Configuration Space Research Articles

Urban green spaces enhanced human thermal comfort through dual pathways of cooling and humidifying

The severe heat stress caused by climate change and urbanization has become an urgent issue for sustainable development, and urban green spaces can effectively mitigate heat stress. However, traditional heat stress assessments based solely on temperature do not fully capture human thermal perception, which also hinders the understanding of the multiple pathways through which green spaces regulate thermal comfort, potentially underestimating their ecological benefits. This study used the universal thermal climate index (UTCI) to assess heat stress in Shanxi. Structural equation modeling was to quantify the contribution of green spaces in regulating UTCI through dual pathways of cooling and humidifying. In the summer of 2019, the average UTCI was 30.87°C, indicating moderate thermal stress, with 27.90% of the area experiencing strong thermal stress. Green spaces reduced UTCI by cooling and humidifying, with respective contributions of 78.02% and 21.98%. In more humid Cold climate zones, the green space humidifying pathway accounted for as much as 63.75%, underscoring the significant role of humidification in reducing UTCI. By quantifying the contribution of the dual pathways through which green spaces regulate UTCI and comprehensively understanding their mechanisms, this study highlighted the importance of vegetation configuration of green spaces in enhancing urban climate resilience.

A machine learning approach for dynamical modelling of Al distributions in zeolites via23Na/27Al solid-state NMR.

One of the main limitations in supporting experimental characterization of Al siting/pairing via modelling is the high computational cost of ab initio calculations. For this reason, most works rely on static or very short dynamical simulations, considering limited Al pairing/siting combinations. As a result, comparison with experiment suffers from a large degree of uncertainty. To alleviate this limitation we have developed neural network potentials (NNPs) which can dynamically sample across broad configurational and chemical spaces of sodium-form aluminosilicate zeolites, preserving the level of accuracy of the ab initio (dispersion-corrected metaGGA) training set. By exploring a wide range of Al/Na arrangements and a combination of experimentally relevant Si/Al ratios, we found that the 23Na NMR spectra of dehydrated high-silica CHA zeolite offer an opportunity to assess the distribution and pairing of Al atoms. We observed that the 23Na chemical shift is sensitive not only to the location of sodium in 6- and 8MRs, but also to the Al-Sin-Al sequence length. Furthermore, neglect of thermal and dynamical contributions was found to lead to errors of several ppm, and has a profound influence on the shape of the spectra and the dipolar coupling constants, thus necessitating the long-term dynamical simulations made feasible by NNPs. Finally, we obtained a predictive regression model for the 23Na chemical shift in CHA (Si/Al = 35, 17, 11) that circumvents the need for expensive NMR density functional calculations and can be easily extended to other zeolite frameworks. By combining NNPs and regression methods, we can expedite the simulations of NMR properties and capture the effect of dynamics on the spectra, which is often overlooked in computational studies despite its clear manifestation in experimental setups.

Event-Triggered Attitude Consensus of Multiple Rigid Body Systems With Prescribed Performance.

The event-triggered almost global attitude consensus problem is considered in this article for multiple rigid body systems with prescribed performance. Two kinds of attitude consensus protocols using axis-angle vectors are proposed at the kinematic level with different prescribed performance constraints. The first protocol aims to achieve the event-triggered attitude consensus almost globally under jointly connected graphs. Based on a prescribed performance function with local states, the configuration space of parameterized attitude representations is shown to be positively invariant which almost globally covers [Formula: see text] . The second protocol is designed to reach attitude consensus with the prescribed transient behavior guaranteed in the event-triggered setting. By defining a prescribed performance function using the metric on axis-angle spaces, a dynamic event-triggered framework is designed to ensure both the attitude geometric topology constraint and prescribed convergence performance. Finally, numerical results are given to show the validness of the two control protocols.

