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Materials that generate structural color may be promising alternatives to dyes and pigments due to their relative long-term stability and environmentally benign properties. Liquid crystal (LC) mixtures of cholesteryl esters demonstrate structural color due to light reflected from the helical structure of the self-assembled molecules. The apparent color depends on the pitch length of the liquid crystal. While a wide range of colors have been achieved with such LC formulations, the nature of the pitch–concentration relationship has been difficult to define. In this work, various machine learning approaches to predict the reflected wavelength, i.e., the position of the selective reflection band, based on LC composition are compared to a Scheffe cubic model. The neural network regression model had a higher root mean squared error (RMSE) than the Scheffe cubic model with improved predictions for formulations not included in the dataset. Decision tree regression provided the best overall performance with the lowest RMSE and predicted position of the selective reflection band within 0.8% of the measured values for LC formulations not included in the dataset. The predicted values using the decision tree were over two-fold more accurate than the Scheffe cubic model. These results demonstrate the utility of machine learning models for predicting physical properties of LC formulations.

With the rapid advancement of mobile robotics technology, Simultaneous Localization and Mapping (SLAM) has become indispensable for enabling robots to autonomously navigate and construct maps of unknown environments in real time. Traditional SLAM algorithms, such as the Extended Kalman Filter (EKF) and FastSLAM, have shown commendable performance in certain applications. However, they encounter significant limitations when dealing with nonlinear systems and non-Gaussian noise distributions, especially in dynamic and complex environments coupled with high computational complexity. To address these challenges, this study proposes an enhanced particle filtering method leveraging particle swarm optimization (PSO) to improve the accuracy of pose estimation and the efficacy of map construction in SLAM algorithms. We begin by elucidating the foundational principles of FastSLAM and its critical role in empowering robots with the ability to autonomously explore and map unknown territories. Subsequently, we delve into the innovative integration of PSO with FastSLAM, highlighting our novel approach of designing a bespoke fitness function tailored to enhance the distribution of particles. This innovation is pivotal in mitigating the degradation issues associated with particle filtering, thereby significantly improving the estimation accuracy and robustness of the SLAM solution in various operational scenarios. A series of simulation experiments and tests were conducted to substantiate the efficacy of the proposed method across diverse environments. The experimental outcomes demonstrate that, compared to the standard particle filtering algorithm, the PSO-enhanced particle filtering effectively mitigates the issue of particle degeneration, ensuring reliable and accurate SLAM performance even in challenging, unknown environments.

Carbon dots (CDs) offer tremendous advantages in the fields such as bioimaging, sensing, biomedicine, catalysis, information encryption, and optoelectronics. However, the inherent challenge is synthesizing CDs with a full-spectrum emission, as most CDs typically produce only blue or green emissions, which severely hinder further investigation into their fluorescence mechanism and restrict their broader applications in light-emitting diodes (LEDs). In this work, we reported a solvent-controlled strategy for the preparation of multicolor CDs with blue, yellow, and red emissions, using o-phenylenediamine (oPD) and ionic liquid 1-butyl-3-methylimidazolium hexafluorophosphate (BmimPF6) as precursors. The detailed characterizations proved that a solvent with a lower boiling point and lower solubility of precursors resulted in a higher degree of dehydration and carbonization process, thereby increasing carbon cores with sp2-conjugated domains and nitrogen doping and further reducing the bandgap energies, causing a significant redshift emission from blue to red. The underlying fluorescence mechanism of the prepared multicolor CDs was contributed to the surface state. Eventually, blue-, yellow-, and red-emitting CDs based on poly(vinyl alcohol) (PVA) films and colorful LEDs devices were fabricated by dispersing the as-synthesized CDs into a PVA solution. The proposed solvent-controlled strategy for multicolor CDs preparation will be helpful for fully utilizing the advantages of CDs and expanding their applications.

During the developmental stages, eggplants are susceptible to diseases, which can impact crop yields and farmers’ economic returns. Therefore, timely and effective detection of eggplant diseases is crucial. Deep learning-based object detection algorithms can automatically extract features from images of eggplants affected by diseases. However, eggplant disease images captured in complex farmland environments present challenges such as varying disease sizes, occlusion, overlap, and small target detection, making it difficult for existing deep-learning models to achieve satisfactory detection performance. To address this challenge, this study proposed an optimized eggplant disease detection algorithm, YOLOv8-E, based on You Only Look Once version 8 nano (YOLOv8n). Firstly, we integrate switchable atrous convolution (SAConv) into the C2f module to design the C2f_SAConv module, replacing some of the C2f modules in the backbone network of YOLOv8n, enabling our proposed algorithm to better extract eggplant disease features. Secondly, to facilitate the deployment of the detection model on mobile devices, we reconstruct the Neck network of YOLOv8n using the SlimNeck module, making the model lighter. Additionally, to tackle the issue of missing small targets, we embed the large separable kernel attention (LSKA) module within SlimNeck, enhancing the model’s attention to fine-grained information. Lastly, we combined intersection over union with auxiliary bounding box (Inner-IoU) and minimum point distance intersection over union (MPDIoU), introducing the Inner-MPDIoU loss to speed up convergence of the model and raise detection precision of overlapped and occluded targets. Ablation studies demonstrated that, compared to YOLOv8n, the mean average precision (mAP) and F1 score of YOLOv8-E reached 79.4% and 75.7%, respectively, which obtained a 5.5% increment and a 4.5% increase, while also reducing the model size and computational complexity. Furthermore, YOLOv8-E achieved higher detection performance than other mainstream algorithms. YOLOv8-E exhibits significant potential for practical application in eggplant disease detection.

To address the issues of decreased detection accuracy, false detections, and missed detections caused by scale differences between near and distant targets and environmental factors (such as lighting and water waves) in surface target detection tasks for uncrewed vessels, the YOLOv8-MSS algorithm is proposed to be used to optimize the detection of water surface targets. By adding a small target detection head, the model becomes more sensitive and accurate in recognizing small targets. To reduce noise interference caused by complex water surface environments during the downsampling process in the backbone network, C2f_MLCA is used to enhance the robustness and stability of the model. The lightweight model SENetV2 is employed in the neck component to improve the model’s performance in detecting small targets and its anti-interference capability. The SIoU loss function enhances detection accuracy and bounding box regression precision through shape awareness and geometric information integration. Experiments on the publicly available dataset FloW-Img show that the improved algorithm achieves an mAP@0.5 of 87.9% and an mAP@0.5:0.95 of 47.6%, which are improvements of 5% and 2.6%, respectively, compared to the original model.

Pipeline maintenance technology based on pipeline intelligent plugging robot (PIPR) has become an effective method for failure accident prevention of high-pressure subsea oil and gas pipelines. However, during the plugging operation, the vortexes and pressure fluctuation are presented under turbulent flow field excitation, which may lead to vortex-induced vibration and failure of the plugging operation. Therefore, in order to ensure the reliability of pipeline plugging, the vibration characteristics are analyzed using numerical simulation, providing guidance on the structural optimization of PIPR’s end face. Firstly, the flow field characteristics under different PIPR’s end faces are investigated. Secondly, an experimental scheme is designed based on Latin Hypercube Sampling Design (LHSD) optimized by greedy strategy. A mathematical model of the end face’s parameters and pressure gradient is established using a back propagation (BP) neural network. Then, an improved whale optimization algorithm (IWOA) is proposed to optimize the end face’s parameters to minimize the pressure gradient of the flow field. Finally, the experimental study is performed to observe the turbulent flow field and pressure fluctuation to validate the optimization results. The results demonstrate that the PIPR’s end face has a great influence on the vortex-induced vibration response. After structural optimization, the average pressure gradient of the optimal PIPR’s end face has decreased by 84.69% and 54.55% before and after the plugging process, compared to the original end face. This study can provide a reference for pipeline plugging operations, which is significant for preventing pipeline failure accidents.

