A set of new promising solid chalcogenides for Na-ion batteries: yield from the crystal chemical, Monte-Carlo and quantum-chemical calculations.

Free crystal space in more than 600 chalcogenide structures taken out from the ICSD has been theoretically analyzed. As a result, wide voids and channels accessible for Na+-ion migration were found in 236 structures. Among them, 165 compounds have not been described in the literature as Na+-conducting materials. These materials have been subjected to stepwise quantitative calculations. The bond valence site energy method has enabled the identification of 57 entries as the most promising ion conductors in which the Na+-ion migration energy (Em) is less than 0.55 eV for 2D or 3D diffusion. The kinetic Monte-Carlo method has been carried out for these substances; as a result, resulting in nine of the most prospective compounds with Na+-ionic conductivity Ϭ≥10-4 S cm-1 at room temperature were selected, for which the density functional theory calculations have been performed yielding six best candidates. Additionally, a logarithmic relationship was established between the values of Em and the diffusion channel radii as well as a linear relationship between Ϭ and the void radius.

Accessibility to palliative care services in Colombia: an analysis of geographic disparities.

OBJECTIVES: Due to the increase in the prevalence of non-communicable diseases and the Colombian demographic transition, the necessity of palliative care has arisen. This study used accessibility and coverage indicators to measure the geographic barriers to palliative care. METHODS: Population-based observational study focused on urban areas and adult population from Colombia, which uses three measurements of geographic accessibility to services: a) density of palliative care services per 100,000 inhabitants, b) analysis of geographic distribution by territorial nodes of the country, and c) spatial analysis of palliative care services using Voronoi diagrams. ArcGIS Pro software was used to map services' locations and identify geographic disparities. RESULTS: A total of 504 palliative care services were identified, of which 77% were primary health care services. The density of palliative care services in Colombia is 1.8 primary care services per 100,000 inhabitants and 0.4 specialized services per 100,000 inhabitants. The average palliative care coverage is 41%, two regions of the country have a coverage below 30%. Twenty-eight percent of the services provide care for a population greater than 50,000 inhabitants within their coverage area, exceeding the acceptable limit by international standards. CONCLUSIONS: Palliative care services are concentrated in three main regions (Bogotá D.C., the Center, and the Caribbean) and are limited in the Orinoquia and Amazonia nodes. Density of specialized palliative care services is extremely low and there are regions without palliative services for adults with palliative needs.

Self-Supervised Open-Set Speaker Recognition with Laguerre-Voronoi Descriptors.

Speaker recognition is a challenging problem in behavioral biometrics that has been rigorously investigated over the last decade. Although numerous supervised closed-set systems inherit the power of deep neural networks, limited studies have been made on open-set speaker recognition. This paper proposes a self-supervised open-set speaker recognition that leverages the geometric properties of speaker distribution for accurate and robust speaker verification. The proposed framework consists of a deep neural network incorporating a wider viewpoint of temporal speech features and Laguerre-Voronoi diagram-based speech feature extraction. The deep neural network is trained with a specialized clustering criterion that only requires positive pairs during training. The experiments validated that the proposed system outperformed current state-of-the-art methods in open-set speaker recognition and cluster representation.

The Characteristics of Structural Properties and Diffusion Pathway of Alkali in Sodium Trisilicate: Nanoarchitectonics and Molecular Dynamic Simulation.

Based on nanoarchitectonics and molecular dynamics simulations, we investigate the structural properties and diffusion pathway of Na atoms in sodium trisilicate over a wide temperature range. The structural and dynamics properties are analyzed through the radial distribution function (RDF), the Voronoi Si- and O-polyhedrons, the cluster function fCL(r), and the sets of fastest (SFA) and slowest atoms (SSA). The results indicate that Na atoms are not placed in Si-polyhedrons and bridging oxygen (BO) polyhedrons; instead, Na atoms are mainly placed in non-bridging oxygen (NBO) polyhedrons and free oxygen (FO) polyhedrons. Here BO, NBO, and FO represent O bonded with two, one, and no Si atoms, respectively. The simulation shows that O atoms in sodium trisilicate undergo numerous transformations: NBF0 ↔ NBF1, NBF1 ↔ NBF2, and BO0 ↔ BO1, where NBF is NBO or FO. The dynamics in sodium trisilicate are mainly distributed by the hopping and cooperative motion of Na atoms. We suppose that the diffusion pathway of Na atoms is realized via hopping Na atoms alone in BO-polyhedrons and the cooperative motion of a group of Na atoms in NBO- and FO-polyhedrons.

