Background:Rosa chinensis Jacq.and Rosa rugosa Thunb.are not only of ornamental value,but also edible flowers and the flower buds have been listed in the Chinese Pharmacopoeia as traditional medicines.The two plants h...Background:Rosa chinensis Jacq.and Rosa rugosa Thunb.are not only of ornamental value,but also edible flowers and the flower buds have been listed in the Chinese Pharmacopoeia as traditional medicines.The two plants have some differences in efficacy,but the flower buds are easily confused for similar traits.In addition,large-scale cultivation of ornamental rose flowers may lead to a decrease in the effective components of medicinal roses.Therefore,it is necessary to study the chemical composition and make quality evaluation of Rosae Chinensis Flos(Yueji)and Rosae Rugosae Flos(Meigui).Methods:In this study,40 batches of samples including Meigui and Yueji from different regions in China were collected to establish high-performance liquid chromatography fingerprints.Then,the fingerprints data was analyzed using principal component analysis,hierarchical cluster analysis,and partial least squares discriminant analysis analysis chemometrics to obtain information on intergroup differences,and non-targeted metabolomic techniques were applied to identify and compare chemical compositions of samples which were chosen from groups with large differences.Differential compounds were screened by orthogonal partial least-squares discriminant analysis and S-plot,and finally multi-component quantification was performed to comprehensively evaluate the quality of Yueji and Meigui.Results:The similarity between the fingerprints of 40 batches roses and the reference print R was 0.73 to 0.93,indicating that there were similarities and differences between the samples.Through principal component analysis and hierarchical cluster analysis of fingerprints data,the samples from different origins and varieties were intuitively divided into four groups.Partial least-squares discriminant analysis analysis showed that Meigui and Yueji cluster into two categories and the model was reliable.A total of 89 compounds were identified by high resolution mass spectrometry,mainly were flavonoids and flavonoid glycosides,as well as phenolic acids.Eight differential components were screened out by orthogonal partial least-squares discriminant analysis and S-plot analysis.Quantitative analyses of the eight compounds,including gallic acid,ellagic acid,hyperoside,isoquercitrin,etc.,showed that Yueji was generally richer in phenolic acids and flavonoids than Meigui,and the quality of Yueji from Shandong and Hebei was better.It is worth noting that Xinjiang rose is rich in various components,which is worth focusing on more in-depth research.Conclusion:In this study,the fingerprints of Meigui and Yueji were established.The chemical components information of roses was further improved based on non-targeted metabolomics and mass spectrometry technology.At the same time,eight differential components of Meigui and Yueji were screened out and quantitatively analyzed.The research results provided a scientific basis for the quality control and rational development and utilization of Rosae Chinensis Flos and Rosae Rugosae Flos,and also laid a foundation for the study of their pharmacodynamic material basis.展开更多
The aerospace community widely uses difficult-to-cut materials,such as titanium alloys,high-temperature alloys,metal/ceramic/polymer matrix composites,hard and brittle materials,and geometrically complex components,su...The aerospace community widely uses difficult-to-cut materials,such as titanium alloys,high-temperature alloys,metal/ceramic/polymer matrix composites,hard and brittle materials,and geometrically complex components,such as thin-walled structures,microchannels,and complex surfaces.Mechanical machining is the main material removal process for the vast majority of aerospace components.However,many problems exist,including severe and rapid tool wear,low machining efficiency,and poor surface integrity.Nontraditional energy-assisted mechanical machining is a hybrid process that uses nontraditional energies(vibration,laser,electricity,etc)to improve the machinability of local materials and decrease the burden of mechanical machining.This provides a feasible and promising method to improve the material removal rate and surface quality,reduce process forces,and prolong tool life.However,systematic reviews of this technology are lacking with respect to the current research status and development direction.This paper reviews the recent progress in the nontraditional energy-assisted mechanical machining of difficult-to-cut materials and components in the aerospace community.In addition,this paper focuses on the processing principles,material responses under nontraditional energy,resultant forces and temperatures,material removal mechanisms,and applications of these processes,including vibration-,laser-,electric-,magnetic-,chemical-,advanced coolant-,and hybrid nontraditional energy-assisted mechanical machining.Finally,a comprehensive summary of the principles,advantages,and limitations of each hybrid process is provided,and future perspectives on forward design,device development,and sustainability of nontraditional energy-assisted mechanical machining processes are discussed.展开更多
As a kind of high-efficiency explosive with compound destructive capability, the energy output law of thermobaric explosives has been receiving great attention. In order to investigate the effects of main components o...As a kind of high-efficiency explosive with compound destructive capability, the energy output law of thermobaric explosives has been receiving great attention. In order to investigate the effects of main components on the explosive characteristics of thermobaric explosives, various high explosives and oxidants were selected to formulate five different types of thermobaric explosive. Then they were tested in both open space and closed space respectively. Pressure measurement system, high-speed camera,infrared thermal imager and multispectral temperature measurement system were used for pressure,temperature and fireball recording. The effects of different components on the explosive characteristics of thermobaric explosive were analyzed. The results showed that in open space, the overpressure is dominated by the high explosives content in the formulation. The addition of the oxidants will decrease the explosion overpressure but will increase the duration and overall brightness of the fireball. While in closed space, the quasi-static pressure formed after the explosion is positively correlated with the temperature and gas production. In addition, it was found that the differences in shell constraints can also alter the afterburning reaction of thermobaric explosives, thus affecting their energy output characteristics. PVC shell constraint obviously increases the overpressure and makes the fireball burn more violently.展开更多
Andrias davidianus(Chinese giant salamander,CGS)is the largest and oldest extant amphibian species in the world and is a source of prospective functional food in China.However,the progress of functional peptides minin...Andrias davidianus(Chinese giant salamander,CGS)is the largest and oldest extant amphibian species in the world and is a source of prospective functional food in China.However,the progress of functional peptides mining was slow due to lack of reference genome and protein sequence data.In this study,we illustrated full-length transcriptome sequencing to interpret the proteome of CGS meat and obtain 10703 coding DNA sequences.By functional annotation and amino acid composition analysis,we have discovered various genes related to signal transduction,and 16 genes related to longevity.We have also found vast variety of functional peptides through protein coding sequence(CDS)analysis by comparing the data obtained with the functional peptide database.Val-Pro-Ile predicted by the CDS analysis was released from the CGS meat through enzymatic hydrolysis,suggesting that our approach is reliable.This study suggested that transcriptomic analysis can be used as a reference to guide polypeptide mining in CGS meat,thereby providing a powerful mining strategy for the bioresources with unknown genomic and proteomic sequences.展开更多
