Complex Topology Research Articles

Length-squared L-function for identifying clustering pattern of network-constrained flows

ABSTRACTThe network-constrained flow is defined as the movement between two locations along road networks, such as the residence-workplace flow of city dwellers. Among flow patterns, clustering (i.e. the origins and destinations are aggregated simultaneously) is one of the cities’ most common and vital patterns, which assists in uncovering fundamental mobility trends. The existing methods for detecting the clustering pattern of network-constrained flows do not consider the impact of road network topology complexity on detection results. They may underestimate the flow clustering between networks of simple topology (roads with simpler shapes and fewer links, e.g. straight roads) but with high network intensity (i.e. flow number per network flow space), and determining the actual scale of an observed pattern remains challenging. This study develops a novel method, the length-squared L-function, to identify clustering patterns of network-constrained flows. We first use the L-function and its derivative to examine the clustering scales. Further, we calculate the local L-function to ascertain the locations of the clustering patterns. In synthetic and practical experiments, our method can identify flow clustering patterns of high intensities and reveal the residents’ main travel behavior trends with taxi OD flows, providing more reasonable suggestions for urban planning.

Reinforcement Learning Based RSS-Threshold Optimization for D2D-Aided HTC/MTC in Dense NOMA Systems

To fulfill the stringent requirements brought by human-type communication (HTC) along with massive machine-type communication (MTC), device-to-device (D2D) and non-orthogonal multiple access (NOMA) techniques will inevitably be incorporated into dense cellular networks to cater massive connectivity and maintain high spectral efficiency. However, such combination may lead to very complex network topologies and bring challenge in resource allocation, interference management and transmission mode selection. Note the received signal strength (RSS) is an important factor for cellular and D2D mode selection, it can affect multi-access mode determination in D2D-aided HTC/MTC dense NOMA systems. Therefore, the RSS threshold of each cell has great impact on system performance and should be carefully tuned. To this end, we formulate the RSS-threshold selection problem as a decentralized partially observable Markov decision process to maximize the performance for downlink and uplink communications. Accordingly, we employ a multi-agent reinforcement learning based scheme wherein each small base station acts as an agent and chooses the optimal RSS threshold to achieve maximum sum rate by interacting with the environment continuously. Extensive simulation results reveal our proposed scheme can improve the system sum rate and coverage by enhancing the connectivity of massive HTC and MTC devices via D2D and NOMA techniques.

Intermittency Near the Boundary of Phase Synchronization in Systems with a Complex Topology of the Attractor

Intermittency Near the Boundary of Phase Synchronization in Systems with a Complex Topology of the Attractor

Analysis of intermittency near the boundary of the phase synchronization in systems with complex topology of attractor

Intermittent behavior near the boundary of phase synchronization in systems with a complex topology of attractor is studied. Using the calculation of statistical characteristics of the laminar phase lengths it is shown that in the case of a relatively small detuning of the control parameters of interacting systems near the phase synchronization boundary the eyelet intermittency takes place.

Experiment investigation of the tip vortex cavitation around a pitching hydrofoil

Experimental research was carried out to investigate the characteristics of tip vortex cavitation (TVC) caused by tip vortex in an oscillating hydrofoil. Shanghai Shipping and Research Institute's looped cavitation tunnel served as the site of the experiments. The hydrofoil used as the test was NACA (National Advisory Committee for Aeronautics) 63820. Two high-speed cameras, one torque sensor, and six hydrophones were employed to capture the cavitation patterns, the torque exerted on the hydrofoil, and the cavitation noise, respectively. The cavitation flow under static angle of attack (AoA) and the dynamic AoA have all been investigated in order to have a better knowledge of the differences between the TVC surrounding these two conditions. The AoA of 8.5°, 10°, and 14.5° and the AoA range of 1.8°–16° were selected for the static condition research and the dynamic condition study, respectively. A tip vortex flow was created between the tip of the stationary hydrofoil and the ceiling of the tunnel test section. To investigate the impact of the cavitation number on the growth of TVC surrounding the pitching hydrofoil, various pressure environments were built up. As for the experiment environment, cavitation numbers 2.47, 2.00, and 1.66 were used. The results indicate: (1) under static conditions, decreasing cavitation and increasing AoA lead to increased stability and topological complexity of TVC. (2) Comparing and analyzing cavitation patterns during the ascending, static, and descending processes reveal that the strongest cavitation intensity during the descending process, followed by static conditions, with the least intensity during the ascending process. (3) The frequency properties under pitching motion include those of each static AoA condition, although the acoustic characteristic of higher AoA has a stronger influence on that of dynamic condition.

