Dynamic brain networks play a pivotal role in diagnosing brain disorders by capturing temporal changes in brain activity and connectivity. Previous methods often rely on sliding-window approaches for constructing thes...
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Tomato(Solanum lycopersicum), an economically important vegetable crop cultivated worldwide, often suffers massive financial losses due to Phytophthora infestans(P. infestans) spread and breakouts. Arbuscular mycorrhi...
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Tomato(Solanum lycopersicum), an economically important vegetable crop cultivated worldwide, often suffers massive financial losses due to Phytophthora infestans(P. infestans) spread and breakouts. Arbuscular mycorrhiza(AM) fungi mediated biocontrol has demonstrated great potential in plant resistance. However, little information is available on the regulation of mycorrhizal tomato resistance against P. ***, microRNAs(miRNAs) sequencing technology was used to analyse miRNA and their targets in the mycorrhizal tomato after *** infection. Our study showed a lower severity of necrotic lesions in mycorrhizal tomato than in nonmycorrhizal controls. We investigated 35 miRNAs that showed the opposite expression tendency in mycorrhizal and nonmycorrhizal tomato after P. infestans infection when compared with uninfected P. infestans. Among them, miR319c was upregulated in mycorrhizal tomato leaves after pathogen infection. Overexpression of miR319c or silencing of its target gene(TCP1) increased tomato resistance to P. infestans, implying that miR319c acts as a positive regulator in tomato after pathogen infection. Additionally, we examined the induced expression patterns of miR319c and TCP1 in tomato plants exposed to salicylic acid(SA) treatment, and SA content and the expression levels of SA-related genes were also measured in overexpressing transgenic plants. The result revealed that miR319c can not only participates in tomato resistance to P. infestans by regulating SA content, but also indirectly regulates the expression levels of key genes in the SA pathway by regulating TCP1. In this study, we propose a novel mechanism in which the miR319c in mycorrhizal tomato increases resistance to P. infestans.
Witness encryption(WE) is a novel type of cryptographic primitive that enables a message to be encrypted via an NP instance. Anyone who possesses a solution to this instance(i.e., a witness) can then recover the messa...
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Witness encryption(WE) is a novel type of cryptographic primitive that enables a message to be encrypted via an NP instance. Anyone who possesses a solution to this instance(i.e., a witness) can then recover the message from the *** introduce a variant of WE that allows ciphertext updates, referred to as ciphertext updateable WE(CUWE). With CUWE,a user can encrypt a message using an instance x and a tag t, and those who possess a valid witness w for x and match the access policy defined by tag t can decrypt the message. Furthermore, CUWE allows for the use of an update token to change the tag t of ciphertext to a different tag. This feature enables fine-grained access control, even after the ciphertext has been created, thereby significantly increasing the usefulness of the WE scheme. We demonstrate that such a WE framework with an updatable ciphertext scheme can be constructed using our puncturable instance-based deterministic encryption(PIDE) and indistinguishability obfuscation(iO). We also propose an instantiation of PIDE utilizing puncturable pseudorandom functions(PRFs) that provide(selectively) indistinguishable security. Finally, we expand our CUWE to ciphertext-updatable functional WE(CUFWE), which offers enhanced data access control.
In the realm of underwater robotics,optical imaging plays a pivotal role in many scientific *** to the effects of absorption and scattering,images captured in turbid water are severely ***,enhancing the quality of und...
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In the realm of underwater robotics,optical imaging plays a pivotal role in many scientific *** to the effects of absorption and scattering,images captured in turbid water are severely ***,enhancing the quality of underwater optical images stands paramount in ensuring the continued advancement and efficacy of underwater robots across its multifarious applications.
As a crucial storage and buffering apparatus for balancing the production and consumption of byproduct gases in industrial processes, accurate prediction of gas tank levels is essential for optimizing energy system sc...
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As a crucial storage and buffering apparatus for balancing the production and consumption of byproduct gases in industrial processes, accurate prediction of gas tank levels is essential for optimizing energy system scheduling. Considering that the continuous switching of the pressure and valve status(mechanism knowledge) would bring about multiple working conditions of the equipment, a multi-condition time sequential network ensembled method is proposed. In order to especially consider the time dependence of different conditions, a centralwise condition sequential network is developed, where the network branches are specially designed based on the condition switching sequences. A branch combination transfer learning strategy is developed to tackle the sample imbalance problem of different condition data. Since the condition or status data are real-time information that cannot be recognized during the prediction process, a pre-trained and ensemble learning approach is further proposed to fuse the outputs of the multi-condition networks and realize a transient-state involved prediction. The performance of the proposed method is validated on practical energy data coming from a domestic steel plant, comparing with the state-of-the-art algorithms. The results show that the proposed method can maintain a high prediction accuracy under different condition switching cases, which would provide effective guidance for the optimal scheduling of the industrial energy systems.
While reinforcement learning has shown promising abilities to solve continuous control tasks from visual inputs, it remains a challenge to learn robust representations from high-dimensional observations and generalize...
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Effective communication is crucial for promoting team cooperation in multi-agent reinforcement learning tasks. Human intention plays a key role in facilitating effective communication, driving individuals to communica...