Polysaccharides have been assessed as a potential natural active component in Chinese herbal medicine with anti-inflammatory properties. However, the complex and indefinite structures of polysaccharides limit their applications. This study explains the structures and anti-inflammatory potentials of three neutral polysaccharides, RIP-A1 (Mw 1.8 × 104 Da), RIP-B1 (Mw 7.4 × 104 Da) and RIP-B2 (Mw 9.3 × 104 Da), which were isolated from the roots of Isatis indigotica Fort. with sequenced ultrafiltration membrane columns, DEAE-52 and Sephadex G-100. The planar structures and microstructures of RIP-A1, RIP-B1 and RIP-B2 were further determined by HPGPC, GC–MS, methylation analysis, FT-IR, SEM and AFM, in which the structure of RIP-A1 was elucidated in detail using 1D/2D NMR. The Raw 264.7 cells were used for the anti-inflammatory activity in vitro. The results showed that RIP-A1, RIP-B1 and RIP-B2 are all neutral polysaccharides, with RIP-A1 having the smallest Mw and the simplest monosaccharide composition of the three. RIP-A1 is mainly composed of Ara and Gal, except for a small quantity of Rha. Its main structure is covered with glycosidic linkages of T-α-Araf, 1,2-α-Rhap, 1,5-α-Araf, T-β-Galp, 1,2,4-α-Rhap, 1,3,5-α-Araf and 1,6-β-Galp with 0.33:0.12:1.02:0.09:0.45:11.41:10.23. RIP-A1 significantly inhibited pro-inflammatory cytokines (NO, TNF-α, IL-6 and IL-1β) and increased anti-inflammatory cytokines (IL-4) in LPS-stimulated RAW 264.7 cells. Moreover, RIP-A1 could significantly inhibit the mRNA expression of TNF-α, IL-6 and L-1β. It could also activate IKK, p65 and IκBα (the components of the NF-κB signaling pathway). In conclusion, the above results show the structural characterization and anti-inflammatory potentials of RIP-A1 as an effective natural anti-inflammatory drug.

The sampling technique is commonly used in research investigations to more accurately estimate data with greater precision, at a lower cost and in less time. In plant DUS (distinctness, uniformity, and stability) testing, many quantitative characteristic data usually need to be obtained through individual measurements. However, there is currently no scientific method for determining the appropriate sampling size. The minimum number of testing samples for DUS testing was calculated based on the theory of sample size in descriptive studies and was validated through simple random sampling. The results show that the quantitative characteristics for the edible mushroom Shiitake (Lentinula edodes) in DUS testing were uniform. The calculated results show that 10 fruiting bodies for a single measurement were sufficient. Furthermore, the outcomes of the random sampling revealed that the mean of 10 samples did not significantly differ from the mean of all data. When the sample size exceeded 10, Cohen’s kappa statistic suggested that the conclusion of distinctness was very close to the near-perfect agreement. Reducing the number of samples did not affect the uniform assessment. This study suggests that the theory of sample size in descriptive studies could be applied to calculate the minimum sample size in DUS testing, and for Shiitake DUS testing, measuring 10 fruiting bodies was sufficient.

In recent years, social media has emerged as an important channel for the dissemination of destination branding. Despite the fact that the dissemination of information through social media enables a broader audience to become acquainted with destinations, the dissemination process of trending events exhibits variances. Consequently, the precise impact of the underlying mechanisms that govern the spread of information on the efficacy of disseminating destination brand trending events remains ambiguous. In an endeavor to bridge this gap, an improved SEIR model was developed in this research to investigate the dynamic dissemination mechanisms and influencing factors of destination trending events within social media. The model was applied to simulate the diffusion mechanism of destinations’ trending events. The results show that during the dissemination process of destination trending events on social media, the proportion of users affected at different stages influences the ultimate effectiveness of information propagation. In light of these insights, this research proposes a social media trending event dissemination strategy to aid in enhancing the propagation efficiency of destination brands through existing resources.

In recent years, renewable energy has seen widespread application. However, due to its intermittent nature, there is a need to develop energy management systems for its scheduling and control. This paper introduces a multi-stage constraint-handling multi-objective optimization method tailored for resilient microgrid energy management. The microgrid encompasses diesel generators, energy storage systems, renewable energy sources, and various load types. The intelligent management of generators, batteries, switchable loads, and controllable loads ensures a reliable power supply for the critical loads. Beyond operational costs, our model also considers grid dependency as a key objective, making it particularly suited for energy management in extreme environments such as islands, border regions, and military bases. Managing complex controls of generators, batteries, switchable loads, and controllable loads presents challenging constraints that the management strategy must meet. To tackle this challenge, we propose an multi-objective optimization algorithm with multi-stage constraint-handling strategy to handle the high-dimensional complex constraints of the resilient energy management problem. Our proposed approach demonstrates superior performance compared to nine leading constrained multi-objective optimization algorithms across various test scenarios. Furthermore, the benefits of our method become increasingly evident as the complexity of the problem increases. Compared to the classical NSGA-II, the proposed NSGA-II-MC method achieved a 49.7% improvement in the Hypervolume metric on large-scale problems.

Seasonal (temporal) variations can influence the δ13C, δ2H, δ18O, and δ15N values and nutrient composition of organic (ORG), green (GRE), and conventional (CON) vegetables with a short growth cycle. Stable isotope ratio mass spectrometry (IRMS) and near-infrared spectroscopy (NIRS) combined with the partial least squares-discriminant analysis (PLS-DA) method were used to investigate seasonal effects on the identification of ORG, GRE, and CON Brassica chinensis L. samples (BCs). The results showed that δ15N values had significant differences among the three cultivation methods and that δ13C, δ2H, and δ18O values were significantly higher in winter and spring and lower in summer. The NIR spectra were relatively clustered across seasons. Neither IRMS-PLS-DA nor NIRS-PLS-DA could effectively identify all BC cultivation methods due to seasonal effects, while IRMS-NIRS-PLS-DA combined with Norris smoothing and derivative pretreatment had better predictive abilities, with an 89.80% accuracy for ORG and BCs, 88.89% for ORG and GRE BCs, and 75.00% for GRE and CON BCs. The IRMS-NIRS-PLS-DA provided an effective and robust method to identify BC cultivation methods, integrating multi-seasonal differences.

Mudstone compaction is widely used in the estimation of subsidence caused by sediment load, basin modeling, and overpressure genesis. The boundary for the depth at which chemical compaction in mudstones begins in the Xihu Depression is across two wells; in Well A, the depth is 2200 m, while in Well D, it is 1750 m. The porosity shows a gradual decreasing trend, followed by a sharp decrease and then an increase. Compared with Well A, the pores in Well D show a faster reduction (resulting in higher compaction) with increasing burial depth. The compaction process is controlled by clay mineral transformation and temperature increase. At a temperature range of 65–105 °C, local dissolution of quartz and pyrite, as well as precipitation of plagioclase, occur in Well D. The quartz, pyrite, and plagioclase contents in Well A do not significantly change, while K-feldspar corrosion and illitization are dominant. At a temperature range of 125–135 °C, diagenesis is reversed. The mixed layer increases across a low range, while chlorite and kaolinite contents increase; the dissolution of dolomite and the intermittent dissolution of calcite cause a local increase in pore size at 4460 m in Well A and 3300 m in Well D. The report values of geothermal gradient raise the depth limit of chemical compaction in Well D compared to that in Well A, thus accelerating the process of illitization and the cementation rate of quartz, and becoming the leading cause of the steeply decreasing trend of porosity in Well D. A compaction model for the Xihu Depression has also been established, which involves mechanical compaction, coexistence of chemical compaction and mechanical compaction, and chemical compaction. In the chemical compaction stage, the chemical/diagenetic compaction of mudstones locally increases the pore size. Moreover, abnormally enlarged pores became important reservoirs in the Xihu Depression.

