Configuration Space Research Articles

Improving agricultural spraying with multi-rotor drones: a technical study on operational parameter optimization

Drones play a key role in enhancing nutrient management efficiency under climate change scenarios by enabling precise and adaptable spray applications. Current aerial spray application research is primarily focused on examining the influence of drone spraying parameters viz., flight height, travel speed, rotor configuration, droplet size, payload, spray pressure, spray discharge and wind velocity on spray droplet deposition characteristics. The present study aimed to study and optimize the effect of spray height, operating pressure, nozzle spacing and spray nozzle mounting configuration on spray discharge rate, spray width, spray distribution pattern, spray uniformity and spray liquid loss. A spray patternator of 5.0 m x 5.0 m was developed per Bureau of Indian Standards (BIS) standard to study the spray volume distribution pattern of boom and hex nozzle configuration. Initially, drone spray operational parameters viz., spray discharge rate (Lm−1), operating pressure (kg cm−2) and spray angle (°) were measured using digital nozzle tester, digital pressure gauge and digital protractor, respectively, in the laboratory. Then optimized the nozzle spacing for boom configuration attachment to drone sprayer and recorded best spray uniformity at 0.6 m nozzle spacing. The drone sprayer hovered at three different heights, viz., 1.0, 2.0 and 3.0 m from the top of the patternator and spray operating pressure was maintained at 4.0 kg cm−2 in outdoor condition. Single pass distribution pattern and one-direction application distribution pattern method used for optimizing height of spray, operating pressure and nozzle mounting confirmation from the results of discharge rate, spray angle, effective spray width, spray liquid loss and spray distribution uniformity. Results showed that, the better spray uniformity distribution was found when the drone sprayer hover height was increased from the top of the patternator (2.0 m). More round spray droplet vertex pattern was generated during the 1.0 m hover height compared to the 2.0 and 3.0 m hover heights due to the direct impact of downwash airflow generated by the rotors. Finally it was concluded that, the good spray volume distribution was found at 2.0 m height of spray with standard hexa nozzle configuration arrangement as compared to the boom spray nozzle arrangement.

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.

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.

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

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.

A robotic skill transfer learning framework of dynamic manipulation for fabric placement

Placing fabric poses a challenge to robots since fabric with high dimensional configuration space can deform during manipulation. Existing methods for placing fabric mostly rely on static operations, which are inefficient and require a large workspace. Therefore, this study applies dynamic manipulation (manipulating uncontrollable parts of the fabric by swinging) to fabric placement, proposing a novel learning framework for robotic dynamic fabric placement skill learning and generalization. The proposed framework integrates reinforcement learning with imitation learning, leveraging expert demonstration data to guide and accelerate skill acquisition. Additionally, fabric characteristics are combined with imitation learning to enable the transfer and generalization of the learned policy to real-world environments The experiments suggest that the proposed framework is capable of achieving the placement tasks for a range of positions and fabrics. For success rate, the policy of the proposed framework ultimately achieves a flatness of exceeding 95% and a placement distance error of less than 2 mm. Moreover, the proposed approach is similar in operation time to the fastest method, while it can reduce the space required for manipulating the fabric by over 15%. Compared with other placement policies, it is promising because of its high accuracy, flexibility, efficiency, as well as adaptability.

Energy efficient weed control in high density planting rainfed cotton

An experiment utilizing a split-plot design was done to ascertain the optimal plant population and weed management strategies for enhancing yield, net income, and energy efficiency in high-density rainfed cotton cultivation. The primary plot treatments comprised three spacing configurations: 100x10 cm, 150x50x10 cm, and 175x50x10 cm. Six weed management approaches were evaluated as subplot treatments, with each treatment replicated three times throughout the 2023-24 rabi season. These practices included power weeding on 20 and 45 days after sowing (DAS), intercropping with black gram, hand weeding on 20 and 45 DAS, herbicide application of Metolachlor (pre-emergence) on 5 DAS, and a combination of Pyrithiobac sodium plus Quizalofop ethyl (post-emergence) on 20 DAS, along with weedy check and weed-free check. The 100 x 10 cm spacing combined with power weeding proved superior, achieving higher yield parameters, greater yield, increased income (Rs. 67238/ha), a benefit-cost ratio of 2.27, energy productivity of 0.38 Kg/MJ, net energy productivity of 30334 MJ/ha, and an energy use efficiency of 4.0 in high-density planting. The narrow-row spacing (100x10 cm) with power weeding also optimized plant population and enhanced per-plant productivity. This configuration also supported proper soil tilling and aeration, enabling a higher uptake of inputs with minimum energy expenditure for weeding, leading to improved overall performance.

