Topological Type Research Articles

The coupled effect of aspect ratio and strut micro-deformation mode on the mechanical properties of lattice structures

Lattice structures have been integrated into various industrial applications owing to their unique compressive properties. Mechanical characterisation is usually done by testing a small specimen which is assumed representative of the utilised lattice. A specimen's aspect ratio (height to diameter/width ratio) is known to affect compressive properties in various engineering materials, yet its influence in lattices has not been investigated thoroughly. In this study, titanium lattice specimens designed with different aspect ratios (ranging from 0.5 to 3.0) and four different topologies (displaying bend and stretch-dominated micro-deformation modes) were fabricated using powder bed fusion and tested in quasi-static compression. Their compressive properties and failure modes were evaluated using acquired stress-strain curves and digital image correlation (DIC) analysis. Reducing the aspect ratio in the bend-dominated lattices increased the measured stiffness of the specimens by up to 40%. Conversely, increasing the aspect ratio of the stretch dominated lattices increased the measured stiffness of the specimens by up to 30%. For both topology types, decreasing the aspect ratio increased the measured strength of the specimens, but the effect was less than that observed for stiffness. Different responses were attributed to gradient strain accumulation and different failure patterns (densification versus shear banding) that were observed depending on the combination of aspect ratio and topology. These findings are particularly important for better predicting the mechanical behaviour of lattice-based components that have aspect ratios outside the range of conventional test specimens.

Design and Mechanical Properties of Maximum Bulk Modulus Microstructures Based on a Smooth Topology with Grid Point Density

The aim of topology optimisation is to determine the optimal distribution of material phases within the periodic cells of a microstructure. In this paper, the density of grid points under element volume fraction is constructed to replace the finite elements in the traditional SIMP framework, avoiding jagged and blurry boundaries in the computational process due to grid dependence. This is then combined with homogenisation theory, a microstructure topology optimisation algorithm with maximum bulk modulus under prescribed volume constraints is proposed, which can obtain 2D and 3D topologies with smooth boundaries. In addition, a closed form expression for the two-dimensional topological concave edge structure (taking the most typical topology as an example) was derived, and a compression experiment was conducted on the topological microstructure based on 3D metal printing technology. Scanning electron microscopy showed that the powder bonded on the surface of the printed structure was not completely melted and the step effect caused the finite element analysis results to be higher than the experimental results. Overall, the finite element simulation and experimental results of the concave surface structure have good consistency, with high strength and energy absorption effects. Topologies based on grid point density obtain microstructures with smooth boundaries, and the introduction of the Heaviside smoothing function and multiple filtering steps within this algorithm leads to more robust optimisation, facilitating 3D or 4D printing of microstructures that meet specific design requirements and confirming the feasibility of the proposed topology for lightweighting studies.

Load balancing and topology dynamic adjustment strategy for power information system network: a deep reinforcement learning-based approach

As power information systems play an increasingly critical role in modern society, higher requirements are placed on the performance and reliability of their network infrastructure. In order to cope with the growing data traffic and network attack threats in the power information system, we select the power information system data center network as the research object and design an overall system solution based on software defined network, including the application layer, control layer and infrastructure layer. A typical fat tree network topology is simulated and analyzed. We define the load balancing and network topology dynamic adjustment problem as a Markov decision process, and design a data flow path acquisition method based on breadth-first search to construct the action space of each host. Then, a deep reinforcement learning algorithm based on deep Q-network, priority experience replay and target network is introduced to provide solutions for optimizing the performance of power information systems and responding to network attacks. Simulation results show that the proposed method is better than the traditional equal-cost multi-path algorithm in terms of average bandwidth utilization, average jitter and average packet loss, and can reduce the probability of network nodes being attacked by more than 11%.