Ore production is usually affected by multiple influencing inputs at open-pit mines.Nevertheless,the complex nonlinear relationships between these inputs and ore production remain unclear.This becomes even more challe...Ore production is usually affected by multiple influencing inputs at open-pit mines.Nevertheless,the complex nonlinear relationships between these inputs and ore production remain unclear.This becomes even more challenging when training data(e.g.truck haulage information and weather conditions)are massive.In machine learning(ML)algorithms,deep neural network(DNN)is a superior method for processing nonlinear and massive data by adjusting the amount of neurons and hidden layers.This study adopted DNN to forecast ore production using truck haulage information and weather conditions at open-pit mines as training data.Before the prediction models were built,principal component analysis(PCA)was employed to reduce the data dimensionality and eliminate the multicollinearity among highly correlated input variables.To verify the superiority of DNN,three ANNs containing only one hidden layer and six traditional ML models were established as benchmark models.The DNN model with multiple hidden layers performed better than the ANN models with a single hidden layer.The DNN model outperformed the extensively applied benchmark models in predicting ore production.This can provide engineers and researchers with an accurate method to forecast ore production,which helps make sound budgetary decisions and mine planning at open-pit mines.展开更多
Super-resolution techniques are employed to enhance image resolution by reconstructing high-resolution images from one or more low-resolution inputs.Super-resolution is of paramount importance in the context of remote...Super-resolution techniques are employed to enhance image resolution by reconstructing high-resolution images from one or more low-resolution inputs.Super-resolution is of paramount importance in the context of remote sensing,satellite,aerial,security and surveillance imaging.Super-resolution remote sensing imagery is essential for surveillance and security purposes,enabling authorities to monitor remote or sensitive areas with greater clarity.This study introduces a single-image super-resolution approach for remote sensing images,utilizing deep shearlet residual learning in the shearlet transform domain,and incorporating the Enhanced Deep Super-Resolution network(EDSR).Unlike conventional approaches that estimate residuals between high and low-resolution images,the proposed approach calculates the shearlet coefficients for the desired high-resolution image using the provided low-resolution image instead of estimating a residual image between the high-and low-resolution image.The shearlet transform is chosen for its excellent sparse approximation capabilities.Initially,remote sensing images are transformed into the shearlet domain,which divides the input image into low and high frequencies.The shearlet coefficients are fed into the EDSR network.The high-resolution image is subsequently reconstructed using the inverse shearlet transform.The incorporation of the EDSR network enhances training stability,leading to improved generated images.The experimental results from the Deep Shearlet Residual Learning approach demonstrate its superior performance in remote sensing image recovery,effectively restoring both global topology and local edge detail information,thereby enhancing image quality.Compared to other networks,our proposed approach outperforms the state-of-the-art in terms of image quality,achieving an average peak signal-to-noise ratio of 35 and a structural similarity index measure of approximately 0.9.展开更多
At present,super-resolution algorithms are employed to tackle the challenge of low image resolution,but it is difficult to extract differentiated feature details based on various inputs,resulting in poor generalizatio...At present,super-resolution algorithms are employed to tackle the challenge of low image resolution,but it is difficult to extract differentiated feature details based on various inputs,resulting in poor generalization ability.Given this situation,this study first analyzes the features of some feature extraction modules of the current super-resolution algorithm and then proposes an adaptive feature fusion block(AFB)for feature extraction.This module mainly comprises dynamic convolution,attention mechanism,and pixel-based gating mechanism.Combined with dynamic convolution with scale information,the network can extract more differentiated feature information.The introduction of a channel spatial attention mechanism combined with multi-feature fusion further enables the network to retain more important feature information.Dynamic convolution and pixel-based gating mechanisms enhance the module’s adaptability.Finally,a comparative experiment of a super-resolution algorithm based on the AFB module is designed to substantiate the efficiency of the AFB module.The results revealed that the network combined with the AFB module has stronger generalization ability and expression ability.展开更多
The employment of deep convolutional neural networks has recently contributed to significant progress in single image super-resolution(SISR)research.However,the high computational demands of most SR techniques hinder ...The employment of deep convolutional neural networks has recently contributed to significant progress in single image super-resolution(SISR)research.However,the high computational demands of most SR techniques hinder their applicability to edge devices,despite their satisfactory reconstruction performance.These methods commonly use standard convolutions,which increase the convolutional operation cost of the model.In this paper,a lightweight Partial Separation and Multiscale Fusion Network(PSMFNet)is proposed to alleviate this problem.Specifically,this paper introduces partial convolution(PConv),which reduces the redundant convolution operations throughout the model by separating some of the features of an image while retaining features useful for image reconstruction.Additionally,it is worth noting that the existing methods have not fully utilized the rich feature information,leading to information loss,which reduces the ability to learn feature representations.Inspired by self-attention,this paper develops a multiscale feature fusion block(MFFB),which can better utilize the non-local features of an image.MFFB can learn long-range dependencies from the spatial dimension and extract features from the channel dimension,thereby obtaining more comprehensive and rich feature information.As the role of the MFFB is to capture rich global features,this paper further introduces an efficient inverted residual block(EIRB)to supplement the local feature extraction ability of PSMFNet.A comprehensive analysis of the experimental results shows that PSMFNet maintains a better performance with fewer parameters than the state-of-the-art models.展开更多
Frequency modulated continuous wave(FMCW)radar is an advantageous sensor scheme for target estimation and environmental perception.However,existing algorithms based on discrete Fourier transform(DFT),multiple signal c...Frequency modulated continuous wave(FMCW)radar is an advantageous sensor scheme for target estimation and environmental perception.However,existing algorithms based on discrete Fourier transform(DFT),multiple signal classification(MUSIC)and compressed sensing,etc.,cannot achieve both low complexity and high resolution simultaneously.This paper proposes an efficient 2-D MUSIC algorithm for super-resolution target estimation/tracking based on FMCW radar.Firstly,we enhance the efficiency of 2-D MUSIC azimuth-range spectrum estimation by incorporating 2-D DFT and multi-level resolution searching strategy.Secondly,we apply the gradient descent method to tightly integrate the spatial continuity of object motion into spectrum estimation when processing multi-epoch radar data,which improves the efficiency of continuous target tracking.These two approaches have improved the algorithm efficiency by nearly 2-4 orders of magnitude without losing accuracy and resolution.Simulation experiments are conducted to validate the effectiveness of the algorithm in both single-epoch estimation and multi-epoch tracking scenarios.展开更多
Single Image Super-Resolution(SISR)technology aims to reconstruct a clear,high-resolution image with more information from an input low-resolution image that is blurry and contains less information.This technology has...Single Image Super-Resolution(SISR)technology aims to reconstruct a clear,high-resolution image with more information from an input low-resolution image that is blurry and contains less information.This technology has significant research value and is widely used in fields such as medical imaging,satellite image processing,and security surveillance.Despite significant progress in existing research,challenges remain in reconstructing clear and complex texture details,with issues such as edge blurring and artifacts still present.The visual perception effect still needs further enhancement.Therefore,this study proposes a Pyramid Separable Channel Attention Network(PSCAN)for the SISR task.Thismethod designs a convolutional backbone network composed of Pyramid Separable Channel Attention blocks to effectively extract and fuse multi-scale features.This expands the model’s receptive field,reduces resolution loss,and enhances the model’s ability to reconstruct texture details.Additionally,an innovative artifact loss function is designed to better distinguish between artifacts and real edge details,reducing artifacts in the reconstructed images.We conducted comprehensive ablation and comparative experiments on the Arabidopsis root image dataset and several public datasets.The experimental results show that the proposed PSCAN method achieves the best-known performance in both subjective visual effects and objective evaluation metrics,with improvements of 0.84 in Peak Signal-to-Noise Ratio(PSNR)and 0.017 in Structural Similarity Index(SSIM).This demonstrates that the method can effectively preserve high-frequency texture details,reduce artifacts,and have good generalization performance.展开更多