Sustainable Approach to Achieve New Green Solutions for the Construction Industry

In recent years, noise levels in administrative buildings have been considered one of the main causes of the stress and lack of productivity of employees. Based on the responses collected from a questionnaire survey distributed among companies with an open space configuration and studying the office’s acoustic conditions, from the specialized literature and through the authors’ experience, this study focused on the development of a new green dividing panel with superior sound absorption and acoustic insulation performances. In the experimental part, it was noticed that the presence of plants can influence the acoustic absorption values through their leaves’ conformation and distribution. Additionally, it was observed that the introduction of a coconut fiber layer in the panel led to higher values of the sound absorption coefficients in most of the studied plants. Through the conducted measurements, Tradescantia pallida registered superior values, with sound absorption coefficients with constantly increased values that varied in the range of 0.72–0.98 for the frequency range of 250–3150 Hz. Also, the weighted sound reduction index recorded a superior value of Rw (C; Ctr) = 27 (−1; −4) dB, comparable to other existing solutions.

Multi-Objective Safety-Enhanced Path Planning for the Anterior Part of a Flexible Ureteroscope in Robot-Assisted Surgery.

In robot-assisted flexible ureteroscopy, planning a safety-enhanced path facilitates ureteroscope reaching the target safely and quickly. However, current methods rarely consider the safety impact caused by body motion of the anterior part, such as impingement on the lumen wall and sweeping motion risk, or the method can only be used in collision-free situations. The kinematic model of the anterior part under C-shaped and S-shaped collision bending is first analysed. Considering the newly defined impingement cost and sweeping area, the differential evolution algorithm is adopted to optimise the path in the configuration space. Experiments were performed to verify the effectiveness of the method. Compared with the competing algorithm, the proposed algorithm reduced impingement cost and sweeping area by an average of 31.0% and 8.64%. Force measurement experiments verified the rationality of the impingement cost expression. The experimental results proved the feasibility of the proposed path planning algorithm.

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.

Optimizing Urban Green Space Configurations for Enhanced Heat Island Mitigation: A Geographically Weighted Machine Learning Approach

Optimizing Urban Green Space Configurations for Enhanced Heat Island Mitigation: A Geographically Weighted Machine Learning Approach

Convex geometric motion planning of multi-body systems on lie groups via variational integrators and sparse moment relaxation

This paper reports a novel result: with proper robot models based on geometric mechanics, one can formulate the kinodynamic motion planning problems for rigid body systems as exact polynomial optimization problems. Due to the nonlinear rigid body dynamics, the motion planning problem for rigid body systems is nonconvex. Existing global optimization-based methods do not parameterize 3D rigid body motion efficiently; thus, they do not scale well to long-horizon planning problems. We use Lie groups as the configuration space and apply the variational integrator to formulate the forced rigid body dynamics as quadratic polynomials. Then, we leverage Lasserre’s hierarchy of moment relaxation to obtain the globally optimal solution via semidefinite programming. By leveraging the sparsity of the motion planning problem, the proposed algorithm has linear complexity with respect to the planning horizon. This paper demonstrates that the proposed method can provide globally optimal solutions or certificates of infeasibility at the second-order relaxation for 3D drone landing using full dynamics and inverse kinematics for serial manipulators. Moreover, we extend the algorithms to multi-body systems via the constrained variational integrators. The testing cases on cart-pole and drone with cable-suspended load suggest that the proposed algorithms can provide rank-one optimal solutions or nontrivial initial guesses. Finally, we propose strategies to speed up the computation, including an alternative formulation using quaternion, which provides empirically tight relaxations for the drone landing problem at the first-order relaxation.