Comparison of Different Innovative Technical Solutions for Voltage Regulation in Distribution Grids

Due to the increasing amount of Dispersed Generation voltage stability is becoming a more and more critical issue especially in rural distribution grids where most of the generation capacity is installed. Characteristic for these types of grid topologies, which were not designed for a huge share of energy feed-in, are the long lines and the dispersion of consumers. Due to the expansion targets of the German Government regarding renewable energies, it is taken for granted that the voltage problems already appearing in the rural areas will be worsen. In numerous research projects different kinds of technical solutions for improving the voltage deviation have been tested in practice during the last years but often without performing an analytical study of network characteristics. Moreover in most of the projects the impact of only one innovative method was tested but research about the interaction of different technologies is seldom. Thus this paper tries to analyse the benefits of different kinds of innovative equipment and the reciprocal effect of these technologies in four real medium voltage networks in the north western part of Germany. Glance at the economic costs of their implementing is also provided.

An Adaptive Protection Scheme based on Fault Location for Smart Micro-Grids

The traditional distribution systems are radial and have unidirectional power flow. The overcurrent protection system of these networks is negatively affected by Distributed Generation (DG) units. The time and current coordination of protection devices of these systems is the most important concern in the case of high DGs penetration. Actually, DGs connection and disconnection change the short circuit level of feeders and disturb the coordination of protection devices. So, new protection schemes should be proposed which must not to be affected by DGs and should work properly in both islanded and grid connected operation modes of the micro-grid. In this paper, an adaptive protection scheme, based on fault location, is proposed for smart micro-grids. The functionality of each protection device is adaptively selected based on detected network topology and fault type. The proposed solution is simulated in the SimPower software. The results show the efficiency of the proposed protection scheme which cannot be affected by DGs.

Abnormal developmental of structural covariance networks in young adults with heavy cannabis use: a 3-year follow-up study

Heavy cannabis use (HCU) exerts adverse effects on the brain. Structural covariance networks (SCNs) that illustrate coordinated regional maturation patterns are extensively employed to examine abnormalities in brain structure. Nevertheless, the unexplored aspect remains the developmental alterations of SCNs in young adults with HCU for three years, from the baseline (BL) to the 3-year follow-up (FU). These changes demonstrate dynamic development and hold potential as biomarkers. A total of 20 young adults with HCU and 22 matched controls were recruited. All participants underwent magnetic resonance imaging (MRI) scans at both the BL and FU and were evaluated using clinical measures. Both groups used cortical thickness (CT) and cortical surface area (CSA) to construct structural covariance matrices. Subsequently, global and nodal network measures of SCNs were computed based on these matrices. Regarding global network measures, the BL assessment revealed significant deviations in small-worldness and local efficiency of CT and CSA in young adults with HCU compared to controls. However, no significant differences between the two groups were observed at the FU evaluation. Young adults with HCU displayed changes in nodal network measures across various brain regions during the transition from BL to FU. These alterations included abnormal nodal degree, nodal efficiency, and nodal betweenness in widespread areas such as the entorhinal cortex, superior frontal gyrus, and parahippocampal cortex. These findings suggest that the topography of CT and CSA plays a role in the typical structural covariance topology of the brain. Furthermore, these results indicate the effect of HCU on the developmental changes of SCNs in young adults.

Tightly focused optical skyrmions and merons formed by electric-field vectors with prescribed characteristics

Abstract Optical skyrmions, which are topological quasi-particles with nontrivial electromagnetic textures, have garnered escalating research interest recently for their potential in diverse applications. In this paper, we present a method for generating tightly focused optical skyrmion and meron topologies formed by electric-field vectors under 4π-focusing system, where both the topology types (including Néel-, Bloch-, intermediate- and anti-skyrmion/meron) and the normal direction of the two-dimensional topology projection plane can be tailored at will. By utilizing time-reversal techniques, we analytically derive the radiation pattern of a multiple concentric-ring array of dipoles (MCAD) to obtain the required illumination fields on the pupil planes of the two high numerical aperture lenses. The Deby vector diffraction integral theory is employed to calculate the corresponding tightly focused field, and their topology characteristics are quantitatively evaluated by the electric-field vector distribution. The results demonstrate that arbitrary electric-field based skyrmion and meron can be conveniently generated by adjusting the oscillation direction of each dipole in the MCAD and the normal direction of the dipole array. The generated optical topologies with fully controllable degrees of freedom provide potential applications in optical information processing, transmission, and storage.

