Search

The issues during Zn deposition in rechargeable Zn-based batteries greatly hinder cycling stability. In this work, a simple and inexpensive approach to tailor the Zn electrodeposition is proposed by tuning the viscosity of the liquid electrolyte (LE). First, the growth mechanisms of Zn deposition under different electrolyte properties are investigated by numerical simulation, from which the bottom deposition tends to fuse with each other when there are more deposition sites, and the mass-transfer coefficient is lower, thus achieving uniform deposition. Besides, the whole process of Zn deposition in charging-discharging cycling is in situ observed by an optical microscope. It is found that the cause of the poor stability in the LE is due to the uneven Zn deposition, resulting in weak bonding between the deposition and the electrode surface, which is also the reason for the formation of dead Zn. In contrast, when an appropriate amount of the polymer is added to the LE to increase the viscosity, an appropriate overpotential can be created, generating more deposition sites. In addition, the viscosity reduces the mass-transfer coefficient, making the distance from the ion to the deposition sites the main controlling factor. The Zn ions are more inclined to move in the direction of electric field lines, which results in a uniform and dense deposition layer. Furthermore, the effectiveness of this method is demonstrated in a Zn-LiFePO

High-performance polyimide-based porous carbon/crystalline composite absorbers (PIC/CNT) were prepared by vacuum freeze-drying and high-temperature pyrolysis.

The leaf chlorophyll content (Chlleaf) is a crucial vegetation parameter in carbon cycle modelling and agricultural monitoring at local, regional and global scales. The red-edge spectral region is sensitive to variations in Chlleaf. An increasing number of sensors are capable of sampling red-edge bands, providing opportunities to estimate Chlleaf. However, the contributions of canopy/foliar/soil factors are always combined in the reflectance signal, which limits the generalizability of vegetation index (VI)-based Chlleaf inversions. This study aims to propose a new red-edge chlorophyll index to decouple the effects of the canopy and soil background from the Chlleaf estimation.The chlorophyll sensitive index (CSI) was proposed, and the regression equations between the CSI and Chlleaf were acquired using PROSAIL (PROSPECT + SAIL) and the 4-Scale-PROSPECT model.Sensitivity analyses showed that the CSI is resistant to variations in the canopy structure and soil background. Validation results obtained using 308 ground-measured samples over nine sites world-wide revealed that CSI improves the Chlleaf retrieval accuracy (root mean square error (RMSE = 9.39 μg cm−2) compared with the existing Medium Resolution Imaging Spectrometer (MERIS) terrestrial chlorophyll index (MTCI; RMSE = 13.00 μg cm−2). Moreover, the CSI method steadily achieves a highly accurate inversion under different LAI and Chlleaf conditions. Based on the CSI regression method, a Chlleaf product with a 30-m/10-day resolution across China was generated.The CSI is sensitive to Chlleaf but resistant to canopy structure and soil moisture parameters, and it has the potential to explicitly retrieve leaf-scale biochemistry in ecosystem modelling and ecological applications.

This paper proposed a multi-objective power inspection method based on multiple attention mechanisms and strong semantic feature extraction.Based on the Deeplabv3+ network model,the CBAM attention mechanism is introduced into the MobileNetV2 network to enchance the contextual information interaction capability; the ASPPAttention fast feature fusion structure is proposed to achievefast extraction of multi-dimensional valid information by designing deep separa-ble convolutions with different perceptual fields, and to enhance the pixel-levelfeature encoding capability with the CA attention mechanism; the lightweightfeature encoding capability is proposed. The proposed lightweight inverted con-volutional decoder structure improves the feature extraction capability of themodel by designing an inverted bottleneck convolutional structure in two quadru-ple downsampling layers with a low number of parameters, and introduces the CAattention mechanism to avoid the heterogeneous gap; in the training process, theconvergence speed of the model is accelerated by using migration learning, andDice Loss is introduced to avoid the effect of the number of samples on the gener-alization of the model. The experimental results show that the MIoU of the powersegmentation inspection method based on attention and information decouplingcan reach 48.5%, the accuracy rate can reach 97.5%, and the detection speed ofthe model can reach 40.8 FPS, which is a better balance of speed and accuracycompared with the network models of HRNet, PSPNet and DeeplabV3+.