Stem cell-derived exosomes (SC-Exos) are used as a source of regenerative medicine, but certain limitations hinder their uses. The effect of hydrolyzed collagen oligopeptides (HCOPs), a functional ingredient of SC-Exos is not widely known to the general public. We herein evaluated the combined anti-aging effects of HCOPs and exosomes derived from human umbilical cord mesenchymal stem cells (HucMSC-Exos) using a senescence model established on human skin fibroblasts (HSFs). This study discovered that cells treated with HucMSC-Exos + HCOPs enhanced their proliferative and migratory capabilities; reduced both reactive oxygen species production and senescence-associated β-galactosidase activity; augmented type I and type III collagen expression; attenuated the expression of matrix-degrading metalloproteinases (MMP-1, MMP-3, and MMP-9), interleukin 1 beta (IL-1β), and tumor necrosis factor-alpha (TNF-α); and decreased the expression of p16, p21, and p53 as compared with the cells treated with HucMSC-Exos or HCOPs alone. These results suggest a possible strategy for enhancing the skin anti-aging ability of HucMSC-Exos with HCOPs.

The fluctuation relation stands as a fundamental result in nonequilibrium statistical physics. Its derivation, particularly in the stationary state, places stringent conditions on the physical systems of interest. On the other hand, numerical analyses usually do not directly reveal any specific connection with such physical properties. This study proposes an investigation of such a connection with the fundamental ingredients of the derivation of the fluctuation relation for the dissipation, which includes the decay of correlations, in the case of heat transport in one-dimensional systems. The role of the heat baths in connection with the system’s inherent properties is then highlighted. A crucial discovery of our research is that different lattice models obeying the steady-state fluctuation relation may do so through fundamentally different mechanisms, characterizing their intrinsic nature. Systems with normal heat conduction, such as the lattice ϕ4 model, comply with the theorem after surpassing a certain observational time window, irrespective of lattice size. In contrast, systems characterized by anomalous heat conduction, such as Fermi–Pasta–Ulam–Tsingou-β and harmonic oscillator chains, require extended observation periods for theoretical alignment, particularly as the lattice size increases. In these systems, the heat bath’s fluctuations significantly influence the entire lattice, linking the system’s fluctuations with those of the bath. Here, the current autocorrelation function allows us to discern the varying conditions under which different systems satisfy with the fluctuation relation. Our findings significantly expand the understanding of the stationary fluctuation relation and its broader implications in the field of nonequilibrium phenomena.

Surface currents obtained from the high-frequency surface-wave radars (HFSWRs) installed along the coast of Fujian Province are utilized to characterize submesoscale eddies in the southwestern Taiwan Strait from 29 January to 26 March 2013. The algorithm based on vector geometry (VG) has been applied to datasets and a total of 414 (161 anticyclonic and 253 cyclonic eddies) were obtained. Eddies with both rotations had a relatively short lifespan (≤3.7 h), and their radii were in the range of 5–22.5 km. Eddies with a lifespan of over 30 minutes were more likely to occur north of the Taiwan Strait shoals and move eastward or northeastward. The deviation of moving directions of eddies with a moving distance of more than 20 km was within 18°. Moreover, eddies could hardly hold their original forms with cyclones extending in the east-west and compressing in the north-south direction, and anticyclones were the opposite. The vorticity and strain rate were proportional to the square of the energy intensity (EI). This study shows that the array HFSWRs have a strong capability to observe short-lived submesoscale eddies.

Two differently substituted pyrazole ligands have been investigated with regard to the topology of their Pt complexes: upon deprotonation, two mononuclear 1:2 PtII-pyrazole complexes—one of the sterically unhindered 4-Me-pzH and one of the bulky 3,5-tBu-pzH (pzH = pyrazole)—yield the corresponding 1:2 PtII-pyrazolato species; the former a triangular, trinuclear metallacycle (1), and the latter a dinuclear, half-lantern species (2) formed via the unprecedented cyclometallation of a butyl group. Stoichiometric oxidation of the colorless PtII2 complex produces the deep-blue, metal–metal bonded PtIII2 analog (3) with a rarely encountered unsymmetrical coordination across the Pt-Pt bond. All three complexes have been characterized by single crystal X-ray structure determination, 1H-NMR, IR, and UV-vis-NIR spectroscopic methods. The XPS spectra of the PtII2 and PtIII2 species are also reported. Density functional theory calculations were carried out to investigate the electronic structure, spectroscopic properties, and chemical bonding of the new complexes. The calculated natural population analysis charges and Wiberg bonding indices indicate a weak σ-interaction in the case of 2 and a formal Pt-Pt single bond in 3.

(1) Background: Periampullary neuroendocrine neoplasms (NENs) are rare tumors that lack a prognostic prediction model. We aimed to design comprehensive and effective nomograms to predict prognosis; (2) Methods: Univariate and multivariate Cox analyses were used to screen out significant variables for the construction of the nomograms. The discrimination and calibration of the nomograms were carried out using calibration plots, concordance indices (C-indices), and area under time-dependent receiver operating characteristic curves (time-dependent AUCs). Decision curve analysis (DCA) was used to compare the clinical applicability of the nomograms, TNM (Tumor- Node-Metastasis) stage, and SEER stage; (3) Results: The independent risk factors for overall survival (OS) and cancer-specific survival (CSS) of patients with periampullary NENs included age, tumor size, histology, differentiation, N stage, M stage, and surgery, which were used to construct the nomograms. The calibration curves and C-indices showed a high degree of agreement between the predicted and actual observed survival rates. The AUCs displayed good calibration and acceptable discrimination of the nomograms. Additionally, the DCA curves indicated that the nomograms showed better clinical applicability; (4) Conclusions: We developed and validated nomogram prognostic models for patients with periampullary NENs. The nomograms provided insightful and applicable tools to evaluate prognosis.

Maximizing rice yield potential has always been the focus of high-yield rice cultivation research. For high-yield rice cultivation and breeding, more research into the link between yield and yield components is essential. In this experiment, 38 rice varieties with different yield types and 185 rice varieties as materials were chosen. The relationships between yield and yield components were studied. The regulation effects of total nitrogen application rate (TNAR) on yield and yield components were observed. The results showed that (1) the grain yield of high-yield varieties was 189.3−195.6%, 76.1−77.7%, and 27.0−28.7% higher than that of super-low-yield, low-yield, and medium-yield varieties, respectively. Compared with rice varieties with other yield types, rice varieties with high-yield type have a higher total number of spikelets. (2) The spikelet number per panicle and total number of spikelets were significantly positively linked with grain yield, but significantly negatively correlated with filled grains and grain weight. (3) With an increase in TNAR (0−340 kg ha−1), the panicles, spikelet number per panicle, and total spikelets of rice varieties with different yield types increased gradually, and the filled grains and grain weight decreased gradually. The higher the TNAR, the more obvious the decrease in filled grains and grain weight. The grain yield of rice varieties with different yield types was the highest under the TNAR at 250 kg ha−1. The main factor contributing to its high yield was the substantial increase in total spikelets. The above results showed that increasing the spikelet number per panicle and total spikelets played a material role in improving rice yield.

Cnaphalocrocis medinalis (Lepidoptera: Crambidae) is a migratory insect pest on rice crops. The migratory C. medinalis population in a particular location may be immigrants, local populations, emigrants, or a mix of these. Immigrants are strongly attracted to plant odor. We conducted research to identify the olfactory receptors in a floral scent mixture that is strongly attractive to C. medinalis. Through gene cloning, 12 olfactory receptor (OR) genes were amplified and expressed in Xenopus oocytes in vitro, and three of them were found to be responsive to plant foliar and floral volatiles. These were CmedOR31, a specific receptor for geraniol; CmedOR32, a broad-spectrum OR gene that responded to both foliar and floral odors; and CmedOR1, which strongly responded to 10−4 M phenylacetaldehyde. The electrophysiological response to phenylacetaldehyde was extremely high, with a current of 3200 ± 86 nA and an extremely high sensitivity. We compared the phylogenetic tree and sequence similarity of CmedOR genes and found that CmedOR1 belonged to a uniquely conserved OR pedigree in the evolution of Glossata species, and the ORs of this pedigree strongly responded to phenylacetaldehyde. The expression of OR1 was significantly higher in the females than in the males. Localization of CmedOR1 in the antennae of C. medinalis by fluorescence in situ hybridization showed that CmedOR1 was expressed in both males and females. CmedOR1 may be an odor receptor used by females to locate food sources. The function of these ORs and their role in pest monitoring were discussed.

