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A novel structure resembling plant stems, termed bio-inspired fractal plant stems multi-cellular circular tubes (BFPMC), was developed by incorporating fractal plant stem characteristics into smaller circular tubes within larger ones. The crashworthiness of this structure under axial impact was investigated using a validated LS-DYNA finite element model. The energy absorption performance of BFPMC tubes, varying in the number of branches, fractal orders, and inner circular diameters, was numerically studied. The numerical findings reveal a 19.27% increase in specific energy absorption (SEA) for BFPMC with $D_i=30$mm compared to $D_i=0$mm, indicating that filling a single circular tube can enhance the structure’s impact resistance. Subsequently, structural parameters conducive to excellent energy absorption characteristics were determined for various combinations of a number of branches, fractal order, and inner circle diameter parameters. These results offer valuable insights for designing multi-cellular double tubes with high energy absorption efficiency.

To enhance the crashworthiness of thin-walled structures, this study proposes a self-similar nested hierarchical hexagonal tube. This innovative design incorporates the hierarchical technique into the structural configuration of hexagonal thin-walled tubes. Numerical analysis, conducted using a validated finite element model, reveals that the proposed self-similar nested hierarchical hexagonal tube (SNHHT) significantly enhances energy absorption compared to traditional hexagonal tubes, maintaining consistent wall thickness and mass conditions. Particularly noteworthy is the improvement in energy absorption indexes under the same mass condition, with SNHHT-4 demonstrating enhancements of up to 76.45% and 86.84% in energy absorption and crushing force efficiency, respectively, while concurrently achieving a 4.11% reduction in initial peak crash force. Subsequently, a parametric study exploring wall thickness, shape factor, and various rib thicknesses was performed to investigate structural crashworthiness. Finally, employing the simplified super-folded element method, the theoretical formulation of mean crushing force was derived, and its accuracy was validated through numerical simulations.

In-network collaborative computation is essential for implementation of a large number of sensor applications. We approach the problem of computation in sensor networks from a parallel and distributed system's perspective. We define COSMOS, the Cluster-based, heterOgeneouSMOdel for Sensor networks. The model abstracts the key features of the class of cluster-based sensor applications. It assumes a hierarchical network architecture comprising of a large number of low cost sensors with limited computation capability, and fewer number of powerful clusterheads, uniformly distributed in a two dimensional terrain. The sensors are organized into single hop clusters, each managed by a distinct clusterhead. The clusterheads are organized in a mesh-like topology. All sensors in a cluster are time synchronized, whereas the clusterheads communicate asynchronously. The sensors are assumed to have multiple power states and a wakeup mechanism to facilitate power management. To illustrate algorithm design using our model, we discuss implementation of algorithms for sorting and summing in sensor networks.

In a proxy re-encryption scheme, a semi-trusted proxy converts a ciphertext for Alice into a ciphertext for Bob without seeing the underlying plaintext. A number of solutions have been proposed in public key settings. Hierarchical identity-based cryptography is a generalization of identity-based encryption that mirrors an organizational hierarchy, which allows a root private key generator to distribute the workload by delegating private key generation and identity authentication to lower-level private key generators. In this paper, we propose a hierarchical identity-based proxy re-encryption (HIBPRE) scheme which achieves IND-PrID-CCA2 security without random oracles. This is the first HIBPRE scheme up to now, and our scheme satisfies unidirectionality, non-interactivity and permits multiple re-encryptions.

The ability to obtain complex global behaviour from simple local rules makes cellular automata an interesting platform for massively parallel computation. However, manually designing a cellular automaton to perform a given computation can be extremely difficult, and automated design techniques such as genetic programming have their limitations because of the absence of human intuition. In this paper, we propose elements of a framework whose goal is to make the manual synthesis of cellular automata rules exhibiting desired global characteristics more programmer-friendly, while maintaining the simplicity of local processing elements. Although many of the framework elements that we describe here are not new, we group them into a consistent framework and show that they can all be implemented on a traditional cellular automaton, which means that they are merely more human-friendly ways of describing simple cellular automata rules, and not foreign structures that require changing the traditional cellular automaton model.