Modelling tomato pericarp microstructure as force control reference for harvesting robot.

BACKGROUND: The harvest of fruit can be significantly advanced with the thriving development of intelligent and automated robot technologies. Nevertheless, the picking success rate of tomato fruit still requires improvement as some fruits are unexpectedly damaged inside, which is imperceptible by machine vision. Herein, a modelling method based on modified Voronoi algorithm is proposed to reconstruct the cellular structure of tomato pericarp. RESULTS: Based on the reconstructed micro-model, the compression physical behaviour of the pericarp cells is simulated to observe internal local stress and potential damage. It is revealed that the simulation result for pericarps of tomatoes with different ripeness is highly consistent to the experimental tests, which has well validated the feasibility of this modelling and simulation method. CONCLUSION: A Voronoi-based modelling method is proposed for micro-reconstruction of tomato pericarp, and the corresponding compression simulation results agree well with the experimental tests. Such result can be utilized as reference to improve the grasping force control for harvesting robot to avoid invisible damage induced by accident overload issue. With the predicting result, superior success rate can be achieved to enhance robot performance. © 2024 Society of Chemical Industry.

[Design and Fabrication of Porous Implants Manufactured by 3D Printing].

Different porous structures were studied through finite element analysis and then optimal porous structure was selected for the orthopedic applications. The optimal Voronoi structure was designed and fabricated using 3D printing. The mechanical properties and osseointegration ability were both investigated. The mechanical tests showed that the tensile strength, compressive strength and bending strength of Voronoi structures were obviously higher than that of the human bone, and the modulus of Voronoi structures were similar to human bone. In addition, the animal experimental results exhibited that obvious bone ingrowth was found from Month 1 to Month 6. This study provides some theoretical references for the orthopedic application of porous structures.

VoroIF-GNN: Voronoi tessellation-derived protein-protein interface assessment using a graph neural network.

We present VoroIF-GNN (Voronoi InterFace Graph Neural Network), a novel method for assessing inter-subunit interfaces in a structural model of a protein-protein complex, relying solely on the input structure without any additional information. Given a multimeric protein structural model, we derive interface contacts from the Voronoi tessellation of atomic balls, construct a graph of those contacts, and predict the accuracy of every contact using an attention-based GNN. The contact-level predictions are then summarized to produce whole interface-level scores. VoroIF-GNN was blindly tested for its ability to estimate the accuracy of protein complexes during CASP15 and showed strong performance in selecting the best multimeric model out of many. The method implementation is freely available at https://kliment-olechnovic.github.io/voronota/expansion_js/.

Understanding chemistry with the symmetry-decomposed Voronoi deformation density charge analysis.

The symmetry-decomposed Voronoi deformation density (VDD) charge analysis is an insightful and robust computational tool to aid the understanding of chemical bonding throughout all fields of chemistry. This method quantifies the atomic charge flow associated with chemical-bond formation and enables decomposition of this charge flow into contributions of (1) orbital interaction types, that is, Pauli repulsive or bonding orbital interactions; (2) per irreducible representation (irrep) of any point-group symmetry of interacting closed-shell molecular fragments; and now also (3) interacting open-shell (i.e., radical) molecular fragments. The symmetry-decomposed VDD charge analysis augments the symmetry-decomposed energy decomposition analysis (EDA) so that the charge flow associated with Pauli repulsion and orbital interactions can be quantified both per atom and per irrep, for example, for σ, π, and δ electrons. This provides detailed insights into fundamental aspects of chemical bonding that are not accessible from EDA.

Diffusion sampling schemes: A generalized methodology with nongeometric criteria.