A Finite Topological Type Theorem for Open Manifolds with Non-negative Ricci Curvature and Almost Maximal Local Rewinding Volume

Abstract In this paper, we present finite topological type theorems for open manifolds with non-negative Ricci curvature, under almost maximal local rewinding volume. Unlike previous related research, our theorems remove the constraints of sectional curvature or conjugate radius, which were crucial additional assumptions on metric regularity in prior results. Notably, our settings do not necessarily satisfy a triangle comparison of Toponogov type. In fact, the method we adopt also extends to many previous related studies.

The influence of synaptic strength and noise on the robustness of central pattern generator

<abstract><p>In this paper, we explore the mechanisms of central pattern generators (CPGs), circuits that can generate rhythmic patterns of motor activity without external input. We study the half-center oscillator, a simple form of CPG circuit consisting of neurons connected by reciprocally inhibitory synapses. We examine the role of asymmetric coupling factors in shaping rhythm activity and how different network topologies contribute to network efficiency. We have discovered that neurons with lower synaptic strength are more susceptible to noise that affects rhythm changes. Our research highlights the importance of asymmetric coupling factors, noise, and other synaptic parameters in shaping the broad regimes of CPG rhythm. Finally, we compare three topology types' regular regimes and provide insights on how to locate the rhythm activity.</p></abstract>

Degrees of maps and multiscale geometry

Abstract We study the degree of an L-Lipschitz map between Riemannian manifolds, proving new upper bounds and constructing new examples. For instance, if $X_k$ is the connected sum of k copies of $\mathbb CP^2$ for $k \ge 4$ , then we prove that the maximum degree of an L-Lipschitz self-map of $X_k$ is between $C_1 L^4 (\log L)^{-4}$ and $C_2 L^4 (\log L)^{-1/2}$ . More generally, we divide simply connected manifolds into three topological types with three different behaviors. Each type is defined by purely topological criteria. For scalable simply connected n-manifolds, the maximal degree is $\sim L^n$ . For formal but nonscalable simply connected n-manifolds, the maximal degree grows roughly like $L^n (\log L)^{-\theta (1)}$ . And for nonformal simply connected n-manifolds, the maximal degree is bounded by $L^\alpha $ for some $\alpha < n$ .

Unsupervised Machine Learning-Based Clustering of High-Frequency Radio Channel Properties: Analysis of Sector Communication Efficiency

Machine learning has emerged as a powerful tool for both engineering and geo-localization applications. In this study, we investigate the Terabit/sec bandwidth wireless technology application using specialized ns-3 simulation tools. Through extensive simulations, we explore various scenarios with diverse parameters, including population density, topology types, and overlapping ratios among consecutive radio sectors centered around a single access point. To extract meaningful insights from the data, we employ the DBScan unsupervised learning method, enabling us to identify the optimal number of classes for sector efficiency features. Our optimization approach considers both the number of outliers and the minimum number of elements within each radio sector. By analysing a synthetic dataset generated from the simulation cases, we uncover valuable insights and establish the optimal working point for the system.

Distinguishing Legendrian knots in topological types 74, 948, 10136 with maximal Thurston–Bennequin number

In a recent work of Dynnikov and Prasolov [I. Dynnikov and M. Prasolov, An algorithm for comparing Legendrian knots, preprint (2023), arXiv:2309.05087] a new method of comparing Legendrian knots with nontrivial symmetry group is proposed. Using this method, we confirm conjectures of [W. Chongchitmate and L. Ng, An atlas of Legendrian knots, Exp. Math. 22(1) (2013) 26–37, arXiv:1010.3997] about Legendrian knots in topological types [Formula: see text], [Formula: see text] and [Formula: see text]. This completes the classification of Legendrian types of rectangular diagrams of knots of complexity up to 9.

Metal-organic framework-based adsorbents for blood purification: progress, challenges, and prospects.