Enhancing the performance of composite phase change materials using novel triply periodic minimal surface structures

Organic phase change materials (PCMs) are widely used in thermal management applications owing to their high latent heats of fusion and low cost. Unfortunately, their poor thermal conductivities severely hamper the thermal transport process. Hence, PCMs are often hybridized with high thermal conductivity materials that promote phase change for time-sensitive applications and potentially increase the power density of thermal energy storage (TES) devices. Optimized thermally conductive networks with high-latent-heat PCM matrices are critical in the thermal management of high heat flux applications. With advancements in additive manufacturing, it has become easier to create complex topologies, such as triply periodic minimal surfaces (TPMS), that can further augment the performance of composite PCMs. TPMS surfaces have shown great promise in improving thermal transport performance but investigations into their thermal properties remain limited. In this study, the thermal performances of PCM infused TPMS structures of wall thicknesses varying from 0.4 mm to 0.8 mm were systematically investigated. The TPMS structures were designed using level-set equations, and each system of structures was fabricated by selective laser melting, a metal additive manufacturing technique. Numerical investigations were performed to understand the phase change dynamics of PCM in TPMS cells and experiments were performed to determine their thermal properties and phase change performance for thermal energy storage applications. Our simulations revealed that for structures with uniform wall thickness of 0.4 mm, the novel Neovius structure resulted in the highest effective thermal conductivity enhancement. For a given thickness, it is thus hypothesized that more complex TPMS topologies have higher surface areas and volumes which enhance their thermal conductivities. However, the complex shape of the Neovius structure could not be further uniformly thickened without self-intersecting geometries. Ultimately, a balance of both the surface area and thickness of the TPMS cell determines the thermal conductivity enhancement. Overall, the 0.6 mm thick IWP cells had the highest thermal conductivity of 13.84 W/m K, which was consistently verified in experiments. This is likely due to the fact that it has a larger surface area than the P structure but is also thicker than the 0.4 mm Neovius structure. To further demonstrate the potential application of our PCM-based TPMS design, low temperature thermal cycling tests were performed. Our results showed the IWP cell structure promoted a more uniform temperature distribution as compared to the P cell. Phase change in the 0.6 mm thick IWP cell structure was completed 1.4 times faster than the 0.8 mm thick P cell and 1.8 times faster than the 0.6 mm thick P cell. The purpose of this study is to provide useful guidelines for the selection of TPMS structures for various applications such as battery packs and spacecraft and to open doors for the co-optimization of materials and device geometry for enhanced energy and power density.

Legal aspects of functional security standardisation of the Internet of Things

IoT technologies provide smart things with the ability to make decisions in the management of physical objects using intelligence and consensus. To support the Internet of Things, technologies such as built-in devices, cloud and fog computing, big data processing, machine learning, and artificial intelligence are used to produce intelligent physical objects. A review of existing security infrastructures for IoT-based intelligent environments shows that every connected device can be a potential entry point for an attack.
 An overview of the key aspects of security standards for smart environments based on the Internet of Things has been provided in the following areas: potential solutions, intelligent environments, limits of security assessment, open issues and challenges. Additional research on the development of methodological and technological standardisation measures in the field of interoperability of heterogeneous IoT devices is an urgent task in order to start further discussions on the development of new security standards and certification infrastructure for smart environments based on the IoT.
 Based on the analysis of the existing problems of implementing the Internet of Things, the methodological and technological features of legal regulation of intellectual environments have been studied. The structures of standardisation of networks and services of the IoT environments at the regional, European and global international levels have been considered.
 The architecture of the Internet of Things environments has been defined as a multi-level, heterogeneous system with a complex topology and the use of innovative technologies. The single phenomenon of IoT security has been identified as a complex concept that includes functional security and information security with their interconnection, contradictions, challenges and risks.
 The functional security of the Internet of Things has been studied in terms of the security function, security completeness and resilience, which are subject to regulation in the technical requirements for the product being designed. An aspect model of IoT interoperability has been presented and examples of its application in terms of interrelated components (transport, syntactic, semantic, behavioural, and policy aspects) have been given.
 An assessment of generally accepted practices and risks of creating regulatory documents (standards, instructions, methodological materials) in the field of functional security of the Internet of Things has been carried out. Recommendations for the introduction of a scientifically based approach to national standardisation of IoT security and measures to address the problem of interoperability of heterogeneous IoT devices have been provided.