Digital in-line holographic microscopy(DIHM)is a widely used interference technique for real-time reconstruction of living cells’morphological information with large space-bandwidth product and compact setup.However,...Digital in-line holographic microscopy(DIHM)is a widely used interference technique for real-time reconstruction of living cells’morphological information with large space-bandwidth product and compact setup.However,the need for a larger pixel size of detector to improve imaging photosensitivity,field-of-view,and signal-to-noise ratio often leads to the loss of sub-pixel information and limited pixel resolution.Additionally,the twin-image appearing in the reconstruction severely degrades the quality of the reconstructed image.The deep learning(DL)approach has emerged as a powerful tool for phase retrieval in DIHM,effectively addressing these challenges.However,most DL-based strategies are datadriven or end-to-end net approaches,suffering from excessive data dependency and limited generalization ability.Herein,a novel multi-prior physics-enhanced neural network with pixel super-resolution(MPPN-PSR)for phase retrieval of DIHM is proposed.It encapsulates the physical model prior,sparsity prior and deep image prior in an untrained deep neural network.The effectiveness and feasibility of MPPN-PSR are demonstrated by comparing it with other traditional and learning-based phase retrieval methods.With the capabilities of pixel super-resolution,twin-image elimination and high-throughput jointly from a single-shot intensity measurement,the proposed DIHM approach is expected to be widely adopted in biomedical workflow and industrial measurement.展开更多
Analyzing polysorbate 20(PS20)composition and the impact of each component on stability and safety is crucial due to formulation variations and individual tolerance.The similar structures and polarities of PS20 compon...Analyzing polysorbate 20(PS20)composition and the impact of each component on stability and safety is crucial due to formulation variations and individual tolerance.The similar structures and polarities of PS20 components make accurate separation,identification,and quantification challenging.In this work,a high-resolution quantitative method was developed using single-dimensional high-performance liquid chromatography(HPLC)with charged aerosol detection(CAD)to separate 18 key components with multiple esters.The separated components were characterized by ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry(UHPLC-Q-TOF-MS)with an identical gradient as the HPLC-CAD analysis.The polysorbate compound database and library were expanded over 7-time compared to the commercial database.The method investigated differences in PS20 samples from various origins and grades for different dosage forms to evaluate the composition-process relationship.UHPLC-Q-TOF-MS identified 1329 to 1511 compounds in 4 batches of PS20 from different sources.The method observed the impact of 4 degradation conditions on peak components,identifying stable components and their tendencies to change.HPLC-CAD and UHPLC-Q-TOF-MS results provided insights into fingerprint differences,distinguishing quasi products.展开更多
Chinese wingnut(Pterocarya stenoptera)is a medicinally and economically important tree species within the family Juglandaceae.However,the lack of high-quality reference genome has hindered its in-depth research.In thi...Chinese wingnut(Pterocarya stenoptera)is a medicinally and economically important tree species within the family Juglandaceae.However,the lack of high-quality reference genome has hindered its in-depth research.In this study,we successfully assembled its chromosome-level genome and performed multiomics analyses to address its evolutionary history and synthesis of medicinal components.A thorough examination of genomes has uncovered a significant expansion in the Lateral Organ Boundaries Domain gene family among the winged group in Juglandaceae.This notable increase may be attributed to their frequent exposure to flood-prone environments.After further differentiation between Chinese wingnut and Cyclocarya paliurus,significant positive selection occurred on the genes of NADH dehydrogenase related to mitochondrial aerobic respiration in Chinese wingnut,enhancing its ability to cope with waterlogging stress.Comparative genomic analysis revealed Chinese wingnut evolved more unique genes related to arginine synthesis,potentially endowing it with a higher capacity to purify nutrient-rich water bodies.Expansion of terpene synthase families enables the production of increased quantities of terpenoid volatiles,potentially serving as an evolved defense mechanism against herbivorous insects.Through combined transcriptomic and metabolomic analysis,we identified the candidate genes involved in the synthesis of terpenoid volatiles.Our study offers essential genetic resources for Chinese wingnut,unveiling its evolutionary history and identifying key genes linked to the production of terpenoid volatiles.展开更多
In Kansas, productivity of grain sorghum [Sorghum bicolor (L.) Moench] is affected by weather conditions at planting and during pollination. Planting date management and selection of hybrid maturity group can help to ...In Kansas, productivity of grain sorghum [Sorghum bicolor (L.) Moench] is affected by weather conditions at planting and during pollination. Planting date management and selection of hybrid maturity group can help to avoid severe environmental stresses during these sensitive stages. The hypothesis of the study was that late May planting improves grain sorghum yield and yield components compared with late June planting. The objectives of this research were to investigate the influence of planting dates yield and yield components of different grain sorghum hybrids, and to determine the optimal planting date and hybrid combination for maximum biomass and grains production. Three sorghum hybrids (early, medium, and late maturing) were planted in late May and late June without irrigation in Kansas at Manhattan/Ashland Bottom Research Station, and Hutchinson in 2010;and at Manhattan/North Farm and Hutchinson in 2011. Data on dry matter production, yield and yield components were collected. Grain yield and yield components were influenced by planting date depending on environmental conditions. At Manhattan (2010), greater grain yield, number of heads per plant, were obtained with late-June planting compared with late May planting, while at Hutchinson (2010) greater yield was obtained with late May planting for all hybrids. The yield component most affected at Hutchinson was the number of kernels∙panicle<sup>−1</sup> and plant density. Late-May planting was favorable for late maturing hybrid (P84G62) in all locations. However, the yield of early maturing hybrid (DKS 28-05) and medium maturing hybrid (DKS 37-07) was less affected by delayed planting. The effects of planting dates on yield and yield components of grain sorghum hybrids were found to be variable among hybrid maturity groups and locations.展开更多
Although most of the existing image super-resolution(SR)methods have achieved superior performance,contrastive learning for high-level tasks has not been fully utilized in the existing image SR methods based on deep l...Although most of the existing image super-resolution(SR)methods have achieved superior performance,contrastive learning for high-level tasks has not been fully utilized in the existing image SR methods based on deep learning.This work focuses on two well-known strategies developed for lightweight and robust SR,i.e.,contrastive learning and feedback mechanism,and proposes an integrated solution called a split-based feedback network(SPFBN).The proposed SPFBN is based on a feedback mechanism to learn abstract representations and uses contrastive learning to explore high information in the representation space.Specifically,this work first uses hidden states and constraints in recurrent neural network(RNN)to implement a feedback mechanism.Then,use contrastive learning to perform representation learning to obtain high-level information by pushing the final image to the intermediate images and pulling the final SR image to the high-resolution image.Besides,a split-based feedback block(SPFB)is proposed to reduce model redundancy,which tolerates features with similar patterns but requires fewer parameters.Extensive experimental results demonstrate the superiority of the proposed method in comparison with the state-of-the-art methods.Moreover,this work extends the experiment to prove the effectiveness of this method and shows better overall reconstruction quality.展开更多