Variational principles of physics and the infinite Ramsey theory

Abstract Application of the Ramsey Infinite Theorem to the fundamental variational principles of physics is addressed. The Hamilton Least Action Principle states that, for a true/actual trajectory of a system, Hamilton’s Action is stationary for the paths, which evolve from the preset initial space-time point to the preset final space-time point. The Hamilton Principle distinguishes between the actual and trial/test trajectories of the system in the configurational space. This enables the transformation of the infinite set of points of the configurational space (available for the system) into the bi-colored, infinite, complete, graph, when the points of the configurational space are seen as the vertices, actual paths connecting the vertices/ points of the configurational space are colored with red; whereas, the trial links/paths are colored with green. Following the Ramsey Infinite Theorem, there exists the infinite, monochromatic sequence of the pathways/clique, which is completely made up of actual or virtual paths, linking the interim states of the system. The same procedure is applicable to the Maupertuis’s principle (classical and quantum), Hilbert-Einstein relativistic variational principle and reciprocal variational principles. Exemplifications of the Infinite Ramsey Theorem are addressed.

Strategic agronomic interventions for leaf yield and quality improvement in moringa (Moringa oleifera L.) under high density planting system

Moringa leaves are rich in minerals, vitamins, proteins, and soluble fiber, making them an ideal dietary supplement for addressing undernourishment, as well as iron and vitamin A deficiencies. The presence of high concentrations of potassium, iron, calcium, and magnesium, and natural antioxidants are particularly beneficial for pregnant mothers and young children. The current study carried out in 2021 and 2022, sought to standardize crop geometry and harvesting heights to enhance leaf biomass and quality in Moringa under High-Density Planting Systems . The research trial employed four different spacing configurations, incorporating single-row and double-row triangular planting at both wider and closer spacing, and included three harvesting heights. The experiment was arranged in a split-plot design with three replications. Observations on growth parameters, physiological traits, and yield characters were recorded at each harvest. Leaf quality parameters were analyzed in dried Moringa leaf powder. Pooled results indicated that double-row triangular planting with narrower intra-row spacing, combined with a 45 cm harvesting height, yielded substantially higher fresh and dried leaf yields of 42.76 t/ha and 7.83 t/ha, respectively. This treatment also produced superior leaf quality parameters, attributed to enhanced growth, physiological responses, and yield outcomes, leading to greater economic returns through enhanced fresh leaf yield.

Design of 3D equilibria and coils for steady-state operation of tokamaks

Abstract Inspired by the respective advantages of tokamaks and stellarators, we propose a quasi-axisymmetric (QA) tokamak-stellarator hybrid concept as a steady-state operation mode of standard tokamaks. Using stellarator optimization tools, we obtain compact 3D equilibria with good QA and sufficient (~20%) rotational transform generated by external coils to replace current drive, with the rest (~80%) generated self-consistently by the bootstrap current. The equilibria can be supported by 3D saddle coils that are compatible with typical tokamak coils and reasonably feasible from engineering perspectives. We consider and compare designs with either high-field-side or low-field-side shaping, and find that the latter is favorable for coil optimization but unfavorable for generating rotational transform and achieving good QA. These results highlight a relatively unexplored region in the space of toroidal fusion configurations.

Shotgun crystal structure prediction using machine-learned formation energies

Stable or metastable crystal structures of assembled atoms can be predicted by finding the global or local minima of the energy surface within a broad space of atomic configurations. Generally, this requires repeated first-principles energy calculations, which is often impractical for large crystalline systems. Here, we present significant progress toward solving the crystal structure prediction problem: we performed noniterative, single-shot screening using a large library of virtually created crystal structures with a machine-learning energy predictor. This shotgun method (ShotgunCSP) has two key technical components: transfer learning for accurate energy prediction of pre-relaxed crystalline states, and two generative models based on element substitution and symmetry-restricted structure generation to produce promising and diverse crystal structures. First-principles calculations were performed only to generate the training samples and to refine a few selected pre-relaxed crystal structures. The ShotunCSP method is less computationally intensive than conventional methods and exhibits exceptional prediction accuracy, reaching 93.3% in benchmark tests with 90 different crystal structures.