Twin delayed deep deterministic policy gradient based energy management strategy for fuel cell/battery/ultracapacitor hybrid electric vehicles considering predicted terrain information

For fuel cell/battery/ultracapacitor hybrid electric vehicles (FCHEV), the complex topology and terrain information pose challenges to fuel cell efficiency, power sources lifespan, and fuel economy. In this paper, a hierarchical energy management strategy (EMS) considering predicted terrain information is constructed to optimize the demand power allocation of FCHEV based on twin delayed deep deterministic policy gradient (TD3). Firstly, to improve the fuel economy of FCHEV, an adaptive fuzzy filter-based upper-level frequency separation is designed considering negative power. Secondly, to achieve the minimum fuel consumption, a TD3-based lower-level EMS is designed, where the terrain information is predicted through multi long short-term memory network and considered in the TD3 framework. The optimal state of charge obtained by dynamic programming (DP) based on the demand power and predicted terrain information, is considered in the reward function of TD3, as well as the optimal experience solved by DP based on historical road data is integrated into the designed hybrid experience replay. Finally, the effectiveness and optimality of the TD3-based EMS considering predicted terrain information are verified by a series of comprehensive simulations under different driving cycles. The simulation results under CYC_SC03 and test driving cycles indicate that compared to traditional TD3-based EMS, the fuel economy is increased by 33.3% and 27.5%, respectively, and can reach the benchmark level of 80% and 80.9% of DP, respectively.

Self-Assembly via Condensation of Imine or Its N-Substituted Derivatives.

ConspectusCompared to traditionally used irreversible chemical reactions, dynamic covalent chemistry (DCC) including imine formation represents a more advanced technique in the preparation of molecules with complex structures and topologies, whose syntheses require the formation of many bonds. By allowing the occurrence of error checking and self-correcting, it is likely that the target molecules with high enough thermodynamic stability could be self-assembled in high or even quantitative yield. Two questions are raised herein. First, it becomes a central problem in self-assembly that how to endow a target product with high enough thermodynamic stability so that it can be produced as the major or the only product within the self-assembly library. Second, the reversible nature of dynamic bonds jeopardizes the intrinsic stability of the products. More specifically, the imine bond which represents the mostly used dynamic covalent bond, is apt to undergo hydrolysis in the presence of water. Developing new approaches to make imine more robust and compatible with water is thus of importance. In this account, we summarized the progress made in our group in the field of self-assembly based on C═N bond formation. In organic solvent where an imine bond is relatively robust, we focus on studying how to enhance the thermodynamic stability of a target molecule by introducing intramolecular forces. These noncovalent interactions either release enthalpy to favor the formation of the target molecule or preorganize the building blocks into specific conformations that mimic the product, so that the entropy loss of the formation of the latter is thus suppressed. In water, which often leads to imine hydrolysis, we developed two strategies to enhance the water-compatibility. By taking advantage of multivalency, namely, multiple bonds are often more robust than a single bond, self-assembly via condensation of imine was performed successfully in water, a solvent that is considered as forbidden zone of imine. Another approach is to replace typical imine with its more robust and water compatible derivatives, namely, either hydrazone or oxime, whose C═N bonds are generally less electrophilic compared to typical imine. With the water-compatible dynamic bonds in hand, a variety topological nontrivial molecules such as catenanes and knots was self-assembled successfully in aqueous media, driven by hydrophobic effect. When the self-assembled molecules in the form of rings and cages were designed for supramolecular purposes, water-compatibility endows a merit that allows the hosts to take advantage of hydrophobic effect to drive host-guest recognition, enabling various tasks to be accomplished, such as separation of guest isomers with similar physical properties, recognition of highly hydrated anions, as well as stabilization of guest dimers.