Blood purification, such as hemodialysis (HD), plasma exchange (PE), and hemoperfusion (HP), is widely applied in patients with organ failure (such as kidney and liver failure). Among them, HP mainly relies on porous adsorbents to efficiently adsorb accumulated metabolic wastes and toxins, thus improving purification efficiency. Metal-organic frameworks (MOFs), with a high porosity, large surface area, high loading capacity, and tailorable topology, are emerging as some of the most promising materials for HP. Compared with non-metal framework counterparts, the self-built metal centers of MOFs feature the intrinsic advantages of coordination with toxin molecules. However, research on MOFs in blood purification is insufficient, particularly in contrast to materials applied in other biomedical applications. Thus, to broaden this area, this review first discusses the essential characteristics, potential mechanisms, and structure-function relationship between MOFs and toxin adsorption based on porosity, topology, ligand functionalization, metal centers, and toxin types. Moreover, the stability, utilization safety, and hemocompatibility of MOFs are illustrated for adsorbent selection. The current development and progress in MOF composites for HD, HP, and extracorporeal membrane oxygenation (ECMO) are also summarized to highlight their practicability. Finally, we propose future opportunities and challenges from materials design and manufacture to the computational prediction of MOFs in blood purification. It is anticipated that our review will expand the interest of researchers for more impact in this area.

A Comprehensive Comparison of Double-Stator Superconducting Brushless Machines Using Two Typical Field-Excitation Stator Topologies

A Comprehensive Comparison of Double-Stator Superconducting Brushless Machines Using Two Typical Field-Excitation Stator Topologies

Цифровой радиоприёмник на основе нейронной сети

One of the areas of digital signal processing used in radio receivers and currently being investigated is the use of artificial neural networks. Signal reception is one of the most difficult theoretical and engineering problems in message transmission. The difficulty lies in the fact that messages need to be extracted from modulated signals, which are exposed to various distorting factors and interference in the radio channel. Therefore, it is desirable to have methods of reception that would be the best (optimal) in the given conditions. There are many different neural network topologies. Single–layer and multi-layer direct propagation are known – perceptrons, recurrent networks, self-organizing networks, as well as hybrid networks (radial-basis, hierarchical classifiers). Each of these types of topologies has its own advantages and disadvantages.The article analyzes current research and development in this area. The implementation of a radio receiver (demodulator) based on a multilayer perceptron is shown and the neural network is trained. Using National Instruments PXI equipment, a study was carried out, which showed that in comparison with an optimal receiver, the probability of a bit error in a receiver based on a neural network is higher, but insignificant. The operation of the receiver under the influence of harmonic interference showed that as the power of the interference increases, the probability of a BER error increases, and the closer the interference is to the carrier frequency, the higher the BER also becomes. Nevertheless, the neural network-based digital receiver under study remains operational at significant levels of interference that are close enough to the carrier frequency of the desired signal.

Gravitational solitons and complete Ricci flat Riemannian manifolds of infinite topological type

Gravitational solitons and complete Ricci flat Riemannian manifolds of infinite topological type

Bending Behavior of 3D Printed Polymeric Sandwich Structures with Various Types of Core Topologies

In this study, bending performance and energy absorption capabilities of sandwich structures with different types of core topologies. Specifically, four types of core geome-tries including cylindrical, hexagonal, square, and triangular were investigated. Sandwich structures were fabricated using Fused Deposition Modelling (FDM) 3D printing method using polylactic acid (PLA) and carbon fiber reinforced polylactic acid (CF-PLA). The ma-terial properties of PLA and CF-PLA were determined via tensile test. Three-point bending tests were performed to achieve the energy absorption performance of sandwich struc-tures. The findings of the bending test show that the core topology has a substantial im-pact on sandwich constructions' capacity to absorb energy. Additionally, it has been ob-served that the use of different materials affects the energy absorption capacity of sand-wich structures.

COVID-19 in Pakistan: A national analysis of five pandemic waves.

