast AGP requires less running time than the original AGP, and it reduced running time by 76–95% on real datasets.

Clustering nonlinearly separable datasets is always an important problem in unsupervised machine learning. Graph cut models provide good clustering results for nonlinearly separable datasets, but solving graph cut models is an NP hard problem. A novel graph-based clustering algorithm is proposed for nonlinearly separable datasets. The proposed method solves the min cut model by iteratively computing only one simple formula. Experimental results on synthetic and benchmark datasets indicate the potential of the proposed method, which is able to cluster nonlinearly separable datasets with less running time.

The estimation of heart rate (HR) based on wearable devices is of interest in fitness. Photoplethysmography (PPG) is a promising approach to estimate HR due to low cost; however, it is easily corrupted by motion artifacts (MA). In this work, a robust approach based on random forest is proposed for accurately estimating HR from the photoplethysmography signal contaminated by intense motion artifacts, consisting of two stages. Stage 1 proposes a hybrid method to effectively remove MA with a low computation complexity, where two MA removal algorithms are combined by an accurate binary decision algorithm whose aim is to decide whether or not to adopt the second MA removal algorithm. Stage 2 proposes a random forest-based spectral peak-tracking algorithm, whose aim is to locate the spectral peak corresponding to HR, formulating the problem of spectral peak tracking into a pattern classification problem. Experiments on the PPG datasets including 22 subjects used in the 2015 IEEE Signal Processing Cup showed that the proposed approach achieved the average absolute error of 1.65 beats per minute (BPM) on the 22 PPG datasets. Compared to state-of-the-art approaches, the proposed approach has better accuracy and robustness to intense motion artifacts, indicating its potential use in wearable sensors for health monitoring and fitness tracking.