Effects of lift-up building design, building setback, and urban open space on pedestrian danger under the joint effect of floodwater and wind

Urban block layouts affect both the flow patterns of floodwater and wind in urban built environments and ultimately pedestrian danger within the urban building array. Computational fluid dynamics (CFD) is used to simulate both the floodwater inundation process and the wind-blowing process within three kinds of urban block configurations under a floodwater-wind joint effect, including the lift-up building design, horizontal and vertical building setbacks, and urban open space. The obtained floodwater velocity, depth, and wind speed at the pedestrian level are adopted to quantify the level of pedestrian danger by using the instability threshold formula of a human body. A laboratory flume experiment regarding the stability/instability of a dummy that represents a pedestrian in a quasi-natural state is performed to qualitatively validate the numerical simulation results. Compared to the cuboid or triangular pillar that supports the lift-up building structure, the circular pillar yields the minimum resistance against floodwater and increases the wind speed at the pedestrian level, consequently leading to a large danger zone area. Horizontal building setbacks decrease the pedestrian danger, but vertical building setbacks intricately affect it. The existence of open space within the block decreases pedestrian danger to some extent depending on the location and area of open space. The wind plays the dominant role in triggering pedestrian instability for both the lift-up building design and the horizontal setback case. For both the lift-up building design and the open space configuration, the walking and running speeds of pedestrians decrease much more than the conventional urban block. The results of this study could be referred to by administrators and stakeholders in the design and management of urban building arrays to mitigate pedestrian risk in the content of resilient city management.

Efficient and scalable wave function compression using corner hierarchical matrices.

The exponential scaling of complete active space and full configuration interaction (CI) calculations limits the ability of quantum chemists to simulate the electronic structures of strongly correlated systems. Herein, we present corner hierarchically approximated CI (CHACI), an approach to wave function compression based on corner hierarchical matrices (CH-matrices)-a new variant of hierarchical matrices based on block-wise low-rank decomposition. By application to dodecacene, a strongly correlated molecule, we demonstrate that CH matrix compression provides superior compression compared to truncated global singular value decomposition. The compression ratio is shown to improve with increasing active space size. By comparison of several alternative schemes, we demonstrate that superior compression is achieved by (a) using a blocking approach that emphasizes the upper-left corner of the CI vector, (b) sorting the CI vector prior to compression, and (c) optimizing the rank of each block to maximize information density.

On Optimization of Factory Space Configuration

In factories, spatial configuration plays an important role in order to improve workers’ comfort which will result in maximising the productivity and at last will contribute to economy generation. With the goal of identifying the major variables influencing shoe factories' spatial layout choices, this study explores the spatial arrangement and organization of shoe factories. By analyzing a cluster of shoe manufacturing plants located in Agra in India through site visit and international case studies in Bangladesh, this study examines how factors such as workflow process, machinery placement, storage areas and logistical considerations impact the spatial arrangement of factories. With the help of workers and factory officials interviews in shoe factories, site visits, and spatial analysis techniques this research provides insights into the actual indoor environment of the factories in terms of spatial arrangement. This actual indoor environment is further comparatively analysed by existing standards for industrial buildings which helped to find out the potential areas for improvement. The findings of this research work aim at identifying the potential areas and best practices for improvement in spatial configuration of shoe factories which will contribute to workers’ comfort.

Augmenting Human Expertise in Weighted Ensemble Simulations through Deep Learning-Based Information Bottleneck.

The weighted ensemble (WE) method stands out as a widely used segment-based sampling technique renowned for its rigorous treatment of kinetics. The WE framework typically involves initially mapping the configuration space onto a low-dimensional collective variable (CV) space and then partitioning it into bins. The efficacy of WE simulations heavily depends on the selection of CVs and binning schemes. The recently proposed state predictive information bottleneck (SPIB) method has emerged as a promising tool for automatically constructing CVs from data and guiding enhanced sampling through an iterative manner. In this work, we advance this data-driven pipeline by incorporating prior expert knowledge. Our hybrid approach combines SPIB-learned CVs to enhance sampling in explored regions with expert-based CVs to guide exploration in regions of interest, synergizing the strengths of both methods. Through benchmarking on alanine dipeptide and chignolin systems, we demonstrate that our hybrid approach effectively guides WE simulations to sample states of interest and reduces run-to-run variances. Moreover, our integration of the SPIB model also enhances the analysis and interpretation of WE simulation data by effectively identifying metastable states and pathways and offering direct visualization of dynamics.