The COVID-19 pandemic showed distinct waves where cases ebbed and flowed. While each country had slight, nuanced differences, lessons from each wave with country-specific details provides important lessons for prevention, understanding medical outcomes and the role of vaccines. This paper compares key characteristics from the five different COVID-19 waves in Pakistan. Data was sourced from daily national situation reports (Sitreps) prepared by the National Emergency Operations Centre (NEOC) in Islamabad. We use specific criteria to define COVID-19 waves. The start of each COVID-19 wave is marked by the day of the lowest number of daily cases preceding a sustained increase, while the end is the day with the lowest number of cases following a 7-days decline, which should be lower than the 7 days following it. Key variables such as COVID-19 tests, cases, and deaths with their rates of change to the peak and then to the trough are used to draw descriptive comparisons. Additionally, a linear regression model estimates daily new COVID-19 deaths in Pakistan. Pakistan saw five distinct waves, each of which displayed the typical topology of a complete infectious disease epidemic. The time from wave-start to peak became progressively shorter, and from wave-peak to trough, progressively longer. Each wave appears to also be getting shorter, except for wave 4, which lasted longer than wave 3. A one percent increase in vaccinations decreased deaths by 0.38% (95% CI: -0.67, -0.08) in wave 5 and the association is statistically significant. Each wave displayed distinct characteristics that must be interpreted in the context of the level of response and the variant driving the epidemic. Key indicators suggest that COVID-19 preventive measures kept pace with the disease. Waves 1 and 2 were mainly about prevention and learning how to clinically manage patients. Vaccination started late during wave 3 and its impact on hospitalizations and deaths became visible in wave 5. The impact of highly virulent strains Alpha/B.1.1.7 and Delta/B.1.617.2 variants during wave 3 and milder but more infectious Omicron/B.1.1.529 during wave 5 are apparent.

A luminescent Zn(II) metal−organic framework assembled with a thiazolothiazole chromophore for sensing mainly cobalt(II) and nitrofuran antibiotics in aqueous solutions

The accumulation of cobalt(II) and antibiotics in the environmental water system has posed a significant threat to both human health and the safety of the ecological environment. Therefore, it is of utmost importance to look for a proficient means to detect cobalt(II) and antibiotics. Herein, we introduce a luminescent Zn-MOF, constructed with a thiazolo[5,4-d]thiazole ligand that features a chromophore group, which serves as a sensor to detect cobalt(II) and antibiotics. The Crystal structure of {[Zn2(OBA)2(4bpyttz)]·2DMF} (1) (4bpyttz=2,5-bis(pyridine-4-yl)thiazolo[5,4-d]thiazole, H2OBA=4,4′-oxybis(benzoic acid)) displayed a 6-c net three-dimensional (3D) framework structure with novel topological type denoted as {44·610·8}. Moreover, luminescence sensing studies demonstrated that 1 exhibits high selectivity and multi-responsiveness of multiple ions mainly for Co2+, and nitrofuran antibiotics mainly for NZF, and NFT in an aqueous medium inducing turning-off effects. The detection limits are determined to be 1.14, 0.14, and 0.072 μM for 1, respectively. Additionally, MOF-derived fluorescence test papers and composite films were utilized for the visual detection of Co2+, NZF, and NFT, thereby expanding the potential applications of the system in real-world sensing. Furthermore, the potential luminescence quenching mechanism of MOF 1 regarding Co2+ ions and nitrofuran antibiotics could be deduced through various characterization methods.

Crystal Chemistry and Structural Complexity of the Uranyl Molybdate Minerals and Synthetic Compounds

This paper reviews not the largest, but at the same time quite an interesting, group of natural and synthetic uranyl molybdate compounds. Nowadays, nine minerals of U and Mo are known, but the crystal structures have only been reported for five of them. Almost an order of magnitude more (69) synthetic compounds are known. A significant discrepancy in the topological types for natural and synthetic phases is shown, which is most likely due to elevated temperatures of laboratory experiments (up to 1000 °C), while natural phases apparently grow at significantly lower temperatures. At the same time, the prevalence of dense topologies (with edge-sharing interpolyhedral linkage) among natural phases can be noted, which is fully consistent with other recently considered mineral groups. Uranyl molybdates demonstrate several similarities with compounds of other U-bearing groups; however, even topological matches do not lead to the appearance of completely isotypic compounds. Structural complexity calculations confirm, in general, crystal chemical observations. Considering the prevalence of dense structures in which coordination polyhedra of uranium and molybdenum are connected through common edges as well as framework architectures, one can expect a less significant influence of interlayer species on the formation of the crystal structure than the main U-bearing complexes. The more structural complexity of the uranyl molybdate units, the more complex of the entire crystal structure is. In addition, there is a tendency for complexity to increase with increasing density of the complex; the simplest structures are vertex-shared, while the complexity increases with the appearance of common edges.