ResNet50-3Cur-HGCN: a novel multimodal hybrid curvature space approach to bearing fault diagnosis

Abstract Bearings are widely used in various fields due to their advantages of high load-bearing capacity, low friction loss, and minimal vibration and impact. In response to the current issues in the process of fault signal identification using graph neural networks, such as the insufficient expression of node data types, the inadequate exploration of feature information carried by the graph structure, and the singularity of mathematical space operations on graph data, this paper proposes a ResNet50-HGCN model based on the combination of Residual Network-50 (ResNet50) and Heterogeneous Graph Convolutional Neural Network (HGCN). The model is trained in a threefold mixed curvature (3 Curvature, 3Cur) space environment for semi-supervised learning, aiming to achieve accurate classification of bearing fault signals. Specifically, first, the time-domain signals and the components of the time-frequency map obtained from the Wavelet Synchrosqueezing Transform (WSST) are used as the bimodal node information for HGCN. Then, the graph network is merged in the 3Cur space for training and weighted validation, obtaining the predicted category labels of the model under the 3Cur configuration space. Finally, experimental data analysis is conducted, sparse confusion matrices for predicted categories are drawn, and four types of accuracy-related evaluation metrics are calculated. The experimental results show that the proposed ResNet50-3Cur-HGCN classification model outperforms other models in the experiment, achieving an accuracy of 97.71%, which verifies the advantage of the method in terms of accuracy and effectiveness. It also provides a good methodological reference for bearing fault diagnosis approaches centered on graph neural networks.

Variational Models with Eulerian–Lagrangian Formulation Allowing for Material Failure

We investigate the existence of minimizers of variational models featuring Eulerian–Lagrangian formulations. We consider energy functionals depending on the deformation of a body, defined on its reference configuration, and an Eulerian map defined on the unknown deformed configuration in the actual space. Our existence theory moves beyond the purely elastic setting and accounts for material failure by addressing free-discontinuity problems where both deformations and Eulerian fields are allowed to jump. To do this, we build upon the work of Henao and Mora-Corral regarding the variational modeling of cavitation and fracture in nonlinear elasticity. Two main settings are considered by modeling deformations as Sobolev and SBV-maps, respectively. The regularity of Eulerian maps is specified in each of these two settings according to the geometric and topological properties of the deformed configuration. We present some applications to specific models of liquid crystals, phase transitions, and ferromagnetic elastomers. Effectiveness and limitations of the theory are illustrated by means of explicit examples.

Non-Euclidean motion planning with graphs of geodesically convex sets

Computing optimal, collision-free trajectories for high-dimensional systems is a challenging and important problem. Sampling-based planners struggle with the dimensionality, whereas trajectory optimizers may get stuck in local minima due to inherent nonconvexities in the optimization landscape. The use of mixed-integer programming to encapsulate these nonconvexities and find globally optimal trajectories has recently shown great promise, thanks in part to tight convex relaxations and efficient approximation strategies that greatly reduce runtimes. These approaches were previously limited to Euclidean configuration spaces, precluding their use with mobile bases or continuous revolute joints. In this paper, we handle such scenarios by modeling configuration spaces as Riemannian manifolds, and we describe a reduction procedure for the zero-curvature case to a mixed-integer convex optimization problem. We further present a method for obtaining approximate solutions via piecewise-linear approximations that is applicable to manifolds of arbitrary curvature. We demonstrate our results on various robot platforms, including producing efficient collision-free trajectories for a PR2 bimanual mobile manipulator.

Hybrid Robotic Arm for Autonomous Aerial Refueling

Autonomous aerial refueling technology is gaining increasing attention to enhance aircraft combat capabilities. Current research on autonomous refueling focuses mainly on flight control laws, with little attention to the automation of refueling pipes. This leads to high demands on control law performance and navigation accuracy, making it difficult to ensure reliability. To address this, we propose a robotic arm system capable of automatic docking during the flexible aerial refueling process. The system uses a conical kinematic space configuration, offering enhanced stability and impact resistance. The frame-type structure achieves a lightweight design. Additionally, we establish a single-objective optimization model for the connecting rod dimensions and apply a genetic algorithm (GA) for their optimization. We also propose a trajectory-fitting calibration theory based on the robotic arm’s special configuration and complete its movement accuracy calibration using a laser tracker. This calibration method reduces the robotic arm’s motion error by 71%, achieving an absolute positioning accuracy better than 3.5 mm, which meets the requirements for autonomous aerial refueling. In summary, this research presents a hybrid robotic arm that meets automatic docking requirements, offering a new approach to autonomous aerial refueling.

