Rapid Exploration Research Articles

UAVs Path Planning based on Combination of Rapidly Exploring Random Tree and Rauch-Tung-Striebel Filter

Aiming at the problem of Unmanned Aerial Vehicle(UAV) formation path planning under complex constraints, a UAV formation path planning method based on the combination of Rapidly exploring Random Tree (RRT) and Rauch-Tung-Striebel (RTS) filter is proposed. Firstly, a path planning algorithm based on the improved RRT algorithm with adaptive step size is de-signed to solve the problem that the RRT algorithm is easy to fall into local optimum. Then, an RTS filter is introduced to smooth the trajectory planned by the improved RRT algorithm to achieve curvature continuity. Finally, taking the smooth trajectory as the reference, a UAV formation path planning algorithm over the Artificial Potential Field (APF) method is designed. The simulation results show that the designed UAV formation path planning algorithm can solve the planning problems of single trajectory and formation trajectories in complex constrained space, and can plan the formation trajectory with continuous curvature, to facilitate the UAV trajectory tracking control.

Noise suppression of airborne transient electromagnetic data with minimum curvature

The airborne transient electromagnetic (ATEM) method is a geophysical exploration technique that offers several advantages, including rapid exploration, minimal interference from complex terrains, dense data sampling, and environmental friendliness. However, the full-frequency time-domain secondary field signal acquisition of the ATEM data is susceptible to various electromagnetic interferences, resulting in noise in the collected data. This noise complicates subsequent processing and interpretation. To address this issue, we have developed a 1D minimum-curvature difference equation with unequal spacing based on minimum-curvature differential equations. Through research, we develop an ATEM noise-suppression method based on multiple single-step and multiple superposition-step implicit iterative formats. In addition, we have investigated the convergence of explicit and implicit iterative schemes via Fourier spectrum analysis and proven that the implicit iterative schemes converge. This method effectively suppresses noise in synthetic and real ATEM data during processing. Our results show that the minimum-curvature method is particularly effective in suppressing sferic and Gaussian noises in ATEM data. As a result, our method provides high-quality and reliable ATEM data for further data processing and interpretation. Moreover, the minimum-curvature method is also capable of suppressing noise in ground and ground-space transient electromagnetic data, as well as magnetotelluric data noise. Therefore, this method exhibits a wide range of potential applications.

Comparative Analysis of Deep Generative Model for Industrial Enzyme Design

Abstract: Although enzymes have the advantage of efficient catalysis, natural enzymes lack stability in industrial environments and do not even meet the required catalytic reactions. This prompted us to urgently de novo design new enzymes. Computational design is a powerful tool, allowing rapid and efficient exploration of sequence space and facilitating the design of novel enzymes tailored to specific conditions and requirements. It is beneficial to de novo design industrial enzymes using computational methods. Currently, only one tool explicitly designed for the enzyme-only generation performs unsatisfactorily. We have selected several general protein sequence design tools and systematically evaluated their effectiveness when applied to specific industrial enzymes. We investigated the literature related to protein generation. We summarized the computational methods used for sequence generation into three categories: structure-conditional sequence generation, sequence generation without structural constraints, and co-generation of sequence and structure. To effectively evaluate the ability of six computational tools to generate enzyme sequences, we first constructed a luciferase dataset named Luc_64. Then we assessed the quality of enzyme sequences generated by these methods on this dataset, including amino acid distribution, EC number validation, etc. We also assessed sequences generated by structure-based methods on existing public datasets using sequence recovery rates and root-mean-square deviation (RMSD) from a sequence and structure perspective. In the functionality dataset, Luc_64, ABACUS-R, and ProteinMPNN stood out for producing sequences with amino acid distributions and functionalities closely matching those of naturally occurring luciferase enzymes, suggesting their effectiveness in preserving essential enzymatic characteristics. Across both benchmark datasets, ABACUS-R and ProteinMPNN, have also exhibited the highest sequence recovery rates, indicating their superior ability to generate sequences closely resembling the original enzyme structures. Our study provides a crucial reference for researchers selecting appropriate enzyme sequence design tools, highlighting the strengths and limitations of each tool in generating accurate and functional enzyme sequences. ProteinMPNN and ABACUS-R emerged as the most effective tools in our evaluation, offering high accuracy in sequence recovery and RMSD and maintaining the functional integrity of enzymes through accurate amino acid distribution. Meanwhile, the performance of protein general tools for migration to specific industrial enzymes was fairly evaluated on our specific industrial enzyme benchmark.