1. Species-rich communities exhibit higher levels of ecosystem functioning compared to species-poor ones, and this positive relationship strengthens over time. One proposed explanation for this phenomenon is the reduction of niche overlap among plants or animals, which corresponds to increased complementarity and reduced competition. 2. In order to examine the potential of increased complementarity among plants or animals to strengthen the relationship between diversity and ecosystem functions, we integrated models of bio-energetic population dynamics and food-web assembly. Through the simulation of various scenarios of plant and animal complementarity change, we sought to elucidate the mechanisms underlying the observed increases in (1) primary productivity, (2) control of herbivores by predators, and (3) reduction of herbivore pressure on plants in species-rich communities. 3. Our findings reveal that increased niche complementarity of plants can steepen the diversity-function relationships if it does not increase their intraspecific competition, while increasing complementarity among animals during community assembly can also have a positive effect but with considerable variability. 4. The study highlights the importance of trait variation both among and within species, and the interplay between intra- and interspecific competition strength in shaping the functioning of ecosystems over time. These results offer insights into the mechanisms underpinning the diversity-functioning relationship, and have practical implications for ecosystem management and conservation efforts.

Three-dimensional (3D) histopathology involves the microscopic examination of a specimen, which plays a vital role in studying tissue’s 3D structures and the signs of diseases. However, acquiring high-quality histological images of a whole organ is extremely time-consuming (e.g., several weeks) and laborious, as the organ has to be sectioned into hundreds or thousands of slices for imaging. Besides, the acquired images are required to undergo a complicated image registration process for 3D reconstruction. Here, by incorporating a recently developed vibratome-assisted block-face imaging technique with deep learning, we developed a pipeline termed HistoTRUST that can rapidly and automatically generate subcellular whole organ’s virtual hematoxylin and eosin (H&E) stained histological images which can be reconstructed into 3D by simple image stacking (i.e., without registration). The performance and robustness of HistoTRUST have been successfully validated by imaging all vital mouse organs (brain, liver, kidney, heart, lung, and spleen) within 1–3 days depending on the size. The generated 3D dataset has the same color tune as the traditional H&E stained histological images. Therefore, the virtual H&E stained images can be directly analyzed by pathologists. HistoTRUST has a high potential to serve as a new standard in providing 3D histology for research or clinical applications.

Since the traffic of novel attacks exceeds current knowledge, realistic traffic labeling methods are prone to mislabeling, which has a significant impact on machine learning-based intrusion detection systems. Data cleaning typically relies on the ability of supervised deep neural networks to learn correct knowledge. Under high noise conditions, noisy labels can affect a supervised network and render it ineffective. To clean traffic datasets under high noise conditions, we propose an unsupervised learning-based data cleaning framework (called ULDC) that does not rely on labels and powerful supervised networks, hence reducing the impact of noisy labels. ULDC evaluates the confidence of observed labels through the distribution and similarity of samples in low dimensions. Moreover, ULDC maximizes the retention of hard samples through adaptive intra-class threshold evaluation, preserving more hard samples for training and improving generalization. In evaluations of ULDC on the CIRA-CIC-DoHBrw-2020 dataset, the percentage of data correction reached more than 75% under high noise, which is better than that of the state-of-the-art methods. ULDC is applicable to traffic data cleaning in both traditional networks and novel networks such as the Internet of Things and mobile networks, and it has been validated on datasets including CIC-IDS-2017 and IoT-23.