Drought is one of the most detrimental environmental factors restricting the growth of wheat (Triticum aestivum L.). The investigation of the impact of uniconazole on carbon metabolism in wheat seedlings under drought stress could provide new insights into wheat stress physiology and tolerance. The effects of uniconazole (30 mg L−1) on wheat drought tolerance were investigated via a physiological analysis of the wheat genotypes ‘Wansu 1510’ (WS1510) and ‘Huacheng wheat 1688’ (HC1688) under a 15% polyethylene glycol (PEG) and 30% PEG treatment and a transcriptome analysis of ‘Wansu 1510’ (WS1510) under a 30% PEG treatment. The results revealed that uniconazole significantly increased the leaf relative water content (RWC), reduced plant height, and counteracted the reduction in fresh weight and root length under drought stress. It inhibited the excessive accumulation of reactive oxygen species (ROS) and protected against membrane lipid peroxidation caused by drought stress by regulating superoxide dismutase (SOD) gene expression, enhancing antioxidant enzymes activities, and adjusting the content of osmoregulatory compounds in drought-stressed plants. Furthermore, uniconazole treatment increased chlorophyll (Chl) and carotenoid (Car) contents, inhibited the increase in sucrose concentration, and alleviated the reduction in starch content due to increased sucrose synthase (SS) activity under drought stress. Transcriptome sequencing revealed that uniconazole regulated the expression of genes associated with starch and sucrose metabolism, porphyrin and chlorophyll metabolism, the photosynthetic antenna proteins, carotenoid biosynthesis, and carbon fixation in photosynthetic organisms, which are involved in carbon metabolism processes and photosynthetic pigment production and which regulate the conversion of sucrose and starch under drought stress. Our findings emphasize the importance of exogenous uniconazole in regulating carbon metabolism in wheat.

Studies on many dense correspondence tasks in the field of computer vision attempt to find spatially smooth results. A typical way to solve these problems is by smoothing the matching costs using edge-preserving filters. However, local filters generate locally optimal results, in that they only take the costs over a small support window into account, and non-local filters based on a minimum spanning tree (MST) tend to overuse the piece-wise constant assumption. In this paper, we propose a linear time non-local cost aggregation method based on two complementary spatial tree structures. The geodesic distances in both the spatial and intensity spaces along the tree structures are used to evaluate the similarity of pixels, and the final aggregated cost is the sum of the outputs from these two trees. The filtering output of a pixel on each tree can be obtained by recursively aggregating the costs along eight sub-trees with linear time complexity. The only difference between the filtering procedures on these two spatial tree structures is the order of the filtering. Experimental results in optical flow estimation and stereo matching on the Middlebury and KITTI datasets demonstrate the effectiveness and efficiency of our method. It turns out that our method outperforms typical non-local filters based on the MST in cost aggregation. Moreover, a comparison of handcrafted features and deep features learned by convolutional neural networks (CNNs) in calculating the matching cost is also provided. The code will be available soon.

Soil-derived dust emissions have been widely associated with health and environmental problems and should therefore be accurately and reliably estimated and assessed. Of these emissions, the inhalable PM10 and PM2.5 are difficult to estimate. Consequently, to calculate PM10 and PM2.5 emissions from soil erosion, an approach based on an artificial neural network (ANN) model which provides a multilayered, fully connected framework that relates input parameters and outcomes was proposed in this study. Owing to the difficulty in obtaining the actual emissions of soil-derived PM10 and PM2.5 over a broad area, the PM10 and PM2.5 simulated results of the ANN model were compared with the published results simulated by the widely used wind erosion prediction system (WEPS) model. The PM10 and PM2.5 emission results, based on the WEPS, agreed well with the field data, with R2 values of 0.93 and 0.97, respectively, indicating the potential for using the WEPS results as a reference for training the ANN model. The calculated r, RMSE and MAE for the results simulated by the WEPS and ANN were 0.78, 3.37 and 2.31 for PM10 and 0.79, 1.40 and 0.91 for PM2.5, respectively, throughout Southern Xinjiang. The uncertainty of the soil-derived PM10 and PM2.5 emissions at a 95% CI was (−66–106%) and (−75–108%), respectively, in 2016. The results indicated that by using parameters that affect soil erodibility, including the soil pH, soil cation exchange capacity, soil organic content, soil calcium carbonate, wind speed, precipitation and elevation as input factors, the ANN model could simulate soil-derived particle emissions in Southern Xinjiang. The results showed that when the study domain was reduced from the entire Southern Xinjiang region to its five administrative divisions, the performance of the ANN improved, producing average correlation coefficients of 0.88 and 0.87, respectively, for PM10 and PM2.5. The performances of the ANN differed by study period, with the best result obtained during the sand period (March to May) followed by the nonheating (June to October) and heating periods (November to February). Wind speed, precipitation and soil calcium carbonate were the predominant input factors affecting particle emissions from wind erosion sources. The results of this study can be used as a reference for the wind erosion prevention and soil conservation plans in Southern Xinjiang.

Replanting is a widely used method for improving the health and carbon sequestration capacity of degraded forests. However, its impact on soil carbon pools remains controversial. This study investigated the effects of replanting broadleaf Phoebe bournei (Hemsl.) Yang in a typical degraded fir forest. Soil carbon content, nutrient levels, and microbial community structure and function were measured at 0, 5, 8, and 12 years after replanting. The degraded fir forests were originally limited in nitrogen and phosphorus. Phoebe bournei replanting significantly increased soil total carbon but reduced total nitrogen and phosphorus levels, resulting in increased soil carbon:nitrogen, carbon:phosphorus, and nitrogen:phosphorus ratios. Microbial biomass carbon, nitrogen, and phosphorus were all significantly reduced, whereas microbial carbon:phosphorus and nitrogen:phosphorus ratios were enhanced. Enzyme activities related to nutrient cycling and carbon decomposition (acidic invertase, polyphenol oxidase, peroxidase, urase, nitrate reductase, and acidic phosphatase activities) were significantly lowered by replanting. Microbial richness and diversity significantly increased, and microbial community composition changed significantly due to replanting. Structural equation modeling revealed the significant role of total phosphorus in microbial biomass, microbial community composition, and enzyme activity, highlighting it as the main factor accelerating soil carbon accumulation. Network analysis identified Leifsonia, Bradyrhizobium, and Mycolicibacterium members as key microbial players in the soil carbon cycle. In summary, P. bournei replanting exacerbated soil phosphorus deficiency, leading to a decrease in soil microbial biomass and changes in community structure, reduced nutrient cycling and carbon-decomposition-related enzyme activities, less litter decomposition, and increased organic carbon accumulation. These findings demonstrate the importance of nutrient limitation in promoting soil carbon accumulation and offer new insights for soil carbon regulation strategies in forestry.

Pericarp color is a crucial commercial trait influencing consumer preferences for bitter gourds. However, until now, the gene responsible for this trait has remained unidentified. In this study, we identified a gene (McAPRR2) controlling pericarp color via a genome-wide association study (GWAS) utilizing the resequencing data of 106 bitter gourd accessions. McAPRR2 exhibits three primary haplotypes: Hap1 is a wild type with a green pericarp, Hap2 is a SA (South Asian) and SEA (Southeast Asia) type with a green pericarp, and Hap3 is primarily a SEA type with a light green pericarp. The McAPRR2 haplotype is significantly correlated with both pericarp color and ecological type. Importantly, McAPRR2 with the light green pericarp demonstrated premature termination due to a 15 bp sequence insertion. The phylogenetic tree clustered according to pericarp color and ecological type, using SNPs located in the McAPRR2 gene and its promoter. High πwild/SEA and πSA/SEA values indicate high nucleotide diversity between wild and SEA types and between SA and SEA types in the McAPRR2 gene. The haplotypes, phylogenetic tree, and nucleotide diversity of McAPRR2 suggest that McAPRR2 has undergone domestication selection. This study identifies McAPRR2 as the key gene determining pericarp color in bitter gourds and introduces a novel insight that McAPRR2 is subject to domestication selection.