PURPOSE: The aim of this paper is to show that geometrical criteria for designing multishell q $$ q $$ -space sampling procedures do not necessarily translate into reconstruction matrices with high figures of merit commonly used in the compressed sensing theory. In addition, we show that a well-known method for visiting k-space in radial three-dimensional acquisitions, namely, the Spiral Phyllotaxis, is a competitive initialization for the optimization of our nonconvex objective function. THEORY AND METHODS: We propose the gradient design method WISH (WeIghting SHells) which uses an objective function that accounts for weighted distances between gradients within M-tuples of consecutive shells, with M $$ M $$ ranging between 1 and the maximum number of shells S $$ S $$ . All the M $$ M $$ -tuples share the same weight ω M $$ {\omega}_M $$ . The objective function is optimized for a sample of these weights, using Spiral Phyllotaxis as initialization. State-of-the-art General Electrostatic Energy Minimization (GEEM) and Spherical Codes (SC) were used for comparison. For the three methods, reconstruction matrices of the attenuation signal using MAP-MRI were tested using figures of merit borrowed from the Compressed Sensing theory (namely, Restricted Isometry Property -RIP- and Coherence); we also tested the gradient design using a geometric criterion based on Voronoi cells. RESULTS: For RIP and Coherence, WISH got better results in at least one combination of weights, whilst the criterion based on Voronoi cells showed an unrelated pattern. CONCLUSION: The versatility provided by WISH is supported by better results. Optimization in the weight parameter space is likely to provide additional improvements. For a practical design with an intermediate number of gradients, our results recommend to carry out the methodology here used to determine the appropriate gradient table.

Metabolic activity diffusion imaging (MADI): I. Metabolic, cytometric modeling and simulations.

Evidence mounts that the steady-state cellular water efflux (unidirectional) first-order rate constant (kio [s-1 ]) magnitude reflects the ongoing, cellular metabolic rate of the cytolemmal Na+ , K+ -ATPase (NKA), c MRNKA (pmol [ATP consumed by NKA]/s/cell), perhaps biology's most vital enzyme. Optimal 1 H2 O MR kio determinations require paramagnetic contrast agents (CAs) in model systems. However, results suggest that the homeostatic metabolic kio biomarker magnitude in vivo is often too large to be reached with allowable or possible CA living tissue distributions. Thus, we seek a noninvasive (CA-free) method to determine kio in vivo. Because membrane water permeability has long been considered important in tissue water diffusion, we turn to the well-known diffusion-weighted MRI (DWI) modality. To analyze the diffusion tensor magnitude, we use a parsimoniously primitive model featuring Monte Carlo simulations of water diffusion in virtual ensembles comprising water-filled and -immersed randomly sized/shaped contracted Voronoi cells. We find this requires two additional, cytometric properties: the mean cell volume (V [pL]) and the cell number density (ρ [cells/µL]), important biomarkers in their own right. We call this approach metabolic activity diffusion imaging (MADI). We simulate water molecule displacements and transverse MR signal decays covering the entirety of b-space from pure water (ρ = V = 0; kio undefined; diffusion coefficient, D0 ) to zero diffusion. The MADI model confirms that, in compartmented spaces with semipermeable boundaries, diffusion cannot be described as Gaussian: the nanoscopic D (Dn ) is diffusion time-dependent, a manifestation of the "diffusion dispersion". When the "well-mixed" (steady-state) condition is reached, diffusion becomes limited, mainly by the probabilities of (1) encountering (ρ, V), and (2) permeating (kio ) cytoplasmic membranes, and less so by Dn magnitudes. Importantly, for spaces with large area/volume (A/V; claustrophobia) ratios, this can happen in less than a millisecond. The model matches literature experimental data well, with implications for DWI interpretations.

A mechanistic computational framework to investigate the hemodynamic fingerprint of the blood oxygenation level-dependent signal.