The isolation of minor components from complex natural product matrices presents a significant challenge in the field of purification science due to their low concentrations and the presence of structurally similar co...The isolation of minor components from complex natural product matrices presents a significant challenge in the field of purification science due to their low concentrations and the presence of structurally similar compounds.This study introduces an optimized twin-column recycling chromatography method for the efficient and simultaneous purification of these elusive constituents.By introducing water at a small flowing rate between the twin columns,a step solvent gradient is created,by which the leading edge of concentration band would migrate at a slower rate than the trailing edge as it flowing from the upstream to downstream column.Hence,the band broadening is counterbalanced,resulting in an enrichment effect for those minor components in separation process.Herein,two target substances,which showed similar peak position in high performance liquid chromatography(HPLC)and did not exceed 1.8%in crude paclitaxel were selected as target compounds for separation.By using the twin-column recycling chromatography with a step solvent gradient,a successful purification was achieved in getting the two with the purity almost 100%.We suggest this method is suitable for the separation of most components in natural produces,which shows higher precision and recovery rate compared with the common lab-operated separation ways for natural products(thin-layer chromatography and prep-HPLC).展开更多
Ternary strategy with a suitable third component is a successful strategy to improve the photovoltaic performance of organic solar cells(OSCs).Very recently,Y-series based giant molecule acceptors or oligomerized acce...Ternary strategy with a suitable third component is a successful strategy to improve the photovoltaic performance of organic solar cells(OSCs).Very recently,Y-series based giant molecule acceptors or oligomerized acceptors have emerged as promising materials for achieving highly efficient and stable binary OSCs,while application as third component for ternary OSCs is limited.Here a novelπ-extended giant dimeric acceptor,GDF,is developed based on central Y series core fusion and rigid BDT as linker,and then incorporated into the state-of-the-art PM1:PC6 system to construct ternary OSCs.The GDF has a near planar backbone,resulting in increasedπ-conjugation,excellent crystallinity,and good electron transport capacity.When GDF is introduced into the PM1:PC6 system,it ensues in a cascade like the lowest unoccupied molecular orbitals(LUMO)energy level alignment,a complementary absorption band with PM1 and PC6,higher and balanced hole and electron mobility,slightly smaller domain size,and a higher exciton dissociation probability for PM1:PC6:GDF(1:1.1:0.1)blend film.As a consequence,the PM1:PC6:GDF(1:1.1:0.1)ternary OSC achieves a champion PCE of 19.22%,with a significantly higher open-circuit voltage and short-circuit current density,compared to 18.45%for the PM1:PC6(1:1.2)binary OSC.Our findings show that employing aπ-extended giant dimeric acceptor as a third component significantly improves the photovoltaic performance of ternary OSCs.展开更多
Lightweight thin-walled structures with lattice infill are widely desired in satellite for their high stiffness-to-weight ratio and superior buckling strength resulting fromthe sandwich effect.Such structures can be f...Lightweight thin-walled structures with lattice infill are widely desired in satellite for their high stiffness-to-weight ratio and superior buckling strength resulting fromthe sandwich effect.Such structures can be fabricated bymetallic additive manufacturing technique,such as selective laser melting(SLM).However,the maximum dimensions of actual structures are usually in a sub-meter scale,which results in restrictions on their appliance in aerospace and other fields.In this work,a meter-scale thin-walled structure with lattice infill is designed for the fuel tank supporting component of the satellite by integrating a self-supporting lattice into the thickness optimization of the thin-wall.The designed structure is fabricated by SLM of AlSi10Mg and cold metal transfer welding technique.Quasi-static mechanical tests and vibration tests are both conducted to verify the mechanical strength of the designed large-scale lattice thin-walled structure.The experimental results indicate that themeter-scale thin-walled structure with lattice infill could meet the dimension and lightweight requirements of most spacecrafts.展开更多
Objective:To determine the active components of Eupolyphaga sinensis Walker(Tu Bie Chong)and explore the mechanisms underlying its fracture-healing ability.Methods: A modified Einhorn method was used to develop a rat ...Objective:To determine the active components of Eupolyphaga sinensis Walker(Tu Bie Chong)and explore the mechanisms underlying its fracture-healing ability.Methods: A modified Einhorn method was used to develop a rat tibial fracture model.Progression of bone healing was assessed using radiological methods.Safranin O/fast green and CD31 immunohistochemical staining were performed to evaluate the growth of bone cells and angiogenesis at the fracture site.Methylthiazoletetrazolium blue and wound healing assays were used to analyze cell viability and migration.The Transwell assay was used to explore the invasion capacity of the cells.Tubule formation assays were used to assess the angiogenesis capacity of human vascular endothelial cells(HUVECs).qRT-PCR was used to evaluate the changes in gene transcription levels.Results: Tu Bie Chong fraction 3(TF3)significantly shortened the fracture healing time in model rats.X-ray results showed that on day 14,fracture healing in the TF3 treatment group was significantly better than that in the control group(P=.0086).Tissue staining showed that cartilage growth and the number of H-shaped blood vessels at the fracture site of the TF3 treatment group were better than those of the control group.In vitro,TF3 significantly promoted the proliferation and wound healing of MC3T3-E1s and HUVECs(all P<.01).Transwell assays showed that TF3 promoted the migration of HUVECs,but inhibited the migration of MC3T3-E1 cells.Tubule formation experiments confirmed that TF3 markedly promoted the ability of vascular endothelial cells to form microtubules.Gene expression analysis revealed that TF3 significantly promoted the expression of VEGFA,SPOCD1,NGF,and NGFR in HUVECs.In MC3T3-E1 cells,the transcript levels of RUNX2 and COL2A1 were significantly elevated following TF3 treatment.Conclusion: TF3 promotes fracture healing by promoting bone regeneration associated with the RUNX2 pathway and angiogenesis associated with the VEGFA pathway.展开更多
The increasing dependence on data highlights the need for a detailed understanding of its behavior,encompassing the challenges involved in processing and evaluating it.However,current research lacks a comprehensive st...The increasing dependence on data highlights the need for a detailed understanding of its behavior,encompassing the challenges involved in processing and evaluating it.However,current research lacks a comprehensive structure for measuring the worth of data elements,hindering effective navigation of the changing digital environment.This paper aims to fill this research gap by introducing the innovative concept of“data components.”It proposes a graphtheoretic representation model that presents a clear mathematical definition and demonstrates the superiority of data components over traditional processing methods.Additionally,the paper introduces an information measurement model that provides a way to calculate the information entropy of data components and establish their increased informational value.The paper also assesses the value of information,suggesting a pricing mechanism based on its significance.In conclusion,this paper establishes a robust framework for understanding and quantifying the value of implicit information in data,laying the groundwork for future research and practical applications.展开更多
基金supported by the key project at the central government level:The ability establishment of sustainable use for valuable Chinese medicine resources(Grant number 2060302)the National Natural Science Foundation of China(Grant number 82373982,82173929).
