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The confinement of waves within a waveguide can enable directional transmission of signals, which has found wide applications in communication, imaging, and signal isolation. Extending this concept to static systems, where material deformation is piled up along a spatial trajectory, remains elusive due to the sensitivity of localized deformation to structural defects and impurities. Here, we propose a general framework to characterize localized static deformation responses in two-dimensional generic static mechanical metamaterials, by exploiting the duality between space in static systems and time in one-dimensional non-reciprocal wave systems. An internal time-reverse symmetry is developed by the space-time duality. Upon breaking this symmetry, quasi-static load-induced deformation can be guided to travel along a designated path, thereby realizing a stress guide. A combination of time-reverse and inversion symmetries discloses the parity-time symmetry inherent in static systems, which can be leveraged to achieve directional deformation shielding. The tailorable stress guides can find applications in various scenarios, ranging from stress shielding and energy harvesting in structural tasks to information processing in mechanical computing devices. [ABSTRACT FROM AUTHOR]

Mechanical computing provides an information processing method to realize sensing‐analyzing‐actuation integrated mechanical intelligence and, when combined with neural networks, can be more efficient for data‐rich cognitive tasks. The requirement of solving implicit and usually nonlinear equilibrium equations of motion in training mechanical neural networks makes computation challenging and costly. Here, an explicit mechanical neuron is developed of which the response can be directly determined without the need of solving equilibrium equations. A training method is proposed to ensure the robustness of the neuron, i.e., insensitivity to defects and perturbations. The explicitness and robustness of the neurons facilitate the assembly of various network structures. Two exemplified networks, a robust mechanical convolutional neural network and a mechanical recurrent neural network with long short‐term memory capabilities for associative learning, are experimentally demonstrated. The introduction of the explicit and robust mechanical neuron streamlines the design of mechanical neural networks fulfilling robotic matter with a level of intelligence. [ABSTRACT FROM AUTHOR]

In this paper, we present a non-symmetry and anti-packing object pattern representation model (NAM) for object detection. A set of distinctive sub-patterns (object parts) is constructed from a set of sample images of the object class; object pattern are then represented using sub-patterns, together with spatial relations observed among the sub-patterns. Many feature descriptors can be used to describe these sub-patterns. he NAM model codes the global geometry of object category, and the local feature descriptor of sub-patterns deal with the local variation of object. By using Edge Direction Histogram (EDH) features to describe local sub-pattern contour shape within an image, we found that richer shape information is helpful in improving recognition performance. Based on this representation, several learning classifiers are used to detect instances of the object class in a new image. The experimental results on a variety of categories demonstrate that our approach provides successful detection of the object within the image.

DNA methylation is a critical mechanism for epigenetic gene regulation. In mammalian cells, CpG methylation density often influences the transcription level of related genes. Here, we report a short and accurate method to determine the CpG methylation density of any DNA region by sequentially applying bisulfite PCR and SssI (CpG) methylase. We introduced simple and efficient binding and washing steps that greatly improve the previous methodology and considerably enhance the accuracy of the assay. The accuracy of this method allows the detection of differences at the level of a single CpG methylation site when homogeneous PCR product was used as substrate. We validated our method by bisulfite sequencing multiple clones of samples with variable levels of CpG methylation. We envision that for its accuracy, simplicity, low-cost, flexibility, minimum instrumentation requirements, and high-throughput potential our method could greatly benefit research on DNA methylation.

An exact elasticity study of the free vibration of a simply-supported cross-ply cylindrical shell containing a piezoelectric layer is presented. The solution of the derived governing differential equations is obtained through the power series expansion method. Both the direct and inverse piezoelectric effects are investigated. Results presented here provide an understand of the behavior of piezoelectric materials when acted as smart materials and can be used to assess various numerical results obtained from approximate shell theories.