Development of High-Throughput Experimentation Approaches for Rapid Radiochemical Exploration.

Positron emission tomography is a widely used imaging platform for studying physiological processes. Despite the proliferation of modern synthetic methodologies for radiolabeling, the optimization of these reactions still primarily relies on inefficient one-factor-at-a-time approaches. High-throughput experimentation (HTE) has proven to be a powerful approach for optimizing reactions in many areas of chemical synthesis. However, to date, HTE has rarely been applied to radiochemistry. This is largely because of the short lifetime of common radioisotopes, which presents major challenges for efficient parallel reaction setup and analysis using standard equipment and workflows. Herein, we demonstrate an effective HTE workflow and apply it to the optimization of copper-mediated radiofluorination of pharmaceutically relevant boronate ester substrates. The workflow utilizes commercial equipment and allows for rapid analysis of reactions for optimizing reactions, exploring chemical space using pharmaceutically relevant aryl boronates for radiofluorinations, and constructing large radiochemistry data sets.

SWISH-X, an Expanded Approach to Detect Cryptic Pockets in Proteins and at Protein-Protein Interfaces.

Protein-protein interactions mediate most molecular processes in the cell, offering a significant opportunity to expand the set of known druggable targets. Unfortunately, targeting these interactions can be challenging due to their typically flat and featureless interaction surfaces, which often change as the complex forms. Such surface changes may reveal hidden (cryptic) druggable pockets. Here, we analyze a set of well-characterized protein-protein interactions harboring cryptic pockets and investigate the predictive power of current computational methods. Based on our observations, we developed a new computational strategy, SWISH-X (SWISH Expanded), which combines the established cryptic pocket identification capabilities of SWISH with the rapid temperature range exploration of OPES MultiThermal. SWISH-X is able to reliably identify cryptic pockets at protein-protein interfaces while retaining its predictive power for revealing cryptic pockets in isolated proteins, such as TEM-1 β-lactamase.

323 Applying MeSH Tree Structures and Condition-to-MeSH Mapping to Catalog and Characterize Clinical Trials Research Focus Areas

OBJECTIVES/GOALS: Characterizing and analyzing research studies presents several challenges given the various ways studies may be labeled or organized. The Medical Subject Headings (MeSH) thesaurus is a hierarchical vocabulary that can index and organize research foci using common business intelligence tools to enable rapid exploration of research portfolios. METHODS/STUDY POPULATION: Metadata from ClinicalTrials.gov on 455,437 trials were downloaded and all MeSH terms associated with trials in the condition_browse section were loaded into a database. The corresponding MeSH trees for each term were then identified and mapped to their ancestor terms within the tree. Trials were then indexed based on top four hierarchical levels for each associated MeSH term. Trials performed at the University of Miami (UM) were identified based on locations associated with the trial as well as matching National Clinical Trial (NCT) numbers identified from internal research administration systems. Business intelligence software (Microsoft PowerBI) was applied to the corresponding dataset to enable end user exploration and analysis of the trials within ClinicalTrials.gov. RESULTS/ANTICIPATED RESULTS: A total of 3,271 studies associated with UM were identified, of which, 3,054 (93.3%) had at least one condition MeSH term linked. A total of 7,711 MeSH terms were associated with the trials overall, representing 1,112 unique MeSH terms; the most common terms were carcinoma (164), lymphoma (155), HIV Infections (139), neoplasms (136), and leukemia (122). Utilizing MeSH hierarchy, trials were characterized were categorized into 36 different trees. The most common top tree nodes were neoplasms (1,181), followed by pathological conditions/signs and symptoms (913), immune system diseases (574), nervous system diseases (513), and digestive system diseases (483). Within trees, a total of 184, 681, and 1057 different MeSH terms were specified at the second, third, and fourth nodes in the hierarchy respectively. DISCUSSION/SIGNIFICANCE: Utilizing existing metadata from trials posted on ClinicalTrials.gov and MeSH tree structures can enable organizations to readily explore the foci of clinical trials research. High rates of MeSH term association to research study conditions are necessary to ensure adequate representation of research foci.