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TiAl intermetallic could be used to replace Ni-based alloy in assemblies to generate excellent specific strength.A(Ti,Zr)-Ni-based amorphous filler metal Ti_(21.25)Zr_(25)Ni_(25)Cu_(18.75)(at.%)was designed using a cl...
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TiAl intermetallic could be used to replace Ni-based alloy in assemblies to generate excellent specific strength.A(Ti,Zr)-Ni-based amorphous filler metal Ti_(21.25)Zr_(25)Ni_(25)Cu_(18.75)(at.%)was designed using a cluster-plus-glue-atom model to successfully vacuum braze K4169 and TiAl bimetallic *** various brazing temperatures and holding time,the quantitative relationships between lattice distortion,grain boundary,dislocation density,and hardness,elastic modulus,shear strength of the joints were ***,the fracture mechanism of the joints was *** brazed seam mainly consisted of solid diffusion reaction layers(ZonesⅠandⅢ)and filler metal residue zone(ZoneⅡ).When the brazing temperature increased to 1030℃,grain refinement occurred in the brazed ***Ⅰwas primarily composed of(Ni)ss[0-11]+TiNi[011]/(Cr,Fe,Ni)ss[0-11]/(Ti,Zr)Ni[0-1-1]+(Cr,Fe,Ni)ss[0-11].The(Ti,Zr)(Ni,Cu)[001]and(Ti,Zr)(Ni,Cu)[101]intermetallic compound-based solid solutions were formed in ZoneⅡ.And the lattice distortion of(Ti,Zr)(Ni,Cu)[101]and(Ti,Zr)(Ni,Cu)[001]was 32.05%and 14.82%,*** a result,the proportion of low angle grain boundaries(LAGBs)and deformed grains in ZoneⅡrose to 38.6%and 38.7%.In ZonesⅠandⅢ,the proportion of LAGBs reduced to 8%and 3.4%,*** the holding time increased,the long-range diffusion of Al in ZoneⅡcaused the(Ti,Zr)(Ni,Cu)[001]with cubic structure to transform into(Ti,Zr)(Ni,Cu,Al)[00-1]with hexagonal crystal system structure,where the lattice distortion was 4.42%and 10.49%for a and *** 1030℃/10 min,the average geometrically nec-essary dislocation densities(GNDs)in ZonesⅠ,ⅡandⅢwere 9.87×10^(14)m^(-2),8.55×10^(14)m^(-2)and 11.4×10^(14)m^(-2),***,the shear strength of joints reached 322 MPa due to the lattice distortion,dislocation strengthening and fine grain ***,the plastic and brittle hard phases were generated in ZoneⅡand displayed a mechanical interlocking structure tha
As a complex hot problem in the financial field,stock trend forecasting uses a large amount of data and many related indicators;hence it is difficult to obtain sustainable and effective results only by relying on empi...
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As a complex hot problem in the financial field,stock trend forecasting uses a large amount of data and many related indicators;hence it is difficult to obtain sustainable and effective results only by relying on empirical *** in the field of machine learning have proved that random forest can form better judgements on this kind of problem,and it has an auxiliary role in the prediction of stock *** study uses historical trading data of four listed companies in the USA stock market,and the purpose of this study is to improve the performance of random forest model in medium-and long-term stock trend *** study applies the exponential smoothing method to process the initial data,calculates the relevant technical indicators as the characteristics to be selected,and proposes the D-RF-RS method to optimize random *** the random forest is an ensemble learning model and is closely related to decision tree,D-RF-RS method uses a decision tree to screen the importance of features,and obtains the effective strong feature set of the model as ***,the parameter combination of the model is optimized through random parameter *** experimental results show that the average accuracy of random forest is increased by 0.17 after the above process optimization,which is 0.18 higher than the average accuracy of light gradient boosting machine *** with the performance of the ROC curve and Precision–Recall curve,the stability of the model is also guaranteed,which further demonstrates the advantages of random forest in medium-and long-term trend prediction of the stock market.
With the rapid development of the economy and industry and the improvement of pollution monitoring,how to accurately predict PM2.5 has become an issue of concern to the government and *** the field of PM2.5 pollution ...
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With the rapid development of the economy and industry and the improvement of pollution monitoring,how to accurately predict PM2.5 has become an issue of concern to the government and *** the field of PM2.5 pollution forecasting,a series of results have emerged so ***,in the existing research field of PM2.5 prediction,most studies tend to predict short-term temporal *** studies tend to ignore the temporal and spatial characteristics of PM2.5 transport,which leads to its poor performance in long-term *** this paper,by optimizing previous PM2.5 deep learning prediction models,we propose a model ***,we add a spatial modular Graph Attention Network(GAT)and couple an Empirical Modal Decomposition algorithm(EMD),considering the temporal and spatial properties of ***,we use Gated Recurrent Unit(GRU)to filter spatio-temporal features for iterative rolling PM2.5 *** experimental results show that the GAT-EGRU model has more advantages in predicting PM2.5 concentrations,especially for long time *** proves that the GAT-EGRU model outperforms other models for PM2.5 *** that,we verify the effectiveness of each module by distillation *** experimental results show that each model module has an essential role in the final PM2.5 prediction *** new model improves the ability to predict PM2.5 after a long time accurately and can be used as a practical tool for predicting PM2.5 concentrations.
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