Funding Information: This work was supported by the Beijing National Laboratory for Molecular Sciences (BNLMS- CXTD-202001), Beijing Municipal Science & Technology Commission (Z181100004818003, Z201100008720006, and Z191100000819003), the National Natural Science Foundation of China (52025023, 51991342, 52021006, and 11888101), the Strategic Priority Research Program of Chinese Academy of Sciences (XDB33000000), the Key R&D Program of Guangdong Province (2020B010189001, 2019B010931001, and 2018B030327001), Pearl River Talent Recruitment Program of Guangdong Province (2019ZT08C321), Beijing Natural Science Foundation (JQ19004), Zhongyuan Thousand Talents Program of Henan Province, and National Top-notch Young Talents of Ten Thousand Talents Program. Publisher Copyright: © 2022 American Chemical Society. The graphene photonic crystal fiber (Gr-PCF), with graphene coated onto the inner hole walls of the fiber, has shown its superiority in various photonic and optoelectronic applications ranging from electro-optic modulators to environmental sensors. However, these applications mainly utilize the linear optical properties of graphene, and its potentials in the nonlinear optical regime are still waiting to be explored. As for the nonlinear applications, the structure and property of Gr-PCF must be precisely manipulated for the tradeoff between nonlinear enhancement and linear absorption loss of graphene. Here, we propose a pressure-controllable chemical vapor deposition strategy to precisely control the uniform fiber length and graphene thickness, realizing the strong and tunable optical nonlinearity of Gr-PCF with acceptable optical loss. Based on the as-fabricated fiber, the nonlinear harmonic generations exhibit nearly one order of magnitude enhancement compared with those of graphene on planar quartz. Moreover, an ultrafast all-fiber laser employing the nonlinear Gr-PCF as a saturable absorber is demonstrated with ∼8 mW output power, ∼2 ps pulse width, and ∼37 MHz repetition frequency. Our results can technically open up an infusive way to precisely engineer the nonlinear properties of graphene optical fibers and broaden their applications in all-fiber photonic and optoelectronic devices.

Evaluating the sorption performance of coexisting phenanthrene and cadmium on rice roots is critical for a better understanding of plant sorption and uptake of combined pollutants. Batch sorption kinetics and isotherms of phenanthrene and cadmium on rice root biomass, as well as key factors in both single and binary systems, were evaluated to access any potential synergistic effects. Results showed that rice root biomass effectively and simultaneously removed phenanthrene and cadmium from aqueous solutions. The presence of a co-solute led to synergistic effects on the sorption, enhancing the affinity parameters, i.e., the Kd (linear model), from 2530 to 3970 L/kg for phenanthrene, and the KF (Freundlich model), from 640 to 777 L/kg for cadmium. The well fitted pseudo-second-order kinetics implied that the coexisting pollutants slowed the sorption rates, decreasing the sorption rate (k2) from 0.185 to 0.155 g/mg/min for phenanthrene and from 0.0361 to 0.00146 g/mg/min for cadmium. The results suggested that further studies should carefully evaluate the remediation and risk-assessment in rice-arable soil under combined pollution.

Chemically and biologically modified substrates for single-cell patterning have been studied extensively, but physically modified structures for single-cell patterning still need further study. In this paper, physically modified silicon nanostructures were introduced to study their effect on SHSY5Y cells. Double-beam double exposure laser interference lithography combined with metal-assisted etching (MACE) was used to fabricate the physically modified silicon nanostructures. It was found that the cells on the gratings stretched and grew orderly along the grating with a small cell area and almost the same cell length compared with those on the Si wafer (control group). While on the grids, the cells were round with limited spreading, grew independently and had the smallest cell area and cell length. Moreover, the localization ratio of cells adhered onto the areas of nanopillars in the grid structures with different periods has been investigated. The results suggest that the physically modified grid silicon nanostructures can regulate the single-cell localization growth and the rational design of substrate structures can maximize the single-cell localization ratio. The findings provide guidance for the design of physically modified nanostructures and regulating single cell patterning, and a better understanding of single-cell localized growth.

Recently, optical remote-sensing images have been steadily growing in size, as they contain massive data and complex backgrounds. This trend presents several problems for object detection, for example, increased computation time and memory consumption and more false positives due to the complex backgrounds of large-scale images. Inspired by deep neural networks combined with time-frequency analysis, we propose a time-frequency analysis-based object detection method for large-scale remote-sensing images with complex backgrounds. We utilize wavelet decomposition to carry out a time-frequency transform and then integrate it with deep learning in feature optimization. To effectively capture the time-frequency features, we propose a feature optimization method based on deep reinforcement learning to select the dominant time-frequency channels. Furthermore, we design a discrete wavelet multiscale attention mechanism (DW-MAM), enabling the detector to concentrate on the object area rather than the background. Extensive experiments show that the proposed method of learning from time-frequency channels not only solves the challenges of large-scale and complex backgrounds, but also improves the performance compared to the original state-of-the-art object detection methods. In addition, the proposed method can be used with almost all object detection neural networks, regardless of whether they are anchor-based or anchor-free detectors, horizontal or rotation detectors.