Blood oxygenation level-dependent (BOLD) functional magnetic resonance imaging (fMRI) is one of the most used imaging techniques to map brain activity or to obtain clinical information about human cortical vasculature, in both healthy and disease conditions. Nevertheless, BOLD fMRI is an indirect measurement of brain functioning triggered by neurovascular coupling. The origin of the BOLD signal is quite complex, and the signal formation thus depends, among other factors, on the topology of the cortical vasculature and the associated hemodynamic changes. To understand the hemodynamic evolution of the BOLD signal response in humans, it is beneficial to have a computational framework available that virtually resembles the human cortical vasculature, and simulates hemodynamic changes and corresponding MRI signal changes via interactions of intrinsic biophysical and magnetic properties of the tissues. To this end, we have developed a mechanistic computational framework that simulates the hemodynamic fingerprint of the BOLD signal based on a statistically defined, three-dimensional, vascular model that approaches the human cortical vascular architecture. The microvasculature is approximated through a Voronoi tessellation method and the macrovasculature is adapted from two-photon microscopy mice data. Using this computational framework, we simulated hemodynamic changes-cerebral blood flow, cerebral blood volume, and blood oxygen saturation-induced by virtual arterial dilation. Then we computed local magnetic field disturbances generated by the vascular topology and the corresponding blood oxygen saturation changes. This mechanistic computational framework also considers the intrinsic biophysical and magnetic properties of nearby tissue, such as water diffusion and relaxation properties, resulting in a dynamic BOLD signal response. The proposed mechanistic computational framework provides an integrated biophysical model that can offer better insights regarding the spatial and temporal properties of the BOLD signal changes.

Deep-learning-based inverse design of three-dimensional architected cellular materials with the target porosity and stiffness using voxelized Voronoi lattices.

Architected cellular materials are a class of artificial materials with cellular architecture-dependent properties. Typically, designing cellular architectures paves the way to generate architected cellular materials with specific properties. However, most previous studies have primarily focused on a forward design strategy, wherein a geometry is generated using computer-aided design modeling, and its properties are investigated experimentally or via simulations. In this study, we developed an inverse design framework for a disordered architected cellular material (Voronoi lattices) using deep learning. This inverse design framework is a three-dimensional conditional generative adversarial network (3D-CGAN) trained based on supervised learning using a dataset consisting of voxelized Voronoi lattices and their corresponding relative densities and Young's moduli. A well-trained 3D-CGAN adopts variational sampling to generate multiple distinct Voronoi lattices with the target relative density and Young's modulus. Consequently, the mechanical properties of the 3D-CGAN generated Voronoi lattices are validated through uniaxial compression tests and finite element simulations. The inverse design framework demonstrates potential for use in bone implants, where scaffold implants can be automatically generated with the target relative density and Young's modulus.

VKECE-3D: Energy-Efficient Coverage Enhancement in Three-Dimensional Heterogeneous Wireless Sensor Networks Based on 3D-Voronoi and K-Means Algorithm.

During these years, the 3D node coverage of heterogeneous wireless sensor networks that are closer to the actual application environment has become a strong focus of research. However, the direct application of traditional two-dimensional planar coverage methods to three-dimensional space suffers from high application complexity, a low coverage rate, and a short life cycle. Most methods ignore the network life cycle when considering coverage. The network coverage and life cycle determine the quality of service (QoS) in heterogeneous wireless sensor networks. Thus, energy-efficient coverage enhancement is a significantly pivotal and challenging task. To solve the above task, an energy-efficient coverage enhancement method, VKECE-3D, based on 3D-Voronoi partitioning and the K-means algorithm is proposed. The quantity of active nodes is kept to a minimum while guaranteeing coverage. Firstly, based on node deployment at random, the nodes are deployed twice using a highly destructive polynomial mutation strategy to improve the uniformity of the nodes. Secondly, the optimal perceptual radius is calculated using the K-means algorithm and 3D-Voronoi partitioning to enhance the network coverage quality. Finally, a multi-hop communication and polling working mechanism are proposed to lower the nodes' energy consumption and lengthen the network's lifetime. Its simulation findings demonstrate that compared to other energy-efficient coverage enhancement solutions, VKECE-3D improves network coverage and greatly lengthens the network's lifetime.

3D Characterization of the Molecular Neighborhood of •OH Radical in High Temperature Water by MD Simulation and Voronoi Polyhedra.