We study the origin of neutrino masses and mixing angles which can accommodate the LMA MSW solutions of the solar neutrino anomaly as well as the solution of the atmospheric neutrino problem, within the framework of the see-saw mechanism. We employ the diagonal form of the Dirac neutrino mass matrices with the physical masses as diagonal elements in the hierarchical order. Such a choice has been motivated from the fact that the known CKM angles for the quark sector, are relatively small. We consider both possibilities where the Dirac neutrino mass matrix is either the charged lepton or the up-quark mass matrix within the framework of SO(10) GUT with or without supersymmetry. The nonzero texture of the right-handed Majorana neutrino mass matrix M_R is used for the generation of the desired bimaximal mixings in a model independent way. Both hierarchical and inverted hierarchical models of the left-handed Majorana neutrino mass matrices are generated and then discussed with examples. The see-saw mass scale which is kept as a free parameter, is predicted in all the examples.

This paper describes a hierarchical controlled quantum teleportation scheme of arbitrary two-qubit based on an eight-particle maximum entanglement state among four parties, one sender and three receivers. In order to prevent the infidelity of the receivers, secret state will be purposefully split and sent to the three receivers. According to the noncloning theorem, only one of the three receivers can recover secret state and the high authorized receiver that recovers the information only requires the cooperation of one of the remaining two receivers. On the contrary, the low authorized party that recovers the secret state requires the cooperation of all remaining receivers.

This paper introduces a novel framework that automates and accelerates the development of embedded Field Programmable Gate Arrays (eFPGAs). The proposed solution is considered as the first environment for tree-based eFPGA implementation including software, hardware and loader. The developed framework allows users to generate eFPGA architecture in the form of hardware description language using Physical Design Flow (PDF) tool. It is a powerful tool that can produce a wide variety of designs ranging from small eFPGA to complex eFPGA. The bit file description of practical application is done in parallel, simultaneously and rapidly by the suggested Computer Aided Design (CAD) tools. The Loader, called Multi-Level Loader (MLL), is also provided to inject the bits into the corresponding SRAMs. Our framework is widely explored by modifying the data width. This research proves that data width equal to 17 has the best trade-off between performance, area and static power. However, it is penalized for buses having data length greater than 32. The experimentation demonstrates that a data width equal to 12 is the best for a 32-bit bus. Automation and significant acceleration of the eFPGA development cycle are also achieved in this study. A set of bench-marking applications with various multi-use purposes is mapped. The experimental results show the efficiency and flexibility of the proposed framework.

Novel three-dimensional (3D) branched nanotubes of sodium niobate (NaNbO₃) were produced by a multi-step reaction, which involves the synthesis of Nb₂O₅ branched nanowires and subsequently treating these precursors in alkali solution. XRD and SEM have been used to analyze current products. All the obtained nanobranches exhibited tubular structure, which was induced by nanoscale Kirkendall effect and surface diffusion. This work demonstrates a simple and efficient pathway to design hierarchical and complex hollow nanostructures, which are expected to have important applications, such as sensors and photocatalysts.

This paper presents a hierarchical stereo matching strategy using the Discrete Wavelet Transform. Both area- and feature-based methods are combined into a single process by the discrete wavelet decomposition. Experiments show that the method is accurate, fast, and robust to noise.