Experimental and LES Study of H2/CH4 Premixed Gas Deflagration Under Different Obstacle Conditions

To study the influence of obstacles on the premixed gas explosion process and provide a theoretical basis for the safe use of fuel and the space configuration within weakly constrained structures, experimental and numerical simulation studies were conducted to investigate the explosion behavior of H2/CH4 premixed gas under different obstacle conditions. According to the angle and position of obstacles, 12 explosion working conditions were set up, and the flame kinetic behavior under the combined influence was obtained. The results show that increasing the direct contact area between the obstacle and the flame near the ignition source can effectively reduce the explosion effect. The explosion consequence is most serious when the obstacle is located in the middle position of the weakly constrained structure. When the obstacle is close to the vent, the later the flame reaches the vent, the more the explosion pressure peaks, and the explosion impact decreases as the angle of the obstacle decreases. In the numerical simulation, it was also found that when the flame passes through the obstacle near the ignition source, it takes on a special “jellyfish” shape toward the vent. In conclusion, the results of the study are useful for making reasonable assumptions about the location of the ignition source and the presence of obstacles based on the degree of damage to the weakly confined structure caused by the premixed gas explosion.

A high-throughput and data-driven computational framework for novel quantum materials

Two-dimensional layered materials, such as transition metal dichalcogenides (TMDs), possess an intrinsic van der Waals gap at the layer interface, allowing for remarkable tunability of the optoelectronic features via external intercalation of foreign guests such as atoms, ions, or molecules. Herein, we introduce a high-throughput, data-driven computational framework for the design of novel quantum materials derived from intercalating planar conjugated organic molecules into bilayer transition metal dichalcogenides and dioxides. By combining first-principles methods, material informatics, and machine learning, we characterize the energetic and mechanical stability of this new class of materials and identify the fifty (50) most stable hybrid materials from a vast configurational space comprising ∼105 materials, employing intercalation energy as the screening criterion.

General Three-Body Problem in Conformal-Euclidean Space: New Properties of a Low-Dimensional Dynamical System

Despite the huge number of studies of the three-body problem in physics and mathematics, the study of this problem remains relevant due to both its wide practical application and taking into account its fundamental importance for the theory of dynamical systems. In addition, one often has to answer the cognitive question: is irreversibility fundamental for the description of the classical world? To answer this question, we considered a reference classical dynamical system, the general three-body problem, formulating it in conformal Euclidean space and rigorously proving its equivalence to the Newtonian three-body problem. It has been proven that a curved configuration space with a local coordinate system reveals new hidden symmetries of the internal motion of a dynamical system, which makes it possible to reduce the problem to a sixth-order system instead of the eighth order. An important consequence of the developed representation is that the chronologizing parameter of the motion of a system of bodies, which we call internal time, differs significantly from ordinary time in its properties. In particular, it more accurately describes the irreversible nature of multichannel scattering in a three-body system and other chaotic properties of a dynamical system. The paper derives an equation describing the evolution of the flow of geodesic trajectories, with the help of which the entropy of the system is constructed. New criteria for assessing the complexity of a low-dimensional dynamical system and the dimension of stochastic fractal structures arising in three-dimensional space are obtained. An effective mathematical algorithm is developed for the numerical simulation of the general three-body problem, which is traditionally a difficult-to-solve system of stiff ordinary differential equations.

K-Means Clustering in Fingerprint-Based Configuration Selection for Fitting Interatomic Potentials.

In this study, we present a method for selecting an arbitrary number of distinct configurations from a larger data set by applying k-means clustering to atomistic configuration fingerprints based on the CrystalNN model and radial distribution function (RDF). This approach improves the accuracy of fitting classical molecular dynamics interatomic potentials to density functional theory (DFT) data for both energies and forces while requiring fewer configurations than random selection. We demonstrate this improvement by fitting an embedded-atom method (EAM) potential for titanium, using various configurational sizes from an initial set of 1800 configurations. The k-means clustering consistently achieves better precision and lower standard deviations for a smaller number of configurations than random selection. The results also suggest that only about 30 configurations are sufficient to obtain an EAM model that describes well the full set of 1800 configurations in terms of energies and forces. Additionally, t-distributed stochastic neighbor embedding (t-SNE) method was used to reduce the configuration fingerprints into 2D space, and it revealed an overlap between two configuration subsets with and without Ti vacancy, indicating similar atomic environments. This similarity is captured by k-means clustering but not by random selection. Furthermore, when the overlapping configurations with vacancies were excluded from the k-means algorithm and used only as a test set, their energy and force predictions showed similar precision to those when they were included. This indicates that the overlapping configurations in the 2D t-SNE space indeed imply potential information redundancy among the atomistic configurations.