Understanding the properties of the •OH radical in aqueous environments is essential for biochemistry, atmospheric chemistry, and the development of green chemistry technologies. In particular, the technological applications involve knowledge of microsolvation of the •OH radical in high temperature water. In this study, the classical molecular dynamics (MD) simulation and the technique based on the construction of Voronoi polyhedra were used to provide 3D characteristics of the molecular vicinity of the aqueous hydroxyl radical (•OHaq). The statistical distribution functions of metric and topological features of solvation shells represented by the constructed Voronoi polyhedra are reported for several thermodynamic states of water, including the pressurized high-temperature liquid and supercritical fluid. Calculations showed a decisive influence of the water density on the geometrical properties of the •OH solvation shell in the sub- and supercritical region: with the decreasing density, the span and asymmetry of the solvation shell increase. We also showed that the 1D analysis based on the oxygen-oxygen radial distribution functions (RDFs) overestimates the solvation number of •OH and insufficiently reflects the influence of transformations in the hydrogen-bonded network of water on the structure of the solvation shell.

Voronoi Tessellations and the Shannon Entropy of the Pentagonal Tilings.

We used the complete set of convex pentagons to enable filing the plane without any overlaps or gaps (including the Marjorie Rice tiles) as generators of Voronoi tessellations. Shannon entropy of the tessellations was calculated. Some of the basic mosaics are flexible and give rise to a diversity of Voronoi tessellations. The Shannon entropy of these tessellations varied in a broad range. Voronoi tessellation, emerging from the basic pentagonal tiling built from hexagons only, was revealed (the Shannon entropy of this tiling is zero). Decagons and hendecagon did not appear in the studied Voronoi diagrams. The most abundant Voronoi tessellations are built from three different kinds of polygons. The most widespread is the combination of pentagons, hexagons, and heptagons. The most abundant polygons are pentagons and hexagons. No Voronoi tiling built only of pentagons was registered. Flexible basic pentagonal mosaics give rise to a diversity of Voronoi tessellations, which are characterized by the same symmetry group. However, the coordination number of the vertices is variable. These Voronoi tessellations may be useful for the interpretation of the iso-symmetrical phase transitions.

Shannon Entropy of Ramsey Graphs with up to Six Vertices.

Shannon entropy quantifying bi-colored Ramsey complete graphs is introduced and calculated for complete graphs containing up to six vertices. Complete graphs in which vertices are connected with two types of links, labeled as α-links and ß-links, are considered. Shannon entropy is introduced according to the classical Shannon formula considering the fractions of monochromatic convex α-colored polygons with n α-sides or edges, and the fraction of monochromatic ß-colored convex polygons with m ß-sides in the given complete graph. The introduced Shannon entropy is insensitive to the exact shape of the polygons, but it is sensitive to the distribution of monochromatic polygons in a given complete graph. The introduced Shannon entropies Sα and Sß are interpreted as follows: Sα is interpreted as an average uncertainty to find the green α-polygon in the given graph; Sß is, in turn, an average uncertainty to find the red ß-polygon in the same graph. The re-shaping of the Ramsey theorem in terms of the Shannon entropy is suggested. Generalization for multi-colored complete graphs is proposed. Various measures quantifying the Shannon entropy of the entire complete bi-colored graphs are suggested. Physical interpretations of the suggested Shannon entropies are discussed.

Interrelationships between Cellular Density, Mosaic Patterning, and Dendritic Coverage of VGluT3 Amacrine Cells.

Amacrine cells of the retina are conspicuously variable in their morphologies, their population demographics, and their ensuing functions. Vesicular glutamate transporter 3 (VGluT3) amacrine cells are a recently characterized type of amacrine cell exhibiting local dendritic autonomy. The present analysis has examined three features of this VGluT3 population, including their density, local distribution, and dendritic spread, to discern the extent to which these are interrelated, using male and female mice. We first demonstrate that Bax-mediated cell death transforms the mosaic of VGluT3 cells from a random distribution into a regular mosaic. We subsequently examine the relationship between cell density and mosaic regularity across recombinant inbred strains of mice, finding that, although both traits vary across the strains, they exhibit minimal covariation. Other genetic determinants must therefore contribute independently to final cell number and to mosaic order. Using a conditional KO approach, we further demonstrate that Bax acts via the bipolar cell population, rather than cell-intrinsically, to control VGluT3 cell number. Finally, we consider the relationship between the dendritic arbors of single VGluT3 cells and the distribution of their homotypic neighbors. Dendritic field area was found to be independent of Voronoi domain area, while dendritic coverage of single cells was not conserved, simply increasing with the size of the dendritic field. Bax-KO retinas exhibited a threefold increase in dendritic coverage. Each cell, however, contributed less dendrites at each depth within the plexus, intermingling their processes with those of neighboring cells to approximate a constant volumetric density, yielding a uniformity in process coverage across the population.SIGNIFICANCE STATEMENT Different types of retinal neuron spread their processes across the surface of the retina to achieve a degree of dendritic coverage that is characteristic of each type. Many of these types achieve a constant coverage by varying their dendritic field area inversely with the local density of like-type neighbors. Here we report a population of retinal amacrine cells that do not develop dendritic arbors in relation to the spatial positioning of such homotypic neighbors; rather, this cell type modulates the extent of its dendritic branching when faced with a variable number of overlapping dendritic fields to approximate a uniformity in dendritic density across the retina.