Thermoelectric materials are widely used in refrigeration chips, thermal power generation, catalysis and other fields. Mg3Bi2-based thermoelectric material is one of the most promising thermoelectric materials. Herein, the Mg3Bi2-based samples were prepared by high temperature synthesis, and the influence of Mg/Sb content on the electrical transport properties and semi-conductivity/semi-metallicity of the materials has been studied. The results indicate that the efficiency of introducing electrons from excess Mg prepared by high temperature synthesis is lower than that introduced by ball milling, due to the high vapor pressure of Mg. The doping of Sb/Te at the Bi site would make it easier for the material to change from p-type conduction to n-type conduction. With the increase in Mg content, the semi-conductivity of the material becomes weaker, the semi-metallicity becomes stronger, and the corresponding conductivity increases. With the increase in Sb content, the samples exhibit the opposite changes. The highest power factor of ~1.98 mWm−1K−2 is obtained from the Mg3.55Bi1.27Sb0.7Te0.03 sample.

Multilayer films can achieve advanced properties and a wide range of applications. The heterogeneous interface plays an important role in the performances of multilayer films. In this paper, the effects of the interface of Ba(Mg1/3Ta2/3)O3/PbZr0.52Ti0.48O3 (BMT/PZT) thin films on dielectric behavior and temperature stability are investigated. The heterogeneous interface structures are characterized by Auger electron spectroscopy (AES). The PZT-BMT interface is different from the BMT-PZT interface in thickness. For the PZT-BMT interface, the PZT thin films are diffused to the whole BMT layers, and the interface thickness is about 90 nm, while the BMT-PZT interface’s thickness is only about 8.6 nm. The presence of heterogeneous interfaces can improve the performances of BMT/PZT thin films and expand their applications. The dielectric constant of BBPP thin films is significantly lower than BPBP thin films, while the dielectric loss is exactly the opposite. The more interfaces there are, the greater the dielectric constant. The relationship between the electric-field-dependent dielectric constant curve and the P-E curve is established. The equivalent interface barrier of the diode is used to show that the dielectric peaks under the positive and negative voltage are different. Similarly, heterogeneous interfaces show a certain improvement in dielectric tunability and temperature stability.

Masson pine (Pinus massoniana Lamb.) is an important resin-producing conifer species in China. Resin yield is a highly heritable trait and varies greatly among different genotypes. However, the mechanisms regulating the resin yield of masson pine remain largely unknown. In this study, physiological, proteomic, and gene expression analysis was performed on xylem tissues of masson pine with high and low resin yield. Physiological investigation showed that the activity of terpene synthase, as well as the contents of soluble sugar, jasmonic acid (JA), methyl jasmonate (MeJA), gibberellins (GA1, GA4, GA9, GA19, and GA20), indole-3-acetic acid (IAA), and abscisic acid (ABA) were significantly increased in the high yielder, whereas sucrose and salicylic acid (SA) were significantly decreased compared with the low one. A total of 2984 differentially expressed proteins (DEPs) were identified in four groups, which were mainly enriched in the biosynthesis of secondary metabolites, protein processing in the endoplasmic reticulum, carbohydrate metabolism, phytohormone biosynthesis, glutathione metabolism, and plant-pathogen interaction. Integrated physiological and proteomic analysis revealed that carbohydrate metabolism, terpenoid biosynthesis, resistance to stress, as well as JA and GA biosynthesis and signaling, play key roles in regulating resin yield. A series of proteins associated with resin yield, e.g., terpene synthase proteins (TPSs), ATP-binding cassette transporters (ABCs), glutathione S-transferase proteins (GSTs), and heat shock proteins (HSPs), were identified. Resin yield-related gene expression was also associated with resin yield. Our study unveils the implicated molecular mechanisms regulating resin yield and is of pivotal significance to breeding strategies of high resin-yielding masson pine cultivars.

BiCuSeO has great application prospects in thermoelectric power generation and thermoelectric catalysis, but it is limited by its lower thermoelectric performance. Herein, BiCuSeO bulk materials were prepared using a solid-phase reaction method and a ball-milling method combined with spark plasma sintering, and then the thermoelectric properties were improved by synergistically increasing carrier concentration and mobility. Al was adopted to dope into the BiCuSeO matrix, aiming to adjust the carrier mobility through energy band adjustment. The results show that Al doping would widen the bandgap and enhance the carrier mobility of BiCuSeO. After Al doping, the thermoelectric properties of the material are improved in the middle- and high-temperature range. Based on Al doping, Pb is adopted as the doping element to dope BiCuSeO to modify the carrier concentration. The results show that Al/Pb dual doping in the BiCuSeO matrix can increase the carrier concentration under the premise of increasing carrier mobility. Therefore, the electrical conductivity of BiCuSeO can be improved while maintaining a large Seebeck coefficient. The power factor of Al/Pb doping reached ~7.67 μWcm−1K−2 at 873 K. At the same time, the thermal conductivity of all doped samples within the test temperature range maintained a low level (−1K−1). Finally, the ZT value of the Al/Pb-doped BiCuSeO reached ~1.14 at 873 K, which is ~2.72 times that of the pure phase, and the thermoelectric properties of the matrix were effectively improved.

In this paper, the anti-cancer activity and molecular mechanisms of the isomers of AD-1 and AD-2 (20(R)-AD-1, 20(R)-AD-2, 20(S)-AD-1 and 20(S)-AD-2) were investigated. The results indicated that all of the four compounds obviously suppressed the viability of various cancer cells, and the anti-cancer activity of 20(R)-AD-1 and 20(R)-AD-2 was significantly better than 20(S)-AD-1 and 20(S)-AD-2, especially for gastric cancer cells (BGC-803). Then, the differences in the anti-cancer mechanisms of the isomers were investigated. The data showed that 20(R)-AD-1 and 20(R)-AD-2 induced apoptosis and decreased MMP, up-regulated the expression of cytochrome C in cytosol, transferred Bax to the mitochondria, suppressed oxidative phosphorylation and glycolysis and stimulated reactive oxygen species (ROS) production. Apoptosis can be attenuated by the reactive oxygen species scavenger N-acetylcysteine. However, 20(S)-AD-1 and 20(S)-AD-2 barely exhibited the same results. The results indicated that 20(R)-AD-1 and 20(R)-AD-2 suppressed cellular energy metabolism and caused apoptosis through the mitochondrial pathway, which ROS generation was probably involved in. Above all, the data support the development of 20(R)-AD-1 and 20(R)-AD-2 as potential agents for human gastric carcinoma therapy.

The rapid growth of renewable energy sources and the expanding market for electric vehicles (EVs) have escalated the demand for safe lithium-ion batteries (LIBs) with excellent performance. But the limitations of safety issues and energy density for LIBs continue to be obstacles to their future use. Recently, single-ion covalent-organic-framework-based (COF-based) solid electrolytes have emerged as a promising avenue to address the limitations of traditional liquid electrolytes and enhance the performance of LIBs. COFs have a porous structure and abundant electron-donating groups, enabling the construction of an available ionic conductive network. So, COFs are the subject of extensive and in-depth investigation, especially in terms of the impacts their adjustable porous structure and tunable chemistry on the research of ionic transport thermodynamics and transport kinetics. In this perspective, we present a comprehensive and significant overview of the recent development progress of single-ion COF-based solid electrolytes, highlighting their rare performance and potential applications in solid lithium batteries. This review illustrates the merits of single-ion conducting solid electrolytes and single-ion COF conductor-based solid electrolytes. Furthermore, the properties of anionic, cationic, and hybrid single-ion COF-based conducting electrolytes are discussed, and their electrochemical performance is also compared when applied in Li-ion batteries. Finally, to solve challenges in COF-based Li-ion batteries, strategies are provided to obtain a high lifespan, rate performance, and stable and safe batteries. This work is promising to offer valuable insights for researchers and the energy storage industry.