Structural hierarchies are universal design paradigms of biological materials, e.g., several materials in nature used for carrying mechanical load or impact protection such as bone, nacre, dentin show structural design at multiple length scales from the nanoscale to the macroscale. Another example is the case of diatoms, microscopic mineralized algae with intricately patterned silica-based exoskeletons, with substructure from the nanometer to micrometer length scale. Previous studies on silica nano-honeycomb structures inspired from these diatom substructures at the nanoscale have shown a great improvement in plasticity, ductility and toughness through these designs over macroscopic silica, though along with a substantial reduction in stiffness. Here, we extend the study of these structural designs to the micron length scale by introducing additional hierarchy levels to implement a multilevel composite design. To facilitate our computational experiments we first develop a mesoscale particle-spring model description of the mechanics of bulk silica/nano-honeycomb silica composites. Our mesoscale description is directly derived from constitutive material behavior found through atomistic simulations at the nanoscale with the first principles-based ReaxFF force field, but is capable of describing deformation and failure of silica materials at tens of micrometer length scales. We create several models of randomly-dispersed fiber-composite materials with a small volume fraction of the nano-honeycomb phase, and analyze the fracture mechanics using J-integral and R-curve studies. Our simulations show a dominance of quasi-brittle fracture behavior in all cases considered. For particular materials with a small volume fraction of the nano-honeycomb phase dispersed as fibers within a bulk silica matrix, we find a large improvement (≈4.4 times) in toughness over bulk silica, while retaining the high stiffness (to 70%) of the material. The increase in toughness is observed to arise primarily from crack path deflection and crack bridging by the nano-honeycomb fibers. The first structural hierarchy at the nanometer scale (nano-honeycomb silica) provides large improvements in ductility and toughness at the cost of a large reduction in stiffness. The second structural hierarchy at the micron length scale (bulk silica/nano-honeycomb composite) recovers the stiffness of bulk silica while substantially improving its toughness. The results reported here provide direct evidence that structural hierarchies present a powerful design paradigm to obtain heightened levels of stiffness and toughness from multiscale engineering a single brittle — and by itself a functionally inferior material — without the need to introduce organic (e.g., protein) phases. Our model sets the stage for the direct simulation of multiple hierarchical levels to describe deformation and failure of complex biological composites.

This paper summarizes our recent attempts to integrate action and perception within a single optimization framework. We start with a statistical formulation of Helmholtz's ideas about neural energy to furnish a model of perceptual inference and learning that can explain a remarkable range of neurobiological facts. Using constructs from statistical physics it can be shown that the problems of inferring the causes of our sensory inputs and learning regularities in the sensorium can be resolved using exactly the same principles. Furthermore, inference and learning can proceed in a biologically plausible fashion. The ensuing scheme rests on Empirical Bayes and hierarchical models of how sensory information is generated. The use of hierarchical models enables the brain to construct prior expectations in a dynamic and context-sensitive fashion. This scheme provides a principled way to understand many aspects of the brain's organization and responses. We will demonstrate the brain-like dynamics that this scheme entails by using models of birdsongs that are based on chaotic attractors with autonomous dynamics. This provides a nice example of how non-linear dynamics can be exploited by the brain to represent and predict dynamics in the environment.

Three-dimensionally hierarchical Bi₂WO₆ architectures have been produced via a facile and economical hydrothermal method without any template or surfactant. This architecture with flower-like morphology is assembled by numbers of intercrossed nanosheets. Moreover, different Bi₂WO₆ nanostructures including multilayered disks and irregular nanoplates can also be produced by simply adjusting the pH value of the precursor solution. Importantly, this kind of hierarchically structured Bi₂WO₆ architecture exhibits a much better photocatalytic activity in the photodegradation of rhodamine B than that of conventional Bi₂WO₆ multilayered disks and nanoplates. This enhanced photocatalytic performance is mainly attributed to the large specific surface areas, special structural features and high capability of absorbed oxygen species. The present work offers an effective approach for the further improvement of photocatalytic activity by designing a desirable micro/nanoarchitecture.

A hierarchical carbon material containing nanopores (micropores and mesopores) and micrometric sized capillaries (macropores) is produced using a combination of hard and soft templates. The hard template is a polypropylene (PP) cloth which decomposes during pyrolysis leaving a macroporous structure. The soft template is a cationic polyelectrolyte which stabilizes the resorcinol/formaldehyde (RF) resin porous structure during drying to give a nanoporous RF resin. The method produces a nanocomposite of the porous RF resin with an imbibed PP cloth. The composite is then pyrolyzed in a inert gas atmosphere to render a carbon material having macropores as well as micro/mesopores. The material exhibits both a large surface area (S_BET = 742 ± 2 m²/g) due to nanopores and goof fluid permeability due to micrometric sized pores. The macropores can be oriented during fabrication. The nanoporous surface can be used to support metal nanoparticles for fuel cell while the macropores allow easy flux of gas and liquids through the monolithic material.