Spatiotemporally continuous long-term leaf area index (LAI) products are urgently needed to monitor environmental changes. The current filter- or curve-fitting-based time series reconstructive algorithms fail to reconstruct the LAI time series with many continuous missing values or missing values in key phenological periods, which are common issues in high-spatial-resolution LAI time series. In this article, a meteorological data-driven backpropagation neural network (MBPNN) was proposed to reconstruct discontinuous LAI profiles with a two-step process using vegetation phenological information. As the basis of the strong dependence of vegetation growth on meteorological conditions, a reasonable growth trajectory of reconstructed LAI can be guaranteed by the algorithm even though if many observed values are missing. Validations for reconstructed LAI were conducted both spatially and temporally based on reference maps and field-measured long-term observations. The results showed that the LAI predicted by the MBPNN had a similar accuracy (RMSE = 0.4076) as the Landsat LAI inversions (RMSE = 0.4083) and a similar reconstructed trajectory as the field-measured LAI series even though over 100 days of continuous data were missing (RMSE = 0.1620). A comparison with the Harmonic ANalysis of Time Series (HANTS) algorithm showed that the accuracy of MBPNN was more stable regardless of the size/position of the missing data, and the proposed method performed much better when the data were continuously missing for 50 days or more.

A simple method that combines liquid-liquid phase separation and high-temperature pyrolysis has been developed for the synthesis of polyimide-derived porous carbon/Co particle-based composite absorbers (PIC/Co-800 and PIC/Co-1000). The excellent heat resistance of polyimide allows the composite precursor to maintain its porous structure during pyrolysis. According to the results, PIC/Co-800 and PIC/Co-1000 have a coral-like porous structure, which can enhance the impedance matching property and microwave attenuation ability of the synthesized materials. The impedance matching condition and dissipation ability of PIC/Co-800 and PIC/Co-1000 have been enhanced due to the synergistic effect between the carbon-induced dielectric loss and Co nanoparticle-induced magnetic loss. PIC/Co-1000 shows the highest absorption performance with a minimum reflection loss (RL) of -40.22 dB at a thickness of 5.3 mm and an effective absorption bandwidth (EABW, RL ≤ -10 dB) of 4.10 GHz at a thickness of 1.4 mm. With thicknesses in the range of 1.4 mm to 5.3 mm, the minimum RL value of each thickness is lower than -15 dB. Therefore, this work provides a new strategy for the synthesis of promising absorbing materials with outstanding EMW absorption performance.

Vegetation index (VI) derived from remotely sensed images is a proxy of terrestrial vegetation information and widely used in land monitoring and global change studies. Recently, the prediction of vegetation properties has been an interest in related communities. With the accumulation of satellite records over the past few decades, the spatial-temporal prediction of VI becomes feasible. In this letter, we developed deep recurrent neural networks (RNNs) with long short-term memory (LSTM) and gated recurrent units (GRUs) to predict the short-term VI based on historical observations. The pixel-based fully connected networks GRU and LSTM (FCGRU and FCLSTM) and patch-based convolutional networks (ConvGRU and ConvLSTM) are established and compared with the traditional multilayer perceptron (MLP) model. Moderate Resolution Imaging Spectroradiometer (MODIS) and Sentinel-2 normalized difference VI (NDVI) data sets were used in the experiments. The prediction performance is evaluated globally in different regions, different vegetation types, and different growing seasons. Results demonstrate that the RNN models can predict VI with high accuracy (average root mean square error (RMSE) around 0.03), which is superior to the MLP model. In general, the pixel-based RNN models performed better than the patch-based models especially in regions with a larger proportion of outliers. And the prediction accuracy is stable over different vegetation types and growing seasons.