Complex quantum network models from spin clusters

In the emerging quantum internet, complex network topology could lead to efficient quantum communication and robustness against failures. However, there are concerns about complexity in quantum communication networks, such as potentially limited end-to-end transmission capacity. These challenges call for model systems in which the impact of complex topology on quantum communication protocols can be explored. Here, we present a theoretical model for complex quantum communication networks on a lattice of spins, wherein entangled spin clusters in interacting quantum spin systems serve as communication links between appropriately selected regions of spins. Specifically, we show that ground state Greenberger-Horne-Zeilinger clusters of the two-dimensional random transverse-field Ising model can be used as communication links between regions of spins. Further, the resulting quantum networks can have complexity comparable to that of the classical internet. Our work provides a generative model for further studies towards determining the network characteristics of the emerging quantum internet.

Higher topological complexity of a map

Higher topological complexity of a map

Topological complexity of $S^3/Q_8$ as fibrewise L-S category

In 2010, M. Sakai and the first author showed that the topological complexity of a space $X$ coincides with the fibrewise unpointed L-S category of a pointed fibrewise space $\proj_{1} \colon X \times X \to X$ with the diagonal map $\Delta \colon X \to X \times X$ as its section. In this paper, we describe our algorithm how to determine the fibrewise L-S category or the Topological Complexity of a topological spherical space form. Especially, for $S^3/Q_8$ where $Q_8$ is the quaternion group, we write a python code to realise the algorithm to determine its Topological Complexity.

Analytical model of mechanical properties for a hierarchical lattice structure based on hierarchical body-centered cubic unit cell

Hierarchical structure features complex geometric topology and excellent mechanical properties. In this work, analytical models are developed to predict the mechanical properties as Young's modulus, Poisson's ratio, and yield stress of a lattice structure composed of hierarchical body-centered cubic. Analytical results were validated by finite element simulations. The compressed experiments of lattice samples fabricated by powder bed fusion were conducted to examine mechanical properties. The analytical model agrees well with the simulation results and experimental results. The largest discrepancy from the analytical model to the finite element simulations for Young's modulus of lattice structures is 7.5%. Regarding Poisson's ratio and yield stress, the largest discrepancy are 0.05% and 13.1%, respectively. Furthermore, the Young's modulus shows an increasing trend as the relative density of lattices increases and geometrical ratio λ decreases. Moreover, the influence of shear deformation on the mechanical properties of the lattice structures decreases with the gradual increase of geometrical ratio λ. The Young's modulus and yield stress for the lattices increase as geometrical ratio λ decreases with increasing relative density. The Poisson's ratio shows an opposite trend to the Young's modulus as well as the yield stress. Finally, the degree of anisotropy of Young's modulus for the lattice structure is investigated. The range of anisotropy values for the lattice structure is large, i.e., 2.98∼745. The anisotropy value of Young's modulus decreases with the relative density increasing. And, the anisotropy value of lattices decreases with increasing relative density, i.e., the monotonic trend. This work provides guidance for future designs of the hierarchical lattice structures from the aspects of theoretical prediction.

Evolutionary neural architecture search combining multi-branch ConvNet and improved transformer

Deep convolutional neural networks (CNNs) have achieved promising performance in the field of deep learning, but the manual design turns out to be very difficult due to the increasingly complex topologies of CNNs. Recently, neural architecture search (NAS) methods have been proposed to automatically design network architectures, which are superior to handcrafted counterparts. Unfortunately, most current NAS methods suffer from either highly computational complexity of generated architectures or limitations in the flexibility of architecture design. To address above issues, this article proposes an evolutionary neural architecture search (ENAS) method based on improved Transformer and multi-branch ConvNet. The multi-branch block enriches the feature space and enhances the representational capacity of a network by combining paths with different complexities. Since convolution is inherently a local operation, a simple yet powerful “batch-free normalization Transformer Block” (BFNTBlock) is proposed to leverage both local information and long-range feature dependencies. In particular, the design of batch-free normalization (BFN) and batch normalization (BN) mixed in the BFNTBlock blocks the accumulation of estimation shift ascribe to the stack of BN, which has favorable effects for performance improvement. The proposed method achieves remarkable accuracies, 97.24 % and 80.06 % on CIFAR10 and CIFAR100, respectively, with high computational efficiency, i.e. only 1.46 and 1.53 GPU days. To validate the universality of our method in application scenarios, the proposed algorithm is verified on two real-world applications, including the GTSRB and NEU-CLS dataset, and achieves a better performance than common methods.