文摘Background:Rosa chinensis Jacq.and Rosa rugosa Thunb.are not only of ornamental value,but also edible flowers and the flower buds have been listed in the Chinese Pharmacopoeia as traditional medicines.The two plants have some differences in efficacy,but the flower buds are easily confused for similar traits.In addition,large-scale cultivation of ornamental rose flowers may lead to a decrease in the effective components of medicinal roses.Therefore,it is necessary to study the chemical composition and make quality evaluation of Rosae Chinensis Flos(Yueji)and Rosae Rugosae Flos(Meigui).Methods:In this study,40 batches of samples including Meigui and Yueji from different regions in China were collected to establish high-performance liquid chromatography fingerprints.Then,the fingerprints data was analyzed using principal component analysis,hierarchical cluster analysis,and partial least squares discriminant analysis analysis chemometrics to obtain information on intergroup differences,and non-targeted metabolomic techniques were applied to identify and compare chemical compositions of samples which were chosen from groups with large differences.Differential compounds were screened by orthogonal partial least-squares discriminant analysis and S-plot,and finally multi-component quantification was performed to comprehensively evaluate the quality of Yueji and Meigui.Results:The similarity between the fingerprints of 40 batches roses and the reference print R was 0.73 to 0.93,indicating that there were similarities and differences between the samples.Through principal component analysis and hierarchical cluster analysis of fingerprints data,the samples from different origins and varieties were intuitively divided into four groups.Partial least-squares discriminant analysis analysis showed that Meigui and Yueji cluster into two categories and the model was reliable.A total of 89 compounds were identified by high resolution mass spectrometry,mainly were flavonoids and flavonoid glycosides,as well as phenolic acids.Eight differential components were screened out by orthogonal partial least-squares discriminant analysis and S-plot analysis.Quantitative analyses of the eight compounds,including gallic acid,ellagic acid,hyperoside,isoquercitrin,etc.,showed that Yueji was generally richer in phenolic acids and flavonoids than Meigui,and the quality of Yueji from Shandong and Hebei was better.It is worth noting that Xinjiang rose is rich in various components,which is worth focusing on more in-depth research.Conclusion:In this study,the fingerprints of Meigui and Yueji were established.The chemical components information of roses was further improved based on non-targeted metabolomics and mass spectrometry technology.At the same time,eight differential components of Meigui and Yueji were screened out and quantitatively analyzed.The research results provided a scientific basis for the quality control and rational development and utilization of Rosae Chinensis Flos and Rosae Rugosae Flos,and also laid a foundation for the study of their pharmacodynamic material basis.
基金supported by the National Natural Science Foundation of China(Nos.52075255,92160301,52175415,52205475,and 92060203)。
文摘The aerospace community widely uses difficult-to-cut materials,such as titanium alloys,high-temperature alloys,metal/ceramic/polymer matrix composites,hard and brittle materials,and geometrically complex components,such as thin-walled structures,microchannels,and complex surfaces.Mechanical machining is the main material removal process for the vast majority of aerospace components.However,many problems exist,including severe and rapid tool wear,low machining efficiency,and poor surface integrity.Nontraditional energy-assisted mechanical machining is a hybrid process that uses nontraditional energies(vibration,laser,electricity,etc)to improve the machinability of local materials and decrease the burden of mechanical machining.This provides a feasible and promising method to improve the material removal rate and surface quality,reduce process forces,and prolong tool life.However,systematic reviews of this technology are lacking with respect to the current research status and development direction.This paper reviews the recent progress in the nontraditional energy-assisted mechanical machining of difficult-to-cut materials and components in the aerospace community.In addition,this paper focuses on the processing principles,material responses under nontraditional energy,resultant forces and temperatures,material removal mechanisms,and applications of these processes,including vibration-,laser-,electric-,magnetic-,chemical-,advanced coolant-,and hybrid nontraditional energy-assisted mechanical machining.Finally,a comprehensive summary of the principles,advantages,and limitations of each hybrid process is provided,and future perspectives on forward design,device development,and sustainability of nontraditional energy-assisted mechanical machining processes are discussed.
基金the support of the National Natural Science Foundation of China(Grant No.12302440)China Postdoctoral Science Foundation(Grant No.2023M741713)。
文摘As a kind of high-efficiency explosive with compound destructive capability, the energy output law of thermobaric explosives has been receiving great attention. In order to investigate the effects of main components on the explosive characteristics of thermobaric explosives, various high explosives and oxidants were selected to formulate five different types of thermobaric explosive. Then they were tested in both open space and closed space respectively. Pressure measurement system, high-speed camera,infrared thermal imager and multispectral temperature measurement system were used for pressure,temperature and fireball recording. The effects of different components on the explosive characteristics of thermobaric explosive were analyzed. The results showed that in open space, the overpressure is dominated by the high explosives content in the formulation. The addition of the oxidants will decrease the explosion overpressure but will increase the duration and overall brightness of the fireball. While in closed space, the quasi-static pressure formed after the explosion is positively correlated with the temperature and gas production. In addition, it was found that the differences in shell constraints can also alter the afterburning reaction of thermobaric explosives, thus affecting their energy output characteristics. PVC shell constraint obviously increases the overpressure and makes the fireball burn more violently.
基金funded by Shenzhen Science and Technology Innovation Commission(KCXFZ20201221173207022)。
文摘Andrias davidianus(Chinese giant salamander,CGS)is the largest and oldest extant amphibian species in the world and is a source of prospective functional food in China.However,the progress of functional peptides mining was slow due to lack of reference genome and protein sequence data.In this study,we illustrated full-length transcriptome sequencing to interpret the proteome of CGS meat and obtain 10703 coding DNA sequences.By functional annotation and amino acid composition analysis,we have discovered various genes related to signal transduction,and 16 genes related to longevity.We have also found vast variety of functional peptides through protein coding sequence(CDS)analysis by comparing the data obtained with the functional peptide database.Val-Pro-Ile predicted by the CDS analysis was released from the CGS meat through enzymatic hydrolysis,suggesting that our approach is reliable.This study suggested that transcriptomic analysis can be used as a reference to guide polypeptide mining in CGS meat,thereby providing a powerful mining strategy for the bioresources with unknown genomic and proteomic sequences.
基金This work was supported by the Pilot Seed Grant(Grant No.RES0049944)the Collaborative Research Project(Grant No.RES0043251)from the University of Alberta.
文摘Ore production is usually affected by multiple influencing inputs at open-pit mines.Nevertheless,the complex nonlinear relationships between these inputs and ore production remain unclear.This becomes even more challenging when training data(e.g.truck haulage information and weather conditions)are massive.In machine learning(ML)algorithms,deep neural network(DNN)is a superior method for processing nonlinear and massive data by adjusting the amount of neurons and hidden layers.This study adopted DNN to forecast ore production using truck haulage information and weather conditions at open-pit mines as training data.Before the prediction models were built,principal component analysis(PCA)was employed to reduce the data dimensionality and eliminate the multicollinearity among highly correlated input variables.To verify the superiority of DNN,three ANNs containing only one hidden layer and six traditional ML models were established as benchmark models.The DNN model with multiple hidden layers performed better than the ANN models with a single hidden layer.The DNN model outperformed the extensively applied benchmark models in predicting ore production.This can provide engineers and researchers with an accurate method to forecast ore production,which helps make sound budgetary decisions and mine planning at open-pit mines.