A Robot Error Prediction and Compensation Method Using Joint Weights Optimization Within Configuration Space

With the growing demand for industrial robots in the aerospace manufacturing process, the lack of positioning accuracy has become a critical factor limiting their broad application in precision manufacturing. To enhance robot positioning accuracy, one crucial approach is to analyze the distribution patterns of robot errors and leverage spatial similarity for error prediction and compensation. However, existing methods in Cartesian space struggle to achieve accurate error estimation when the robot is loaded or the end-effector orientations are varied. To address these challenges, a novel method for robot error prediction and accuracy compensation within configuration space is proposed. The analysis of robot error distribution reveals that the spatial similarity of robot errors is more pronounced and stable in configuration space compared to Cartesian space, and this property exhibits significant anisotropy across joint dimensions. A spatial-interpolation-based unbiased estimation method with joint weights optimization is proposed for robot errors prediction, and the particle filter method is utilized to search for the optimal joint weights, enhancing the anisotropic characteristics of the prediction model. Based on the robot error prediction model, a cyclic searching method is employed to directly compensate for the joint angles. An experimental system is established using an industrial robot equipped with a 120 kg end-effector and a laser tracker. Eighty sampling points with diverse poses are randomly selected within the task workspace to measure the robot errors before and after compensation. The proposed method achieves an error prediction accuracy of 0.172 mm, reducing the robot error from the original 4.96 mm to 0.28 mm, thus meeting the stringent accuracy requirements for hole machining in robotic aerospace assembly processes.

Low-Frequency Acoustic Performance of a Microperforated Panel Integrated Coiled-Up Space Absorber With Subwavelength Thickness

Abstract Low-frequency broadband sound absorption with minimal dimensions and material cost is an ongoing research challenge in engineering acoustics. Common acoustic structures, such as microperforated panels (MPPs) and porous structures, are ineffective in alleviating low-frequency noise. In this context, a sound-absorbing panel consisting of two axially coiled-up tubes and MPP is proposed for effective low-frequency noise abatement. Initially, an electro-acoustic analogy-based analytical approach is developed to predict the acoustic absorption performance of series and parallel configurations of MPP and coiled-up tubes, and the findings are corroborated by full-field finite element simulations. The parametric analyses revealed that by carefully choosing the geometric features of the coiled-up tubes, the absorption spectra of each tube can be coupled with that of MPP. Thus the bandwidth of absorption can be broadened. Furthermore, it is observed that the parallel configuration of MPP and coiled-up tubes significantly lowered the thickness of the absorber without affecting the absorption bandwidth. Importantly, the parallel configuration of MPP and coiled-up space demonstrated more than 80% absorption in the frequency range of 250–350 Hz.

Calculation of bound and continuum states of the Ne3 van der Waals trimer

Abstract We use the configuration space Faddeev formalism to calculate bound and continuum states of the Ne3 van der Waals trimer. Continuum states below the breakup threshold describe the scattering of a neon atom off of a Ne2 diatomic molecule. We identify a resonant feature which we attribute to the presence of a three body resonance.

The topological complexity of the ordered configuration space of disks in a strip

How hard is it to program n n robots to move about a long narrow aisle such that only w w of them can fit across the width of the aisle? In this paper, we answer that question by calculating the topological complexity of conf ⁡ ( n , w ) \operatorname {conf}(n,w) , the ordered configuration space of n n open unit-diameter disks in the infinite strip of width w w . By studying its cohomology ring, we prove that, as long as n n is greater than w w , the topological complexity of conf ⁡ ( n , w ) \operatorname {conf}(n,w) is 2 n − 2 ⌈ n w ⌉ + 1 2n-2\big \lceil \frac {n}{w}\big \rceil +1 , providing a lower bound for the minimum number of cases such a program must consider.