The Use of Computational Geometry Techniques to Resolve the Issues of Coverage and Connectivity in Wireless Sensor Networks.

Wireless Sensor Networks (WSNs) enhance the ability to sense and control the physical environment in various applications. The functionality of WSNs depends on various aspects like the localization of nodes, the strategies of node deployment, and a lifetime of nodes and routing techniques, etc. Coverage is an essential part of WSNs wherein the targeted area is covered by at least one node. Computational Geometry (CG) -based techniques significantly improve the coverage and connectivity of WSNs. This paper is a step towards employing some of the popular techniques in WSNs in a productive manner. Furthermore, this paper attempts to survey the existing research conducted using Computational Geometry-based methods in WSNs. In order to address coverage and connectivity issues in WSNs, the use of the Voronoi Diagram, Delaunay Triangulation, Voronoi Tessellation, and the Convex Hull have played a prominent role. Finally, the paper concludes by discussing various research challenges and proposed solutions using Computational Geometry-based techniques.

A Distributed Algorithm for Real-Time Multi-Drone Collision-Free Trajectory Replanning.

In this paper, we present a distributed algorithm to generate collision-free trajectories for a group of quadrotors flying through a common workspace. In the setup adopted, each vehicle replans its trajectory, in a receding horizon manner, by solving a small-scale optimization problem that only involves its own individual variables. We adopt the Voronoi partitioning of space to derive local constraints that guarantee collision avoidance with all neighbors for a certain time horizon. The obtained set of collision avoidance constraints explicitly takes into account the vehicle's orientation to avoid infeasiblity issues caused by ignoring the quadrotor's rotational motion. Moreover, the resulting constraints can be expressed as Bézier curves, and thus can be evaluated efficiently, without discretization, to ensure that collision avoidance requirements are satisfied at any time instant, even for an extended planning horizon. The proposed approach is validated through extensive simulations with up to 100 drones. The results show that the proposed method has a higher success rate at finding collision-free trajectories for large groups of drones compared to other Voronoi diagram-based methods.

Autonomous Exploration of Unknown Indoor Environments for High-Quality Mapping Using Feature-Based RGB-D SLAM.

Simultaneous localization and mapping (SLAM) system-based indoor mapping using autonomous mobile robots in unknown environments is crucial for many applications, such as rescue scenarios, utility tunnel monitoring, and indoor 3D modeling. Researchers have proposed various strategies to obtain full coverage while minimizing exploration time; however, mapping quality factors have not been considered. In fact, mapping quality plays a pivotal role in 3D modeling, especially when using low-cost sensors in challenging indoor scenarios. This study proposes a novel exploration algorithm to simultaneously optimize exploration time and mapping quality using a low-cost RGB-D camera. Feature-based RGB-D SLAM is utilized due to its various advantages, such as low computational cost and dense real-time reconstruction ability. Subsequently, our novel exploration strategies consider the mapping quality factors of the RGB-D SLAM system. Exploration time optimization factors are also considered to set a new optimum goal. Furthermore, a Voronoi path planner is adopted for reliable, maximal obstacle clearance and fixed paths. According to the texture level, three exploration strategies are evaluated in three real-world environments. We achieve a significant enhancement in mapping quality and exploration time using our proposed exploration strategies compared to the baseline frontier-based exploration, particularly in a low-texture environment.