文摘Super-resolution techniques are employed to enhance image resolution by reconstructing high-resolution images from one or more low-resolution inputs.Super-resolution is of paramount importance in the context of remote sensing,satellite,aerial,security and surveillance imaging.Super-resolution remote sensing imagery is essential for surveillance and security purposes,enabling authorities to monitor remote or sensitive areas with greater clarity.This study introduces a single-image super-resolution approach for remote sensing images,utilizing deep shearlet residual learning in the shearlet transform domain,and incorporating the Enhanced Deep Super-Resolution network(EDSR).Unlike conventional approaches that estimate residuals between high and low-resolution images,the proposed approach calculates the shearlet coefficients for the desired high-resolution image using the provided low-resolution image instead of estimating a residual image between the high-and low-resolution image.The shearlet transform is chosen for its excellent sparse approximation capabilities.Initially,remote sensing images are transformed into the shearlet domain,which divides the input image into low and high frequencies.The shearlet coefficients are fed into the EDSR network.The high-resolution image is subsequently reconstructed using the inverse shearlet transform.The incorporation of the EDSR network enhances training stability,leading to improved generated images.The experimental results from the Deep Shearlet Residual Learning approach demonstrate its superior performance in remote sensing image recovery,effectively restoring both global topology and local edge detail information,thereby enhancing image quality.Compared to other networks,our proposed approach outperforms the state-of-the-art in terms of image quality,achieving an average peak signal-to-noise ratio of 35 and a structural similarity index measure of approximately 0.9.
基金Supported by Sichuan Science and Technology Program(2021YFQ0003,2023YFSY0026,2023YFH0004).
文摘At present,super-resolution algorithms are employed to tackle the challenge of low image resolution,but it is difficult to extract differentiated feature details based on various inputs,resulting in poor generalization ability.Given this situation,this study first analyzes the features of some feature extraction modules of the current super-resolution algorithm and then proposes an adaptive feature fusion block(AFB)for feature extraction.This module mainly comprises dynamic convolution,attention mechanism,and pixel-based gating mechanism.Combined with dynamic convolution with scale information,the network can extract more differentiated feature information.The introduction of a channel spatial attention mechanism combined with multi-feature fusion further enables the network to retain more important feature information.Dynamic convolution and pixel-based gating mechanisms enhance the module’s adaptability.Finally,a comparative experiment of a super-resolution algorithm based on the AFB module is designed to substantiate the efficiency of the AFB module.The results revealed that the network combined with the AFB module has stronger generalization ability and expression ability.
基金Guangdong Science and Technology Program under Grant No.202206010052Foshan Province R&D Key Project under Grant No.2020001006827Guangdong Academy of Sciences Integrated Industry Technology Innovation Center Action Special Project under Grant No.2022GDASZH-2022010108.
文摘The employment of deep convolutional neural networks has recently contributed to significant progress in single image super-resolution(SISR)research.However,the high computational demands of most SR techniques hinder their applicability to edge devices,despite their satisfactory reconstruction performance.These methods commonly use standard convolutions,which increase the convolutional operation cost of the model.In this paper,a lightweight Partial Separation and Multiscale Fusion Network(PSMFNet)is proposed to alleviate this problem.Specifically,this paper introduces partial convolution(PConv),which reduces the redundant convolution operations throughout the model by separating some of the features of an image while retaining features useful for image reconstruction.Additionally,it is worth noting that the existing methods have not fully utilized the rich feature information,leading to information loss,which reduces the ability to learn feature representations.Inspired by self-attention,this paper develops a multiscale feature fusion block(MFFB),which can better utilize the non-local features of an image.MFFB can learn long-range dependencies from the spatial dimension and extract features from the channel dimension,thereby obtaining more comprehensive and rich feature information.As the role of the MFFB is to capture rich global features,this paper further introduces an efficient inverted residual block(EIRB)to supplement the local feature extraction ability of PSMFNet.A comprehensive analysis of the experimental results shows that PSMFNet maintains a better performance with fewer parameters than the state-of-the-art models.
基金funded by the National Natural Science Foundation of China,grant number 42074176,U1939204。
文摘Frequency modulated continuous wave(FMCW)radar is an advantageous sensor scheme for target estimation and environmental perception.However,existing algorithms based on discrete Fourier transform(DFT),multiple signal classification(MUSIC)and compressed sensing,etc.,cannot achieve both low complexity and high resolution simultaneously.This paper proposes an efficient 2-D MUSIC algorithm for super-resolution target estimation/tracking based on FMCW radar.Firstly,we enhance the efficiency of 2-D MUSIC azimuth-range spectrum estimation by incorporating 2-D DFT and multi-level resolution searching strategy.Secondly,we apply the gradient descent method to tightly integrate the spatial continuity of object motion into spectrum estimation when processing multi-epoch radar data,which improves the efficiency of continuous target tracking.These two approaches have improved the algorithm efficiency by nearly 2-4 orders of magnitude without losing accuracy and resolution.Simulation experiments are conducted to validate the effectiveness of the algorithm in both single-epoch estimation and multi-epoch tracking scenarios.
基金supported by Beijing Municipal Science and Technology Project(No.Z221100007122003).
文摘Single Image Super-Resolution(SISR)technology aims to reconstruct a clear,high-resolution image with more information from an input low-resolution image that is blurry and contains less information.This technology has significant research value and is widely used in fields such as medical imaging,satellite image processing,and security surveillance.Despite significant progress in existing research,challenges remain in reconstructing clear and complex texture details,with issues such as edge blurring and artifacts still present.The visual perception effect still needs further enhancement.Therefore,this study proposes a Pyramid Separable Channel Attention Network(PSCAN)for the SISR task.Thismethod designs a convolutional backbone network composed of Pyramid Separable Channel Attention blocks to effectively extract and fuse multi-scale features.This expands the model’s receptive field,reduces resolution loss,and enhances the model’s ability to reconstruct texture details.Additionally,an innovative artifact loss function is designed to better distinguish between artifacts and real edge details,reducing artifacts in the reconstructed images.We conducted comprehensive ablation and comparative experiments on the Arabidopsis root image dataset and several public datasets.The experimental results show that the proposed PSCAN method achieves the best-known performance in both subjective visual effects and objective evaluation metrics,with improvements of 0.84 in Peak Signal-to-Noise Ratio(PSNR)and 0.017 in Structural Similarity Index(SSIM).This demonstrates that the method can effectively preserve high-frequency texture details,reduce artifacts,and have good generalization performance.