Storage duration detection for frozen meat, especially meat exceeding the state reserve time several times, has always been a big challenge in food safety inspection. Under long freezing times, the physical and chemical properties of meat change complexly. In this paper, the SS-OCT detection method under a low-frequency electric field is firstly (to our knowledge) applied to the predict storage durations of long-expired frozen meat. The average normalized cross-correlation (ANCC) is put forward as a comprehensive parameter to reflect both the electric–kinetic and optical properties of meat’s biological changes. A monotonically increasing inversion rule between ANCC and the storage duration of frozen meat is found after investigating 3840 pork samples, the frozen storage durations of which were from 1 to 13 months. To verify the correctness and accuracy of our method, nine groups of long-expired frozen pork samples were investigated. The maximum relative error for their storage durations is less than 5.71%, which means that our SS-OCT method under a low-frequency electric field is promising in providing a rapid on-site storage duration detection method without any complicated laboratory pretreatments for food safety inspection.

Low-threshold lasing under pulsed optical pumping is demonstrated in GaN-based microrod cavities at room temperature, which are fabricated on the patterned sapphire substrates (PSS). Because the distribution of threading dislocations (TDs) is different at different locations, a confocal micro-photoluminescence spectroscopy (μ-PL) was performed to analyze the lasing properties of the different diameter microrods at the top of the triangle islands and between the triangle islands of the PSS substrates, respectively. The μ-PL results show that the 2 μm-diameter microrod cavity has a minimum threshold of about 0.3 kW/cm2. Whispering gallery modes (WGMs) in the microrod cavities are investigated by finite-difference time-domain simulation. Combined with the dislocation distribution in the GaN on the PSS substrates, it is found that the distribution of the strongest lasing WGMs always moves to the region with fewer TDs. This work reveals the connection between the lasing modes and the dislocation distribution, and can contribute to the development of low-threshold and high-efficiency GaN-based micro-lasers.

The Jiaodong Peninsula is located on the junction of the North China Craton (NCC) and South China Block (SCB), where Mesozoic igneous rocks are widespread. However, the petrogenesis and tectonic settings for these Mesozoic igneous rocks are still controversial. In this study, we present detailed geochronological and geochemical analyses of quartz monzonite, monzogranite, syenogranite, and alkali feldspar granite in the Qingdao area, east of the Jiaodong Peninsula, to constrain their petrogenesis and tectonic setting. Zircon U–Pb dating shows that they mainly formed in the Early Cretaceous (120.5–113.1 Ma). Quartz monzonite exhibits adakitic geochemical features (e.g., low Y and high Sr/Y). Combined with its Sr–Nd–Hf isotopic features, we suggest that quartz monzonite may have been produced by the partial melting of phengite-bearing eclogites at the base of the thickened continental crust of the NCC. In contrast, monzogranite and syenogranite exhibit I-type granite affinities, whereas alkali feldspar granite exhibits features consistent with A-type granite. The strongly negative εHf(t) and εNd(t) values of the I-type rocks indicate that they were most likely produced through partial melting of granitic gneisses from the NCC, whereas A-type magmas may be formed through fractional crystallization from the non-adakitic granitic magma. Combined with previous studies, we suggest that these granitoids were formed in a lithospheric extensional setting via the rollback of the subducted Paleo-Pacific slab, which resulted in the reworking of the deep crust beneath the Sulu ultrahigh-pressure metamorphic belt.

Driven by technological advances from Industry 4.0, Healthcare 4.0 synthesizes medical sensors, artificial intelligence (AI), big data, the Internet of things (IoT), machine learning, and augmented reality (AR) to transform the healthcare sector. Healthcare 4.0 creates a smart health network by connecting patients, medical devices, hospitals, clinics, medical suppliers, and other healthcare-related components. Body chemical sensor and biosensor networks (BSNs) provide the necessary platform for Healthcare 4.0 to collect various medical data from patients. BSN is the foundation of Healthcare 4.0 in raw data detection and information collecting. This paper proposes a BSN architecture with chemical sensors and biosensors to detect and communicate physiological measurements of human bodies. These measurement data help healthcare professionals to monitor patient vital signs and other medical conditions. The collected data facilitates disease diagnosis and injury detection at an early stage. Our work further formulates the problem of sensor deployment in BSNs as a mathematical model. This model includes parameter and constraint sets to describe patient body characteristics, BSN sensor features, as well as biomedical readout requirements. The proposed model’s performance is evaluated by multiple sets of simulations on different parts of the human body. Simulations are designed to represent typical BSN applications in Healthcare 4.0. Simulation results demonstrate the impact of various biofactors and measurement time on sensor selections and readout performance.

The application of fog Internet of Things (IoT) technology helps solve the problem of weak computing power faced by IoT terminals. Due to asymmetric differences in communication methods, sensing data offloading from IoT terminals to fog and cloud layers faces different security issues, and both processes should be protected through certain data transmission protection measures. To take advantage of the relative asymmetry between cloud, fog, and sensing layers, this paper considers using physical layer security technology and encryption technology to ensure the security of the sensing data unloading process. An efficient resource allocation method based on deep reinforcement learning is proposed to solve the problem of channel and power allocation in fog IoT scenarios, as well as the selection of unloading destinations. This problem, which is NP-hard, belongs to the attribute of mixed integer nonlinear programming. Meanwhile, the supporting parameters of the method, including state space, action space, and rewards, are all adaptively designed based on scene characteristics and optimization goals. The simulation and analysis show that the proposed method possesses good convergence characteristics. Compared to several heuristic methods, the proposed method reduces latency by at least 18.7% on the premise that the transmission of sensing data is securely protected.

In this paper, scouring shear failure theory is optimized based on the gangue slope near the thermal power field in Baiguo Town, Panzhou City, Guizhou Province. Based on the particle flow PFC (particle flow code) 3D fluid–solid coupling method, the scouring failure mechanism of ditch no. 5 of the gangue slope is comprehensively analyzed from the perspectives of the failure mode, displacement, motion track, and stress–strain. We consider the scouring shear theory in respect of (c, φ); this theory is dominated by two types of scouring intensity factors and can effectively explain the internal mechanisms of gully formation. The rainfall scouring failure of gangue slopes can be divided into four stages: (1) integral splash erosion and local pitting at the bottom of the slope; (2) erosion diversion and pitting in the slope; (3) the tributary–slope crest extension schist erosion stage; and (4) integral gully erosion and the landslide stage. The failure process is not only characterized by discontinuous failure but also occurs in the order of bottom–middle–branch–top. A three-section stepped effect is observed during the process in which the gangue is scoured and destroyed, which fully verifies the intermittent characteristics of the scouring and destruction of gangue slopes. During the whole process, the maximum displacement is concentrated at the top of the slope, and its proportions are as follows: top of the slope > tributary > middle of the slope > foot of the slope. The peak displacement of the slope crest in the horizontal Y-direction accounts for 41.76%, and that in the Z-direction accounts for 45.84%. Scouring deposits can be divided into the arc erosion deposit mode and the sector erosion deposit mode. Mainstream gullies primarily control whether deposits are characterized as arc or straight erosion deposits. The later stage of the second phase of scouring is the incubation period of the tributary gully. The large accumulation makes the stress at the bottom of the slope increase sharply, and the fluctuation value is between 2 and 6.8 MPa. The generation of the branch notch is mainly determined by X-direction stress, and 8.6 MPa is the critical stress. In efforts to prevent and control rainfall and landslide, the slope foot area should be preferentially protected, and the soil mass in the slope should be reduced to prevent the maximum energy fluctuation caused by scouring, so as to prevent significant displacement damage of the slope top.

In this paper, we propose a novel method for planning grinding trajectories on curved surfaces to improve the grinding efficiency of large aluminum alloy surfaces with welds and defect areas. Our method consists of three parts. Firstly, we introduce a deficiency positioning method based on a two-dimensional image and three-dimensional point cloud, which enables us to accurately and quickly locate the three-dimensional defective areas. Secondly, we propose a 2D weld positioning method based on the defect area and obtain the spatial position of the 3D weld by combining the relationship between 2D and 3D images. Additionally, we propose an orthogonal projection method from the point cloud to the aluminum alloy surface to calculate the weld reinforcement. Thirdly, we present a space spiral grinding trajectory planning method for complex curved surfaces based on the characteristics of the weld reinforcement, spatial position, and spatial position information of the defect area. This method shortens the grinding time of the defect area and improves efficiency. Simulation and experimental results show that our grinding trajectory planning method is more efficient than other grinding methods in removing defects from the surface of aluminum alloys. Moreover, the defect area after grinding is smoother than before.