An efficient displacement-based isogeometric formulation for geometrically exact viscoelastic beams

We propose a novel approach to the linear viscoelastic problem of shear-deformable geometrically exact beams. The generalized Maxwell model for one-dimensional solids is here efficiently extended to the case of arbitrarily curved beams undergoing finite displacement and rotations. High efficiency is achieved by combining a series of distinguishing features, that are: (i) the formulation is displacement-based, therefore no additional unknowns, other than incremental displacements and rotations, are needed for the internal variables associated with the rate-dependent material; (ii) the governing equations are discretized in space using the isogeometric collocation method, meaning that elements integration is totally bypassed; (iii) finite rotations are updated using the incremental rotation vector, leading to two main benefits: minimum number of rotation unknowns (the three components of the incremental rotation vector) and no singularity problems; (iv) the same SO(3)-consistent linearization of the governing equations and update procedures as for non-rate-dependent linear elastic material can be used; (v) a standard second-order accurate time integration scheme is made consistent with the underlying geometric structure of the kinematic problem. Moreover, taking full advantage of the isogeometric analysis features, the formulation permits accurately representing beams and beam structures with highly complex initial shape and topology, paving the way for a large number of potential applications in the field of architectured materials, meta-materials, morphing/programmable objects, topological optimizations, etc. Numerical applications are finally presented in order to demonstrate attributes and potentialities of the proposed formulation.

Landscape complexity beyond invariance and the elastic manifold

AbstractThis paper characterizes the annealed, topological complexity (both of total critical points and of local minima) of the elastic manifold. This classical model of a disordered elastic system captures point configurations with self‐interactions in a random medium. We establish the simple versus glassy phase diagram in the model parameters, with these phases separated by a physical boundary known as the Larkin mass, confirming formulas of Fyodorov and Le Doussal. One essential, dynamical, step of the proof also applies to a general signal‐to‐noise model of soft spins in an anisotropic well, for which we prove a negative‐second‐moment threshold distinguishing positive from zero complexity. A universal near‐critical behavior appears within this phase portrait, namely quadratic near‐critical vanishing of the complexity of total critical points, and cubic near‐critical vanishing of the complexity of local minima. These two models serve as a paradigm of complexity calculations for Gaussian landscapes exhibiting few distributional symmetries, that is, beyond the invariant setting. The two main inputs for the proof are determinant asymptotics for non‐invariant random matrices from our companion paper (Ben Arous, Bourgade, McKenna 2022), and the atypical convexity and integrability of the limiting variational problems.

UV laser formation of complex topologies for sensitive elements of navigation sensors based on surface acoustic waves

A method utilizing a UV laser to form complex electrode structures on the surface of the sensitive element prototype for a solid-state gyroscope (SSG) based on surface acoustic waves (SAW) was tested. The prospects of using the method are demonstrated in terms of the flexibility of changing the topology, the speed of navigation sensors prototypes manufacturing, and reducing the cost. Methods for forming surface structures on piezoelectric materials using an IR and UV laser are compared. The parameters of laser formation of a surface pattern for configuring the topology of a sensitive element by evaporation of a thin-film coating 5000 nm and 200 nm thick deposited on lithium niobate substrates are determined. The amplitude-frequency characteristics of the manufactured samples of SAW-based SSG sensing elements are obtained. Additionally a finite-element modeling was performed to obtain theoretical prediction of dependencies of the sensing element characteristics on the thickness of the metallization forming the electrodes. It was found that when using a fiber UV laser with a wavelength of 355 nm, it is necessary to impose more stringent requirements on the quality of metallization and on the calculation of laser radiation regimes for processing coatings with a thickness of 200 nm. It is shown that when using a UV laser, it is possible to manufacture more compact sensitive elements of navigation sensors on surface acoustic waves than when using IR lasers.