基金National Natural Science Foundation of China (62275267, 62335018, 12127805, 62105359)National Key Research and Development Program of China (2021YFF0700303, 2022YFE0100700)Youth Innovation Promotion Association, CAS (2021401)
文摘Digital in-line holographic microscopy(DIHM)is a widely used interference technique for real-time reconstruction of living cells’morphological information with large space-bandwidth product and compact setup.However,the need for a larger pixel size of detector to improve imaging photosensitivity,field-of-view,and signal-to-noise ratio often leads to the loss of sub-pixel information and limited pixel resolution.Additionally,the twin-image appearing in the reconstruction severely degrades the quality of the reconstructed image.The deep learning(DL)approach has emerged as a powerful tool for phase retrieval in DIHM,effectively addressing these challenges.However,most DL-based strategies are datadriven or end-to-end net approaches,suffering from excessive data dependency and limited generalization ability.Herein,a novel multi-prior physics-enhanced neural network with pixel super-resolution(MPPN-PSR)for phase retrieval of DIHM is proposed.It encapsulates the physical model prior,sparsity prior and deep image prior in an untrained deep neural network.The effectiveness and feasibility of MPPN-PSR are demonstrated by comparing it with other traditional and learning-based phase retrieval methods.With the capabilities of pixel super-resolution,twin-image elimination and high-throughput jointly from a single-shot intensity measurement,the proposed DIHM approach is expected to be widely adopted in biomedical workflow and industrial measurement.
基金financial support from the Science Research Program Project for Drug Regulation,Jiangsu Drug Administration,China(Grant No.:202207)the National Drug Standards Revision Project,China(Grant No.:2023Y41)+1 种基金the National Natural Science Foundation of China(Grant No.:22276080)the Foreign Expert Project,China(Grant No.:G2022014096L).
文摘Analyzing polysorbate 20(PS20)composition and the impact of each component on stability and safety is crucial due to formulation variations and individual tolerance.The similar structures and polarities of PS20 components make accurate separation,identification,and quantification challenging.In this work,a high-resolution quantitative method was developed using single-dimensional high-performance liquid chromatography(HPLC)with charged aerosol detection(CAD)to separate 18 key components with multiple esters.The separated components were characterized by ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry(UHPLC-Q-TOF-MS)with an identical gradient as the HPLC-CAD analysis.The polysorbate compound database and library were expanded over 7-time compared to the commercial database.The method investigated differences in PS20 samples from various origins and grades for different dosage forms to evaluate the composition-process relationship.UHPLC-Q-TOF-MS identified 1329 to 1511 compounds in 4 batches of PS20 from different sources.The method observed the impact of 4 degradation conditions on peak components,identifying stable components and their tendencies to change.HPLC-CAD and UHPLC-Q-TOF-MS results provided insights into fingerprint differences,distinguishing quasi products.
基金supported by National Natural Science Foundation of China(32360307)the Natural Science Foundation of Inner Mongolia(2023MS03031)+1 种基金Inner Mongolia Grassland Talents Project(3211002406)the Open Fund of State Key Laboratory of Tree Genetics and Breeding(Chinese Academy of Forestry)(Grant No.TGB2021004).
文摘Chinese wingnut(Pterocarya stenoptera)is a medicinally and economically important tree species within the family Juglandaceae.However,the lack of high-quality reference genome has hindered its in-depth research.In this study,we successfully assembled its chromosome-level genome and performed multiomics analyses to address its evolutionary history and synthesis of medicinal components.A thorough examination of genomes has uncovered a significant expansion in the Lateral Organ Boundaries Domain gene family among the winged group in Juglandaceae.This notable increase may be attributed to their frequent exposure to flood-prone environments.After further differentiation between Chinese wingnut and Cyclocarya paliurus,significant positive selection occurred on the genes of NADH dehydrogenase related to mitochondrial aerobic respiration in Chinese wingnut,enhancing its ability to cope with waterlogging stress.Comparative genomic analysis revealed Chinese wingnut evolved more unique genes related to arginine synthesis,potentially endowing it with a higher capacity to purify nutrient-rich water bodies.Expansion of terpene synthase families enables the production of increased quantities of terpenoid volatiles,potentially serving as an evolved defense mechanism against herbivorous insects.Through combined transcriptomic and metabolomic analysis,we identified the candidate genes involved in the synthesis of terpenoid volatiles.Our study offers essential genetic resources for Chinese wingnut,unveiling its evolutionary history and identifying key genes linked to the production of terpenoid volatiles.
文摘In Kansas, productivity of grain sorghum [Sorghum bicolor (L.) Moench] is affected by weather conditions at planting and during pollination. Planting date management and selection of hybrid maturity group can help to avoid severe environmental stresses during these sensitive stages. The hypothesis of the study was that late May planting improves grain sorghum yield and yield components compared with late June planting. The objectives of this research were to investigate the influence of planting dates yield and yield components of different grain sorghum hybrids, and to determine the optimal planting date and hybrid combination for maximum biomass and grains production. Three sorghum hybrids (early, medium, and late maturing) were planted in late May and late June without irrigation in Kansas at Manhattan/Ashland Bottom Research Station, and Hutchinson in 2010;and at Manhattan/North Farm and Hutchinson in 2011. Data on dry matter production, yield and yield components were collected. Grain yield and yield components were influenced by planting date depending on environmental conditions. At Manhattan (2010), greater grain yield, number of heads per plant, were obtained with late-June planting compared with late May planting, while at Hutchinson (2010) greater yield was obtained with late May planting for all hybrids. The yield component most affected at Hutchinson was the number of kernels∙panicle<sup>−1</sup> and plant density. Late-May planting was favorable for late maturing hybrid (P84G62) in all locations. However, the yield of early maturing hybrid (DKS 28-05) and medium maturing hybrid (DKS 37-07) was less affected by delayed planting. The effects of planting dates on yield and yield components of grain sorghum hybrids were found to be variable among hybrid maturity groups and locations.
基金the National Key R&D Program of China(No.2019YFB1405900)the National Natural Science Foundation of China(No.62172035,61976098)。
文摘Although most of the existing image super-resolution(SR)methods have achieved superior performance,contrastive learning for high-level tasks has not been fully utilized in the existing image SR methods based on deep learning.This work focuses on two well-known strategies developed for lightweight and robust SR,i.e.,contrastive learning and feedback mechanism,and proposes an integrated solution called a split-based feedback network(SPFBN).The proposed SPFBN is based on a feedback mechanism to learn abstract representations and uses contrastive learning to explore high information in the representation space.Specifically,this work first uses hidden states and constraints in recurrent neural network(RNN)to implement a feedback mechanism.Then,use contrastive learning to perform representation learning to obtain high-level information by pushing the final image to the intermediate images and pulling the final SR image to the high-resolution image.Besides,a split-based feedback block(SPFB)is proposed to reduce model redundancy,which tolerates features with similar patterns but requires fewer parameters.Extensive experimental results demonstrate the superiority of the proposed method in comparison with the state-of-the-art methods.Moreover,this work extends the experiment to prove the effectiveness of this method and shows better overall reconstruction quality.