Optimization of High-Density Planting Configurations for Poovan Banana (Musa spp.) in Coconut-Based Agroforestry Systems of the Cauvery Delta Zone

Bananas (Musa spp.) are a vital global agricultural commodity and an essential crop in tropical agricultural systems. The Poovan cultivar, known for its high productivity and adaptability, is particularly effective in intercropping within coconut-based agroforestry systems. This study investigates the impact of planting geometries on crop performance in the Cauvery Delta Zone by evaluating five spatial configurations, ranging from 2.1×2.1m to 0.9×0.9m, with a focus on morphological, physiological, and economic parameters. The results indicate that wider spacing configurations, especially 2.1×2.1m, significantly improve leaf morphological traits, including maximum leaf length (148.17 cm), breadth (77.75 cm), and leaf area index (2.61m²/plant). Additionally, these configurations enhance key fruit quality characteristics, such as increased bunch weight (16kg), improved fruit dimensions (20cm length), higher sugar content (22°Brix), and greater fruit firmness (4.5kg/cm²). The economic analysis suggests that a 1.5×1.5m spacing provides the most favorable cost-benefit ratio (1.14). This study offers valuable insights into the complex relationships between planting density, resource allocation, and productivity in tropical farming systems, providing evidence-based recommendations for optimizing both agricultural performance and economic viability in integrated farming systems.

Terahertz bound states in the continuum on-and-off-Г point of a moiré photonic superlattice.

Moiré metasurfaces exhibit high optical tuneabilities and versatile light manipulation capabilities. Both infinite quality factor (Q factor) and topological vortex configurations in momentum space (k-space) of the bound state in the continuum (BIC) have introduced new dimensions for light modulation. Herein, we propose a moiré metasurface comprising two identical square photonic lattices superimposed with a commensurate angle of 12.68°. By tuning the incidence angle, the symmetric-protected BICs, Friedrich-Wintgen BIC, and accidental BIC can be achieved simultaneously in our moiré metasurfaces. It is found that the quasi-BICs maintain an ultrahigh Q factor beyond 107. The photonic band structures manifest that the three types of BICs are at the center of far-field polarization vortices in k-space, which have their own topological charges. We experimentally show that these BICs exhibit high sensitivity to subtle changes in analyte refractive index for thin-film sensor application. Our discovery predicts an approach to a highly sensitive multi-channel terahertz biosensor.

Cup products and the higher topological complexity of configuration spaces of the circle with two anchored points

Cup products and the higher topological complexity of configuration spaces of the circle with two anchored points

Mobility and non-household environments: understanding dengue transmission patterns in urban contexts.

Households (HH) have been traditionally described as the main environments where people are at risk of dengue (and other arbovirus) infection. Mounting entomological evidence has suggested a larger role of environments other than HH in transmission. Recently, an agent-based model (ABM) estimated that over half of infections occur in non-household (NH) environments like workplaces, markets, and recreational sites. However, the importance of human and vector mobility and the configurations of urban spaces in mediating the effects of NH on dengue transmission remains understudied. To improve our knowledge of the relevance of NH in transmission, we expanded an ABM calibrated from field data in Kenya to examine movement of people and vectors under different spatial configurations of buildings. In this model, we assessed the number of people traveling between HH and NH and their distance. Those were studied on three different urban configurations, on which the NH are spatially distributed either randomly (scattered), centered (in a single center), or clustered (in more than one cluster). Across simulations, the number of people moving is a major influential variable where higher levels of movement between HH and NH increases the number of cases. In addition, the number of cases is higher when NH are scattered. Intriguingly, the distance that people travel from HH to NH seems to have little effect on dengue burden; however, it affects the level of spatial clustering of cases. These results highlight the importance of NH as a major spreader of infections between HH and NH environments supporting the relevance of NH in transmission and its interaction with human movement in driving dengue dynamics.

Tunneling dynamics of the relativistic Schrödinger/Salpeter equation

Abstract We investigate potential scattering and tunneling dynamics of a particle wavepacket evolving according to the relativistic Schrödinger equation (also known as the Salpeter equation). The tunneling properties of the Salpeter equation differ from those of the standard relativistic wave equations (such as the Klein–Gordon or Dirac equations). In particular, the tunneling solutions must be found by working in momentum space, given that the equation in configuration space contains a pseudo-differential operator. The resulting integral equations are derived and solved numerically for wavepackets scattering on model potential barriers. The solutions are characterized by the absence of Klein tunneling and an effect of the potential on the fraction of the transmitted wavepacket that propagates outside the light cone, a feature that has in the past been well-studied only for free propagation.