IntroductionInvasive alien plants (IAPs) are major hazards to biodiversity, human health, and the agricultural economy. As one of the most aggressive species of IAPs, the distribution area of Centaurea solstitialis L. has increased exponentially in the past two years since its invasion into Xinjiang, China, in July 2014. Predicting the potential geographic distributions (PGDs) of C. solstitialis in China can provide theoretical support for preventing the continued spread of this weed.MethodsIn this study, based on 5,969 valid occurrence records of C. solstitialis and 33 environmental variables, we constructed an ensemble model to predict suitable habitats for C. solstitialis under climate change scenarios.ResultsOur results showed that the mean true skill statistic (TSS) values, area under the receiver operating characteristic (ROC) curve (AUC), and Cohen’s Kappa (KAPPA) for the ensemble model were 0.954, 0.996, and 0.943, respectively. The ensemble model yielded more precise predictions than those of the single model. Temperature seasonality (Bio4), minimum temperature of the coldest month (Bio6), precipitation of the driest month (Bio14), and human influence index (HII) have significantly disrupted the PGDs of C. solstitialis in China. The total (high) suitability habitat area of C. solstitialis in China was 275.91 × 104 (67.78 × 104) km2, accounting for 71.26 (7.06)% of China. The PGDs of C. solstitialis in China under the current climate were mainly in East China (Shandong, Jiangsu, Shanghai, Zhejiang, and Anhui), Central China (Henan, southwestern Shanxi, southern Shaanxi, southern Gansu, Hubei, Hunan, Jiangxi, Chongqing, and Guizhou), and South China (southern Tibet, eastern Sichuan, Yunnan, Guangxi, Guangdong, Fujian, and Taiwan). Under future climate scenarios, the total suitability habitat area for C. solstitialis will expand, whereas the high suitability habitat area will decrease.DiscussionThe main manifestation is that the shift of southeast China into a moderate suitability habitat, and the total suitability habitats will be extended to northwest China. More focus needs to be placed on preventing further spread of C. solstitialis in northwest China.

We used network pharmacology to predict the correlation between the pathway of Bushenhuoxue formula in the treatment of vascular dementia and carried out experiments to verify the correlation between drug composition and disease. By screening the active components and key targets through various databases and drawing the topological network diagram, we obtained 502 effective compound targets, 601 disease targets, 95 disease-related compound targets, and 162 pathways. The pathway related to vascular dementia may be neurodegeneration-multiple diseases, PI3K-Akt signaling pathway, Mitogen-activated protein kinase signaling pathway, or HIF-1 signaling pathway. By detecting the learning and memory ability of vascular dementia rats, the morphology of the hippocampus under the electron microscope, the degree of neuronal damage, and autophagy-related proteins, the results showed that the Bushenhuoxue formula could improve the neuronal injury induced by ischemia in the hippocampus, down-regulate the level of autophagy, and thereby improve learning and memory. Therefore, the Bushenhuoxue formula may improve the ischemic injury of neurons by regulating the mechanism of neuronal autophagy.

Electron-shuttling agents such as pyrogenic carbon (PC) can mediate long-distance electron transfer and play numerous key roles in aquatic and soil biogeochemical processes. The electron-shuttling capacity of PC relies on both the surface oxygen-containing functional groups and bulk graphitic structures. Although the impacts of oxygen-containing functional groups on the electron-shuttling performance of PC are well studied, there remains insufficient understanding on the function of graphitic structures. Here, we studied the functions of PC in mediating microbial (

Large-stroke rapid motion is required in many industrial servo systems (e.g. pick-and-place applications), on which constraints of control input and motion speed are usually imposed. This paper presents a hybrid control scheme for large-stroke rapid motion in such systems. The controller resorts to the time-optimal control (TOC) for maximum acceleration initially and then switches into a linear control law to achieve smooth settling. A speed regulation stage is inserted in the tracking process to prevent violating the speed constraint. To tackle the unmeasured speed and unknown disturbance, an extended state observer (ESO) can be included in the controller. The controller is applied to a permanent magnet synchronous motor (PMSM) servo system for large-stroke position regulation. Digital simulation via MATLAB is conducted first, followed by an experimental verification using a TMS320F28335 board. The results confirm that the proposed controller can track a wide range of target references with desirable performance under speed constraint and load disturbance and has a competitive advantage over the popular active disturbance-rejection control (ADRC) scheme.

