Deep learning-based coke dry quenching material location prediction using physical information reconstruction features

The encoding of either physical or semantic features of words was biased in an intentional learning situation. A modified recognition test was then employed to assess the effectiveness of this study manipulation and its consequences for retention. The Ss were required to select test items that were either physically similar, semantically similar, or identical to a study word. Results revealed that Ss biased toward physical encoding were more successful in selecting physically similar than semantically similar test items, while the opposite was true of Ss biased toward semantic encoding. The Ss in the two study conditions did not differ in their ability to select test items that were identical to a study word. This pattern of results was interpreted as evidence that semantic and physical information can be equally well retained over the long term. Limits on the generality of prior findings of rapid decay for physical information are discussed.

BackgroundIdentification of the structural domains of proteins is important for our understanding of the organizational principles and mechanisms of protein folding, and for insights into protein function and evolution. Algorithmic methods of dissecting protein of known structure into domains developed so far are based on an examination of multiple geometrical, physical and topological features. Successful as many of these approaches are, they employ a lot of heuristics, and it is not clear whether they illuminate any deep underlying principles of protein domain organization. Other well-performing domain dissection methods rely on comparative sequence analysis. These methods are applicable to sequences with known and unknown structure alike, and their success highlights a fundamental principle of protein modularity, but this does not directly improve our understanding of protein spatial structure.ResultsWe present a novel graph-theoretical algorithm for the identification of domains in proteins with known three-dimensional structure. We represent the protein structure as an undirected, unweighted and unlabeled graph whose nodes correspond to the secondary structure elements and edges represent physical proximity of at least one pair of alpha carbon atoms from two elements. Domains are identified as constrained partitions of the graph, corresponding to sets of vertices obtained by the maximization of the cycle distributions found in the graph. When a partition is found, the algorithm is iteratively applied to each of the resulting subgraphs. The decision to accept or reject a tentative cut position is based on a specific classifier. The algorithm is applied iteratively to each of the resulting subgraphs and terminates automatically if partitions are no longer accepted. The distribution of cycles is the only type of information on which the decision about protein dissection is based. Despite the barebone simplicity of the approach, our algorithm approaches the best heuristic algorithms in accuracy.ConclusionOur graph-theoretical algorithm uses only topological information present in the protein structure itself to find the domains and does not rely on any geometrical or physical information about protein molecule. Perhaps unexpectedly, these drastic constraints on resources, which result in a seemingly approximate description of protein structures and leave only a handful of parameters available for analysis, do not lead to any significant deterioration of algorithm accuracy. It appears that protein structures can be rigorously treated as topological rather than geometrical objects and that the majority of information about protein domains can be inferred from the coarse-grained measure of pairwise proximity between elements of secondary structure elements.

Impact of Geometric Forms on the Effectiveness and Physical Features of POD-based Geometric Parameterization

We consider a recently introduced framework for the description of memory effects based on quantum state distinguishability quantifiers, in which entropic quantifiers can be included. After briefly presenting the approach, we validate it considering the performance of different quantifiers in the characterization of the reduced dynamics of a two-level system undergoing decoherence. We investigate the different behaviors of these quantifiers in the dependence on physical features of the model, such as environmental temperature and coupling strength. It appears that the performance of the different quantifiers conveys the same physical information, though with different sensitivities, thus supporting robustness of the approach.

Existing research in metasurface design was based on trial-and-error high-intensity iterations and requires deep acoustic expertise from the researcher, which severely hampered the development of the metasurface field. Using deep learning enabled the fast and accurate design of hypersurfaces. Based on this, in this paper, an integrated learning approach was first utilized to construct a model of the forward mapping relationship between the hypersurface physical structure parameters and the acoustic field, which was intended to be used for data enhancement. Then a dual-feature fusion model (DFCNN) based on a convolutional neural network was proposed, in which the first feature was the high-dimensional nonlinear features extracted using a data-driven approach, and the second feature was the physical feature information of the acoustic field mined using the model. A convolutional neural network was used for feature fusion. A genetic algorithm was used for network parameter optimization. Finally, generalization ability verification was performed to prove the validity of the network model. The results showed that 90% of the integrated learning models had an error of less than 3 dB between the real and predicted sound field data, and 93% of the DFCNN models could achieve an error of less than 5 dB in the local sound field intensity.

Wheelset bearing fault diagnosis becomes challenging under time-varying train speeds, where traditional diagnostic methods often fail to remain effective. To address this issue, a multi-source physical feature graph attention network based on vibration–rotational speed data fusion is proposed to identify wheelset bearing failures under time-varying speed conditions. The method fuses three types of information to enhance rotational speed adaptability. First, a two-channel convolutional neural network is constructed to extract features from vibration signals and key-phase signals. Next, a self-attention mechanism combined with global average pooling is introduced to fuse the two feature sources. To further obtain effective information, a recursive graph auto-encoder exploits the recursiveness of vibration to generate inter-sample relationship graphs, enhancing feature interpretability. Finally, a graph attention network is used to fuse the extracted features with the recurrence graph. Experiments demonstrate that the proposed method achieves higher accuracy and rotational speed adaptability under time-varying speed conditions.