Meiyu-front rainstorm is one of the main disastrous weather events in summer in East China. In this study, seven assimilation experiments of multi-type observation data such as wind profile data, microwave radiometer data and radiosonde sounding data are designed to forecast the Meiyu-front rainstorm on 15 June 2020. The results show that the seven experiments can basically simulate the orientation of rain belt. The comprehensive experiment which assimilates all types of observations performs the best in simulating the location of heavy rainstorm and shows good performance in simulating the precipitation above moderate rain. For the comprehensive experiment, the forecast deviation of rainstorm and heavy rainstorm is small, and the equitable threat score has also been greatly improved compared with other experiments. It is found that the convective available potential energy is enhanced after the assimilation of surface observation data. In addition, the wind convergence and water vapor transportation are modified after assimilating wind profile data. Accordingly, the precipitation efficiency is improved in the comprehensive experiment. The profiles of pseudo-equivalent potential temperature, vorticity and divergence show that, the assimilation of new-types observation data from wind profiler radar and microwave radiometer increases the instability of atmospheric stratification and enhances the ascending motion in the heavy precipitation center. The above results show that the introduction of various some new-type data before the numerical simulation can reduce the forecast deviation. In addition, the combined assimilation of microwave radiometer and sounding data presents better performance than single data assimilation, which indicates that data mutual complementation is essential to improving forecast accuracy.

The species belonging to the Rhododendron genus are well-known for their colorful corolla. Molecular marker systems have the potential to elucidate genetic diversity as well as to assess genetic fidelity in rhododendrons. In the present study, the reverse transcription domains of long terminal repeat retrotransposons were cloned from rhododendrons and used to develop an inter-retrotransposon amplified polymorphism (IRAP) marker system. Subsequently, 198 polymorphic loci were generated from the IRAP and inter-simple sequence repeat (ISSR) markers, of which 119 were derived from the IRAP markers. It was shown that in rhododendrons, IRAP markers were superior to the ISSRs in some polymorphic parameters, such as the average number of polymorphic loci (14.88 versus 13.17). The combination of the IRAP and ISSR systems was more discriminative for detecting 46 rhododendron accessions than each of the systems on their own. Furthermore, IRAP markers demonstrated more efficiency in genetic fidelity detection of in-vitro-grown R. bailiense Y.P.Ma, C.Q.Zhang and D.F.Chamb, an endangered species recently recorded in Guizhzhou Province, China. The available evidence revealed the distinct properties of IRAP and ISSR markers in the rhododendron-associated applications, and highlighted the availability of highly informative ISSR and IRAP markers in the evaluation of genetic diversity and genetic fidelity of rhododendrons, which may facilitate preservation and genetic breeding of rhododendron plants.

Background: Granulomatosis with polyangiitis (GPA) is an antineutrophil-cytoplasmic-antibody (ANCA)-associated small-vessel vasculitis characterized by necrotizing granulomatous inflammation. Symptoms of skin involvement can appear in 30–50% of patients with GPA, and may present as the initial presentation. Case Presentation: We describe two patients who presented with multiple deep, large, nonhealing skin ulcers postoperatively with purulent drainage and fever. Both patients were diagnosed with GPA after an extensive evaluation, including histopathology. Infectious, connective tissue disease and malignant etiologies were excluded. Their cANCA and PR3-ANCA levels were positive. Patient 2 was diagnosed early and recovered well after treatment with corticosteroids and rituximab; however, Patient 1 had a poor prognosis due to a long disease course. Conclusions: Diseases with multiple deep, large skin ulcers and fever can be infectious or noninfectious. Atypical manifestations may lead to missed diagnosis and misdiagnosis. GPA may initially present in a localized form before progressing to a generalized disease. The two cases we have highlighted will prompt clinicians to nevertheless call for a low threshold for diagnosis.

This paper coupled a unified passive and active microwave observation operator—namely, an enhanced, physically-based, discrete emission-scattering model—with the community land model (CLM) in a data assimilation (DA) system. By implementing the system default local ensemble transform Kalman filter (LETKF) algorithm, the Soil Moisture Active and Passive (SMAP) brightness temperature TBp (p = Horizontal or Vertical polarization) assimilations for only soil property retrieval and both soil properties and soil moisture estimates were investigated with the aid of in situ observations at the Maqu site. The results indicate improved estimates of soil properties of the topmost layer in comparison to measurements, as well as of the profile. Specifically, both assimilations of TBH lead to over a 48% reduction in root mean square errors (RMSEs) for the retrieved clay fraction from the background compared to the top layer measurements. Both assimilations of TBV reduce RMSEs by 36% for the sand fraction and by 28% for the clay fraction. However, the DA estimated soil moisture and land surface fluxes still exhibit discrepancies when compared to the measurements. The retrieved accurate soil properties alone are inadequate to improve those estimates. The discussed uncertainties (e.g., fixed PTF structures) in the CLM model structures should be mitigated.

Brittle fracture is a typical mechanical characteristic of high-strength self-compacting concrete, and the research on its toughening modification remains the highlight in the engineering field. To understand the effect of toughening materials (including polymer latex powders, rubber particles, and polyethylene fibers) on the mechanical behavior of C80 high-strength self-compacting concrete under static loading, the failure mode, mechanical strength, strain field, and crack opening displacement (COD) of prepared high-strength self-compacting concrete under compressive, splitting, and flexural loads were studied based on digital image technology (DIC). The corresponding mechanism is also discussed. The results show that the hybrid of polymer latex powders, rubber particles, and polyethylene fibers can increase the crack path and inhibit the development of macrocracks in concrete, thus turning the fracture behavior of concrete from brittle to ductile. The addition of toughening materials reduced the compressive and flexural strengths of high-strength self-compacting concrete, but it increased the splitting strength. DIC showed that the incorporation of toughening materials promoted the redistribution of strain and reduced the degree of strain concentration in high-strength self-compacting concrete. The evolution of COD in high-strength self-compacting concrete can be divided into two stages, including the linear growth stage and the plastic yield stage. The linear growth stage can be extended by incorporating toughening materials. The COD and energy absorption capacity of concrete were enhanced with the addition of toughening materials, and the best enhancement was observed with the hybrid of polymer latex powders, rubber particles, and polyethylene fibers. Overall, this research provides a reference for exploring effective technical measures to improve the toughness of high-strength self-compacting concrete.

Polypropylene undergoes permanent alterations during the processing and manufacture of cables due to shear effects, which in turn affect all of its qualities. There are currently few research papers on the modulation of material structure and many performance parameters by the shear stress field. In this study, we examined the impact of various shear levels on the structural, mechanical, and electrical properties of polypropylene, as well as their relationships. The results indicated that shear strength decreased the material’s viscosity and oxidation resistance. As shear duration increased, crystallinity began to diminish. In the meantime, the crystallinity improved as the shear temperature rose. The thermal elongations of the sheared samples were all about 5%. Short-term shearing boosted the material’s toughness, but as the degree of shear continued to increase, the material’s toughness and rigidity steadily decreased. The storage modulus of the material decreased with increasing shear, the loss peak initially increased and subsequently decreased, the peak position shifted from low to high temperature, and the loss factor was relatively small. In samples sheared for brief periods, an accumulation of space charge and an increase in its nonlinear threshold field strength were observed. With increasing shear time, the material’s space charge accumulation was repressed, the current density initially grew and then reduced, and the nonlinear threshold field strength initially fell and then increased. Under shearing, the electrical strength of the material increased by approximately 2%. In addition, the presence of an antioxidant improved all of the aforementioned features.