In this paper, hierarchical meso/macroporous aluminas were obtained by using nonionic block copolymer EO₁₀₆PO₇₀EO₁₀₆(F127)/agarose hydrogel as cotemplates. The hierarchical structure was confirmed by SEM, TEM and small-angle X-ray diffraction. The results showed that Al₂O₃ exhibited a hierarchical structure with interconnected replicable macropores reproduced by agarose scaffold and ordered mesopores constructed by F127 with uniform size. The template employed here is easy to prepare, degradable and reproducible, indicating the agarose xerogel as a promising candidate for the fabrication of porous metal oxides.

High porosity $\alpha$-Fe₂O₃ has attracted a lot of attention due to its exceptional structure. In this paper, nanoflake assembled hierarchical porous flower-like $\alpha$-Fe₂O₃ was prepared by hydrothermal and calcination methods without any additional templates. Scanning electron microscopy (SEM) morphological characterization results show that with the increase of calcination temperature (400^∘C, 450^∘C, 500^∘C, 550^∘C, 600^∘C), pores appeared. However, the results of nitrogen adsorption show that the specific surface area of the $\alpha$-Fe₂O₃ reaches the maximum value (52.19m²/g) when the calcination temperature is 500^∘C. The gas sensing performance of flower-like $\alpha$-Fe₂O₃ with different calcination temperature is compared, interestingly, with the increase of calcination temperature, the response of the samples increased first and then decreased, and reached the maximum value (44.2–100 parts per million (ppm) acetone) when the calcination temperature was 500^∘C. The minimum concentration for acetone was 200 ppb (response value is 2.0). Moreover, calcined at 500^∘C, hierarchical porous $\alpha$-Fe₂O₃ has a fast response recovery (4/25 s) and low working temperature (210^∘C). These excellent gas sensing properties are mainly due to porous structure, large specific surface area, and oxygen vacancies on the surface, which make it a promising material for acetone sensors.

In this research, classification of the 18 reaching movements was done in 3D space using the elbow flexion/extension angle and the upper arm acceleration. For this, a hierarchical structure was proposed where the approximate region of the movements was determined at high level and then, motion types were recognized exactly at low level. To evaluate this hierarchical structure, the flat classifiers were implemented with the same features. The hierarchical classifier improves the formation of the decision boundaries better than the flat classifiers due to its proper structure. It improves the discrimination of the within-group members by applying an algorithm to increase the ratio of the between-class covariance matrix trace to the within-class covariance matrix trace. It also benefits from low dimensional feature space. Recognition accuracy was higher than the flat classifiers. In other evaluation metrics (Recall, Precision and F-score), this structure indicated better performance, especially in those classes with the least recognition percentage.

The concept of aggregation within fuzzy sets has been deeply modified from the initial view as mappings where a fixed number of aggregated items, and the aggregation itself, take values in the unit interval to assume a family of mapping and impose certain restrictions to assure consistency and computability, till focus on aggregation as algorithms on lists of objects. This chapter points out how new concepts such as recursivity, map reducibility or hierarchy can be translated into computable aggregations.

As the energy consumption of storage systems is grows at a staggering rate, hybrid clusters have gained increasing importance as a potential approach to tackle this challenge. By introducing low-power nodes, data-driven companies like Facebook and Baidu have reduced the energy consumption effectively in their Master/Slave based storage systems. However, the Master/Slave based systems have several typical disadvantages such as low scalabilities and single points of failure. The P2P based systems with high scalabilities utilizes file location algorithms instead of table lookup mechanisms, thus resulting in a problem of how to utilize the different storage nodes discriminatively. In this paper, a hierarchical storage strategy called vnode hierarchical remapping (VHR) is proposed based on ’a P2P distributed system called ZDFS. The strategy guarantees the high scalability and viability of ZDFS, and takes advantage of different storage nodes. Several test cases running on X86 and ARM hybrid clusters are carried out, and the test results demonstrate that the VHR works well, it achieves a good I/O performance and low data access response time while reducing the energy consumption by 44.8%.

This study introduces an optimization method for solving the cross-coupled controller design of CNC machine. Based on the particle swarm optimization algorithm, wewi propose a hierarchical particle swarm optimization (HPSO) algorithm by adopting the concept of artificial bee colony algorithm, the instant update selection strategy which provides the newest information for other particles. Herein, we use the HPSO algorithm to solve the cross-coupled controller design and optimization of CNC machine. Simulation results and discussion are introduced to show the effectiveness and performance of the proposed approach.