The boll weevil, Anthonomus grandis Boheman (Coleoptera: Curculionidae), is an invasive alien species that can damage cotton plants and cause huge economic losses in the cotton industry. Currently, A. grandis is mainly distributed in the American continent. However, few studies have indicated the distribution and modification of its suitable global habitats after undergoing climate change. Based on the 339 distribution records of A. grandis and eight bioclimatic variables, we used the optimal MaxEnt model to predict the potential global distribution of A. grandis under the current (1970–2000) and future climatic scenarios (SSP5-8.5). The annual mean temperature (bio1) and isothermality (bio3) were the two most important bioclimatic variables, which indicates that the survival of A. grandis is extremely sensitive to temperature fluctuations. Under the current scenario, the highly suitable habitats were mainly distributed in America (the USA, Mexico, Brazil, Argentina, Paraguay, and Uruguay), Africa (South Africa, Ethiopia, and Mozambique), Asia (Pakistan, India, Thailand, Burma, and China), and Oceania (Australia). In future scenarios (SSP5-8.5), the potential suitable global habitats reached the highest level in America, Africa, Asia, and Oceania in the 2090s. Our study provides a meaningful reference for researchers, quarantine officers, and governments to devise suitable management control strategies for A. grandis.

Wuyi Mountain in Southeast China is the origin of black tea and oolong tea. It is also considered the ‘treasure trove of tea cultivars’ because of its rich tea germplasm resources. In the present study, the population structure and genetic diversity of 137 tea germplasms from Wuyi Mountain and its adjacent areas were analyzed by SNPs. The information index (I), observed heterozygosity (Ho), expected heterozygosity (He) and fixation index (F) polymorphisms of the selected SNPs were high, stable and reliable. Ho had an average of 0.389, while He had an average of 0.324, indicating that Wuyi Mountain tea germplasms had rich genetic diversity. The AMOVA results showed that genetic variation came mainly from intrapopulation variation, accounting for 66% of the total variation. The differences in the Fst and Nei values of tea germplasm between Wuyi Mountain and its adjacent areas are similar to the geographical differences. Multiple analyses based on high-quality SNPs found that the landraces of tea plants on Wuyi Mountain had different genetic backgrounds from the wild-type landraces and the landraces of Wuyi Mountain tea plants underwent population differentiation. This study provides a basis for the effective protection and utilization of tea germplasms on Wuyi Mountain and lays a foundation for identifying potential parents to optimize tea cultivation.

The unclear Li

The aim of the study was to present the intraoperative findings of the relevant digital nerves of the duplicated thumbs in an excision and reconstruction procedure for the Wassel-Flatt type Ⅳ radial polydactyly.The study was conducted on patients with Wassel-Flatt type IV radial polydactyly who underwent excision and reconstruction between 2018 and 2021 at our institution. The ulnar digital nerve of the radial thumb and the radial digital nerve of the ulnar thumb were identified and traced intraoperatively. The level of the bifurcation of the nerves and abnormal findings were documented.A total of 123 hands in 119 patients were included in this study. In 114 hands, the bifurcation of the nerves was located within 1 cm of the metacarpophalangeal flexion crease. The radial digital nerve to the ulnar thumb was abnormally compressed in deep fascial tissue in 7 of these 114 hands. In 5 hands, the level of bifurcation was more than 1 cm proximal to the crease. No radial digital nerve to the ulnar thumb was identified in the remaining 4 hands.Although rare, abnormal nerve compression of the digital nerve may exist in duplicated thumbs of Wassel-Flatt type IV radial polydactyly.In an excision and reconstruction procedure, we suggest that the bifurcation of the nerves should be identified before the nerve to the radial thumb is excised to avoid injuring the nerve to the main ulnar thumb.