Predicting the Emergence of SARS-CoV-2 Clades

Evolution is a process of change where mutations in the viral RNA are selected based on their fitness for replication and survival. Given that current phylogenetic analysis of SARS-CoV-2 identifies new viral clades after they exhibit evolutionary selections, one wonders whether we can identify the viral selection and predict the emergence of new viral clades? Inspired by the Kolmogorov complexity concept, we propose a generative complexity (algorithmic) framework capable to analyze the viral RNA sequences by mapping the multiscale nucleotide dependencies onto a state machine, where states represent subsequences of nucleotides and state-transition probabilities encode the higher order interactions between these states. We apply computational learning and classification techniques to identify the active state-transitions and use those as features in clade classifiers to decipher the transient mutations (still evolving within a clade) and stable mutations (typical to a clade). As opposed to current analysis tools that rely on the edit distance between sequences and require sequence alignment, our method is computationally local, does not require sequence alignment and is robust to random errors (substitution, insertions and deletions). Relying on the GISAID viral sequence database, we demonstrate that our method can predict clade emergence, potentially aiding with the design of medications and vaccines

Neuro-Inspired Hierarchical Multimodal Learning

Integrating and processing information from various sources or modalities are critical for obtaining a comprehensive and accurate perception of the real world. Drawing inspiration from neuroscience, we develop the Information-Theoretic Hierarchical Perception (ITHP) model, which utilizes the concept of information bottleneck. Distinct from most traditional fusion models that aim to incorporate all modalities as input, our model designates the prime modality as input, while the remaining modalities act as detectors in the information pathway. Our proposed perception model focuses on constructing an effective and compact information flow by achieving a balance between the minimization of mutual information between the latent state and the input modal state, and the maximization of mutual information between the latent states and the remaining modal states. This approach leads to compact latent state representations that retain relevant information while minimizing redundancy, thereby substantially enhancing the performance of downstream tasks. Experimental evaluations on both the MUStARD and CMU-MOSI datasets demonstrate that our model consistently distills crucial information in multimodal learning scenarios, outperforming state-of-the-art benchmarks

Simulation of Soil Cutting and Power Consumption Optimization of a Typical Rotary Tillage Soil Blade

The rotary tillage knife roller, as one of the typical soil-touching parts of the tillage equipment cutting process, is in direct contact with the soil. During the cutting process, there are problems related to structural bending, deformation, and high power consumption, caused by impact and load, and it is difficult to observe the micro-change law of the rotary tillage tool and soil. In view of the above problems, we took the soil of the cotton experimental field in Shihezi, Xinjiang, and the soil-contacting parts of the rotary tillage equipment, specifically the rotary tiller roller, as the research subject. Using the finite-element method (FEM) to simulate the structure of the rotary tiller with different bending angle parameters, we obtained its average stress and deformation position information, and obtained a range linear relationship between the bending angle and the structural performance of the rotary tiller tool. Using discrete element method (DEM)-based simulation to build the corresponding contact model, soil particle model, and soil–rotary tillage knife roll interaction model to simulate the dynamic process of a rotary tillage knife roll cutting soil, we obtained the change rules of the soil deformation area, cutting process energy, cutting resistance, and soil particle movement. By using the orthogonal simulation test and the response surface method, we optimized the kinematic parameters of the rotary tiller roller and the key design parameters of a single rotary tiller. Taking the reduction of cutting power consumption as the optimization goal and considering the influence of the bending angle on its structural performance, the optimal parameter combination was obtained as follows: the forward speed was 900 m/h, the rotation speed was 100 rad/min, the bending angle was 115°, and the minimum power consumption of the cutter roller was 0.181 kW. The corresponding average stress and deformation were 0.983 mm and 41.826 MPa, which were 15.8%, 13%, and 7.9% lower than the simulation results of power consumption, stress, and deformation under the initial parameter setting, respectively. Finally, the effectiveness of the simulation optimization model in reducing power consumption and the accuracy of the soil-cutting simulation were verified by a rotary tilling inter-field test, which provided theoretical reference and technical support for the design and optimization of other typical soil-touching parts of tillage and related equipment, such as disc harrow, ploughshare, and sub-soiling shovel

Spanning Network Gels from Nanoparticles and Graph Theoretical Analysis of Their Structure and Properties

Gels self-assembled from colloidal nanoparticles (NPs) translate the size-dependent properties of nanostructures to materials with macroscale volumes. Large spanning networks of NP chains provide high interconnectivity within the material necessary for a wide range of properties from conductivity to viscoelasticity. However, a great challenge for nanoscale engineering of such gels lies in being able to accurately and quantitatively describe their complex non-crystalline structure that combines order and disorder. The quantitative relationships between the mesoscale structural and material properties of nanostructured gels are currently unknown. Here, it is shown that lead telluride NPs spontaneously self-assemble into a spanning network hydrogel. By applying graph theory (GT), a method for quantifying the complex structure of the NP gels is established using a topological descriptor of average nodal connectivity that is found to correlate with the gel’s mechanical and charge transport properties. GT descriptions make possible the design of non-crystalline porous materials from a variety of nanoscale components for photonics, catalysis, adsorption, and thermoelectrics.Hydrogels of lead telluride nanoparticles are structurally characterized using graph theory. The morphology of the gels is quantified, identifying the effect of various salt concentrations and compositions on the connectivity of the network structures. Significant influence by divalent cations on gel structure is observed. Relationships between the structural descriptors and viscoelastic and charge transport properties are evaluated.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/172992/1/adma202201313_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/172992/2/adma202201313-sup-0001-SuppMat.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/172992/3/adma202201313.pd