Purple heading Chinese cabbage has become popular in recent years due to its attractive color and health benefits. However, purple varieties remain rare, and the regulation mechanism of anthocyanin accumulation in Chinese cabbage is still largely unknown. By introducing the purple color trait from Brassica juncea, a new purple heading Chinese cabbage cultivar (18M-245) was generated with deep purple leaves at both the seedling and adult stages. Anthocyanin accumulation in 18M-245 increased when grown at low temperatures. FISH and genotyping results showed that the purple trait was caused by an alien chromosome addition line derived from the Brassica B genome. The LDOX coding gene BjuB014115 from the addition line was highly expressed in 18M-245, consistent with the results of anthocyanin accumulation. Meanwhile, several MYB and bHLH transcriptional factors from the Brassica A genome were found to directly bind to the promoter of BjuB014115, suggesting that interactions between the Brassica A and B genomes are involved in the regulatory network of anthocyanin biosynthesis in Chinese cabbage. Our results provide new insights into the regulation mechanism of anthocyanin biosynthesis in purple heading Chinese cabbage.

The reliable monitoring of the depth of anesthesia (DoA) is essential to control the anesthesia procedure. Electroencephalography (EEG) has been widely used to estimate DoA since EEG could reflect the effect of anesthetic drugs on the central nervous system (CNS). In this study, we propose that a deep learning model consisting mainly of a deep residual shrinkage network (DRSN) and a 1 × 1 convolution network could estimate DoA in terms of patient state index (PSI) values. First, we preprocessed the four raw channels of EEG signals to remove electrical noise and other physiological signals. The proposed model then takes the preprocessed EEG signals as inputs to predict PSI values. Then we extracted 14 features from the preprocessed EEG signals and implemented three conventional feature-based models as comparisons. A dataset of 18 patients was used to evaluate the models’ performances. The results of the five-fold cross-validation show that there is a relatively high similarity between the ground-truth PSI values and the predicted PSI values of our proposed model, which outperforms the conventional models, and further, that the Spearman’s rank correlation coefficient is 0.9344. In addition, an ablation experiment was conducted to demonstrate the effectiveness of the soft-thresholding module for EEG-signal processing, and a cross-subject validation was implemented to illustrate the robustness of the proposed method. In summary, the procedure is not merely feasible for estimating DoA by mimicking PSI values but also inspired us to develop a precise DoA-estimation system with more convincing assessments of anesthetization levels.

Dental caries, particularly secondary caries, which is the main contributor to dental repair failure, has been the subject of extensive research due to its biofilm-mediated, sugar-driven, multifactorial, and dynamic characteristics. The clinical utility of restorations is improved by cleaning bacteria nearby and remineralizing marginal crevices. In this study, a novel multifunctional dental resin composite (DRC) composed of Sr-N-co-doped titanium dioxide (Sr-N-TiO2) nanoparticles and nano-hydroxyapatite (n-HA) reinforcing fillers with improved antibacterial and mineralization properties is proposed. The experimental results showed that the anatase-phase Sr-N-TiO2 nanoparticles were synthesized successfully. After this, the curing depth (CD) of the DRC was measured from 4.36 ± 0.18 mm to 5.10 ± 0.19 mm, which met the clinical treatment needs. The maximum antibacterial rate against Streptococcus mutans (S. mutans) was 98.96%, showing significant inhibition effects (p < 0.0001), which was experimentally verified to be derived from reactive oxygen species (ROS). Meanwhile, the resin exhibited excellent self-remineralization behavior in an SBF solution, and the molar ratio of Ca/P was close to that of HA. Moreover, the relative growth rate (RGR) of mouse fibroblast L929 indicated a high biocompatibility, with the cytotoxicity level being 0 or I. Therefore, our research provides a suitable approach for improving the antibacterial and mineralization properties of DRCs.

This work investigates the impacts of structural parameters on the performances of p-GaN/AlGaN/GaN HEMT-based ultraviolet (UV) phototransistors (PTs) using Silvaco Atlas. The simulation results show that a larger Al content or greater thickness for the AlGaN barrier layer can induce a higher two-dimensional electron gas (2DEG) density and produce a larger photocurrent. However, they may also lead to a larger dark current due to the incomplete depletion of the GaN channel layer. The depletion conditions with various Al contents and thicknesses of the AlGaN layer are investigated in detail, and a borderline between full depletion and incomplete depletion was drawn. An optimized structure with an Al content of 0.23 and a thickness of 14 nm is achieved for UV-PT, which exhibits a high photocurrent density of 92.11 mA/mm, a low dark current density of 7.68 × 10−10 mA/mm, and a large photo-to-dark-current ratio of over 1011 at a drain voltage of 5 V. In addition, the effects of other structural parameters, such as the thickness and hole concentration of the p-GaN layer as well as the thickness of the GaN channel layer, on the performances of the UV-PTs are also studied in this work.

In this paper, N, N’-m-phenylene dimaleimide (HVA-2) grafted crosslinked polyethylene (XLPE) insulation materials with different HVA-2 contents were prepared. The grafting, crosslinking, and crystalline structure were characterized by Fourier-transform infrared spectroscopy (FTIR), Soxhlet extraction, and differential scanning calorimetry (DSC), respectively. The space charge distribution, direct current (DC) breakdown strength, and DC conduction current density were tested and the electronic structure was calculated from first-principles. HVA-2 grafting modification can significantly reduce the accumulation of space charges and the conduction current density of XLPE, but have a negative effect on DC breakdown strength. The polar groups of the grafted HVA-2 anchored on XLPE by the grafting reaction can introduce deep traps densely and evenly in XLPE, which would capture and scatter charge carriers, thus reducing the carrier concentration and mobility and further improving the space charge distribution and reducing conduction current density. However, the grafting of HVA-2 can increase the crosslinking extent of XLPE to make the crystallinity decrease and the crystallization inhomogeous, leading to a certain decrease in the breakdown strength of the grafted XLPE.

High sensitivity to climate change has been demonstrated in global maize production, especially the extreme high temperature and drought events. At present, the interactive effects of those extreme event stresses on maize growth at the grain filling stage are less well studied. In this study, a two-year field experiment was conducted to evaluate the compound effects of three stressors (high temperature, drought, and stress duration) at the filling phase on phenological period, grain filling rate, yield component, and yield of summer maize. The precipitation and temperature were controlled by using a rain-shelter systems and a warming system with infrared radiation lamps in field plots. The results indicated the significant influences of high temperature, drought stress, and the interaction on the growth and yield of summer maize. Under the individual factor of drought, compared to normal irrigation (NI), the significant decrease in grain-filling rate for mild drought (LD) and severe drought (SD) were 70.7% and 60.6%, respectively, while the decrease in grain yield for LD and SD were 80.1% and 93.6%, respectively. Under the individual factor of temperature, the consequences on grain-filling rate and grain yield were more severe at high temperature (temperature increase by 4.5 °C) than low temperature (temperature increase by 2.3 °C). The compound of high temperature and drought indicated that the grain yield increase by the compound effects of 3 °C for 5 days under normal irrigation (NIT3D5) and the compound effects of 3 °C for 7 days under mild drought (LDT3D7) were 3.5% and 10.9%, respectively, compared to without temperature stress. The grain yields were reduced by the other compound effects. The preferential temperature (temperature increase by 2.3 °C) promoted maize growth under normal irrigation and mild drought, while high temperature (temperature increase by 4.5 °C) significantly inhibited maize growth under normal irrigation and heavy drought. The warming climate is favorable to maize production within certain limits, and will provide a scientific basis for agricultural disaster prevention and mitigation.

Peptide detectability is defined as the probability of identifying a peptide from a mixture of standard samples, which is a key step in protein identification and analysis. Exploring effective methods for predicting peptide detectability is helpful for disease treatment and clinical research. However, most existing computational methods for predicting peptide detectability rely on a single information. With the increasing complexity of feature representation, it is necessary to explore the influence of multivariate information on peptide detectability. Thus, we propose an ensemble deep learning method, PD-BertEDL. Bidirectional encoder representations from transformers (BERT) is introduced to capture the context information of peptides. Context information, sequence information, and physicochemical information of peptides were combined to construct the multivariate feature space of peptides. We use different deep learning methods to capture the high-quality features of different categories of peptides information and use the average fusion strategy to integrate three model prediction results to solve the heterogeneity problem and to enhance the robustness and adaptability of the model. The experimental results show that PD-BertEDL is superior to the existing prediction methods, which can effectively predict peptide detectability and provide strong support for protein identification and quantitative analysis, as well as disease treatment.