Combining Surface-Induced Dissociation and Charge Detection Mass Spectrometry to Reveal the Native Topology of Heterogeneous Protein Complexes.

Charge detection mass spectrometry (CDMS) enables the direct mass measurement of heterogeneous samples on the megadalton scale, as the charge state for a single ion is determined simultaneously with the mass-to-charge ratio (m/z). Surface-induced dissociation (SID) is an effective activation method to dissociate non-intertwined, non-covalent protein complexes without extensive gas-phase restructuring, producing various subcomplexes reflective of the native protein topology. Here, we demonstrate that using CDMS after SID on an Orbitrap platform offers subunit connectivity, topology, proteoform information, and relative interfacial strengths of the intact macromolecular assemblies. SID dissects the capsids (∼3.7 MDa) of adeno-associated viruses (AAVs) into trimer-containing fragments (3mer, 6mer, 9mer, 15mer, etc.) that can be detected by the individual ion mass spectrometry (I2MS) approach on Orbitrap instruments. SID coupled to CDMS provides unique structural insights into heterogeneous assemblies that are not readily obtained by traditional MS measurements.

Contour attention network for cerebrovascular segmentation from TOF-MRA volumetric images.

Cerebrovascular segmentation is a crucial step in the computer-assisted diagnosis of cerebrovascular pathologies. However, accurate extraction of cerebral vessels from time-of-flight magnetic resonance angiography (TOF-MRA) data is still challenging due to the complex topology and slender shape. The existing deep learning-based approaches pay more attention to the skeleton and ignore the contour, which limits the segmentation performance of the cerebrovascular structure. We aim to weight the contour of brain vessels in shallow features when concatenating with deep features. It helps to obtain more accurate cerebrovascular details and narrows the semantic gap between multilevelfeatures. This work proposes a novel framework for priority extraction of contours in cerebrovascular structures. We first design a neighborhood-based algorithm to generate the ground truth of the cerebrovascular contour from original annotations, which can introduce useful shape information for the segmentation network. Moreover, We propose an encoder-dual decoder-based contour attention network (CA-Net), which consists of the dilated asymmetry convolution block (DACB) and the Contour Attention Module (CAM). The ancillary decoder uses the DACB to obtain cerebrovascular contour features under the supervision of contour annotations. The CAM transforms these features into a spatial attention map to increase the weight of the contour voxels in main decoder to better restored the vessel contourdetails. The CA-Net is thoroughly validated using two publicly available datasets, and the experimental results demonstrate that our network outperforms the competitors for cerebrovascular segmentation. We achieved the average dice similarity coefficient ( ) of 68.15 and 99.92% in natural and synthetic datasets. Our method segments cerebrovascular structures with bettercompleteness. We propose a new framework containing contour annotation generation and cerebrovascular segmentation network that better captures the tiny vessels and improve vessel connectivity.

Marine ecosystem models of realistic complexity rarely exhibits significant endogenous non-stationary dynamics

Despite the observation of cyclic and chaotic dynamics in nature, it is still not clear whether this behaviour is inherent to ecological systems or caused by external forcings. Here we explored a set of approximately 210,000 simulations to assess how often a model of realistic complexity exhibits non-stationary dynamics when external perturbations are excluded. Remarkably, less than one third of the population shown non-stationary dynamics and, even when present, fluctuations were rather small. The inherent stability of plankton communities showed to be related to the presence of multiple feedbacks in the food web structure, omnivory, low centre of gravity, and supports the conclusion that food webs of realistic complexity rarely exhibit significant endogenous non-stationary dynamics. Finally, we computed Lyapunov exponents for the non-stationary trajectories, in order to assess in which proportion they were periodic or chaotic, and we concluded that less than 10% of the non-stationary trajectories (3% of the total) showed sensitivities to initial conditions. This further supports the conclusion that complex topology mainly damps endogenous fluctuations in the food web.