基金supported by the National Natural Science Foundation of China(22078281)。
文摘The isolation of minor components from complex natural product matrices presents a significant challenge in the field of purification science due to their low concentrations and the presence of structurally similar compounds.This study introduces an optimized twin-column recycling chromatography method for the efficient and simultaneous purification of these elusive constituents.By introducing water at a small flowing rate between the twin columns,a step solvent gradient is created,by which the leading edge of concentration band would migrate at a slower rate than the trailing edge as it flowing from the upstream to downstream column.Hence,the band broadening is counterbalanced,resulting in an enrichment effect for those minor components in separation process.Herein,two target substances,which showed similar peak position in high performance liquid chromatography(HPLC)and did not exceed 1.8%in crude paclitaxel were selected as target compounds for separation.By using the twin-column recycling chromatography with a step solvent gradient,a successful purification was achieved in getting the two with the purity almost 100%.We suggest this method is suitable for the separation of most components in natural produces,which shows higher precision and recovery rate compared with the common lab-operated separation ways for natural products(thin-layer chromatography and prep-HPLC).
基金supported by the Yunnan Fundamental Research Project(202301BF070001-009,KC-22222357)the Sichuan Science and Technology Program(2023NSFSC0990)the School of Materials Science and Engineering,Jiangsu Engineering Laboratory of Light-Electricity-Heat Energy-Converting Materials and Applications。
文摘Ternary strategy with a suitable third component is a successful strategy to improve the photovoltaic performance of organic solar cells(OSCs).Very recently,Y-series based giant molecule acceptors or oligomerized acceptors have emerged as promising materials for achieving highly efficient and stable binary OSCs,while application as third component for ternary OSCs is limited.Here a novelπ-extended giant dimeric acceptor,GDF,is developed based on central Y series core fusion and rigid BDT as linker,and then incorporated into the state-of-the-art PM1:PC6 system to construct ternary OSCs.The GDF has a near planar backbone,resulting in increasedπ-conjugation,excellent crystallinity,and good electron transport capacity.When GDF is introduced into the PM1:PC6 system,it ensues in a cascade like the lowest unoccupied molecular orbitals(LUMO)energy level alignment,a complementary absorption band with PM1 and PC6,higher and balanced hole and electron mobility,slightly smaller domain size,and a higher exciton dissociation probability for PM1:PC6:GDF(1:1.1:0.1)blend film.As a consequence,the PM1:PC6:GDF(1:1.1:0.1)ternary OSC achieves a champion PCE of 19.22%,with a significantly higher open-circuit voltage and short-circuit current density,compared to 18.45%for the PM1:PC6(1:1.2)binary OSC.Our findings show that employing aπ-extended giant dimeric acceptor as a third component significantly improves the photovoltaic performance of ternary OSCs.
基金The authors are grateful for the support by National Key Research and Development Program of China(2021YFF0500300,2020YFB1708300)the National Natural Science Foundation of China(52205280,12172041).
文摘Lightweight thin-walled structures with lattice infill are widely desired in satellite for their high stiffness-to-weight ratio and superior buckling strength resulting fromthe sandwich effect.Such structures can be fabricated bymetallic additive manufacturing technique,such as selective laser melting(SLM).However,the maximum dimensions of actual structures are usually in a sub-meter scale,which results in restrictions on their appliance in aerospace and other fields.In this work,a meter-scale thin-walled structure with lattice infill is designed for the fuel tank supporting component of the satellite by integrating a self-supporting lattice into the thickness optimization of the thin-wall.The designed structure is fabricated by SLM of AlSi10Mg and cold metal transfer welding technique.Quasi-static mechanical tests and vibration tests are both conducted to verify the mechanical strength of the designed large-scale lattice thin-walled structure.The experimental results indicate that themeter-scale thin-walled structure with lattice infill could meet the dimension and lightweight requirements of most spacecrafts.
基金supported by“the Fundamental Research Funds for the Central Universities”(2020-JYB-ZDGG-054)“Beijing university of Chinese medicine XINAO Award Fund”(2019)Beijing University of Chinese Medicine Scientific Research and Development Fund(2170072220002).
文摘Objective:To determine the active components of Eupolyphaga sinensis Walker(Tu Bie Chong)and explore the mechanisms underlying its fracture-healing ability.Methods: A modified Einhorn method was used to develop a rat tibial fracture model.Progression of bone healing was assessed using radiological methods.Safranin O/fast green and CD31 immunohistochemical staining were performed to evaluate the growth of bone cells and angiogenesis at the fracture site.Methylthiazoletetrazolium blue and wound healing assays were used to analyze cell viability and migration.The Transwell assay was used to explore the invasion capacity of the cells.Tubule formation assays were used to assess the angiogenesis capacity of human vascular endothelial cells(HUVECs).qRT-PCR was used to evaluate the changes in gene transcription levels.Results: Tu Bie Chong fraction 3(TF3)significantly shortened the fracture healing time in model rats.X-ray results showed that on day 14,fracture healing in the TF3 treatment group was significantly better than that in the control group(P=.0086).Tissue staining showed that cartilage growth and the number of H-shaped blood vessels at the fracture site of the TF3 treatment group were better than those of the control group.In vitro,TF3 significantly promoted the proliferation and wound healing of MC3T3-E1s and HUVECs(all P<.01).Transwell assays showed that TF3 promoted the migration of HUVECs,but inhibited the migration of MC3T3-E1 cells.Tubule formation experiments confirmed that TF3 markedly promoted the ability of vascular endothelial cells to form microtubules.Gene expression analysis revealed that TF3 significantly promoted the expression of VEGFA,SPOCD1,NGF,and NGFR in HUVECs.In MC3T3-E1 cells,the transcript levels of RUNX2 and COL2A1 were significantly elevated following TF3 treatment.Conclusion: TF3 promotes fracture healing by promoting bone regeneration associated with the RUNX2 pathway and angiogenesis associated with the VEGFA pathway.
基金supported by the EU H2020 Research and Innovation Program under the Marie Sklodowska-Curie Grant Agreement(Project-DEEP,Grant number:101109045)National Key R&D Program of China with Grant number 2018YFB1800804+2 种基金the National Natural Science Foundation of China(Nos.NSFC 61925105,and 62171257)Tsinghua University-China Mobile Communications Group Co.,Ltd,Joint Institutethe Fundamental Research Funds for the Central Universities,China(No.FRF-NP-20-03)。
文摘The increasing dependence on data highlights the need for a detailed understanding of its behavior,encompassing the challenges involved in processing and evaluating it.However,current research lacks a comprehensive structure for measuring the worth of data elements,hindering effective navigation of the changing digital environment.This paper aims to fill this research gap by introducing the innovative concept of“data components.”It proposes a graphtheoretic representation model that presents a clear mathematical definition and demonstrates the superiority of data components over traditional processing methods.Additionally,the paper introduces an information measurement model that provides a way to calculate the information entropy of data components and establish their increased informational value.The paper also assesses the value of information,suggesting a pricing mechanism based on its significance.In conclusion,this paper establishes a robust framework for understanding and quantifying the value of implicit information in data,laying the groundwork for future research and practical applications.