Design and validation of refractory alloys using machine learning, CALPHAD, and experiments

Refractory multicomponent alloys (RMCAs) have garnered attention as potential materials for high-temperature structural applications, due to their excellent mechanical properties. However, conventional alloy design has limitations in terms of constrained compositional space and a lack of computational databases with adequate coverage. To address this, we present a design framework that leverages machine learning (ML), the CALculation of PHAse Diagram (CALPHAD) method, and experimental validation to efficiently develop refractory alloys. The present study focuses on the Mo-Nb-W ternary system. Six ternary alloys were inversely designed by means of the conditional generative adversarial network (cGAN) and fabricated via arc melting. The ternary alloys exhibit a single BCC phase which is consistent with CALPHAD calculations as well as Scheil simulations. The present interactive design loop between the ML surrogate model and experiments is demonstrated through the accurate hardness prediction, resulting in cGAN models capable of rapid exploration of the higher-order design space. The hardness of the Mo-Nb-W alloys is in the range of 5–6 GPa due to their solid solution strengthening.

Load Frequency Active Disturbance Rejection Control Based on Improved Particle Swarm Optimization

A load frequency control (LFC) system based on active disturbance rejection control (ADRC) is designed to solve the problem of frequency modulation caused by large-scale renewable energy grid connection. Traditional parameter-tuning methods are inefficient and often fail to achieve desired control outcomes. To overcome this, an improved Particle Swarm Optimization (PSO) algorithm is introduced, incorporating Levy flight and chaotic mapping. This enhanced algorithm combines the long step–length search capability of Levy flight with the rapid exploration of initial solution space using Tent chaotic mapping, enhancing PSO’s global search ability and addressing premature convergence issues. Simulation results demonstrate that the ADRC controller optimized by the improved algorithm exhibits greater robustness and smaller deviation compared to the original algorithm, showcasing its excellent control performance.

Autonomous Rapid Exploration in Close-Proximity of Asteroids

The increasing number of space missions may overwhelm ground support infrastructure, prompting the need for autonomous deep-space guidance, navigation, and control (GNC) systems. These systems offer sustainable and cost-effective solutions, particularly for asteroid missions that deal with uncertain environments. This study proposes a paradigm shift from the proposals currently found in the literature for autonomous asteroid exploration, which inherit the conservative architecture from the ground-in-the-loop approach that relies heavily on reducing uncertainties before close-proximity operations. Instead, it advocates for robust guidance and control to handle uncertainties directly, without extensive navigation campaigns. From a series of conservative assumptions, the authors demonstrate the feasibility of this autonomous GNC for robotic spacecraft by using existing technology. It is shown that a bolder operational approach enables autonomous spacecraft to significantly reduce exploration time by weeks or months. This paradigm shift holds great potential for reducing costs and saving time in autonomous missions of the future.

Localization landscape of optical waves in multifractal photonic membranes

In this paper, we investigate the localization properties of optical waves in disordered systems with multifractal scattering potentials. In particular, we apply the localization landscape theory to the classical Helmholtz operator and, without solving the associated eigenproblem, show accurate predictions of localized eigenmodes for one- and two-dimensional multifractal structures. Finally, we design and fabricate nanoperforated photonic membranes in silicon nitride (SiN) and image directly their multifractal modes using leaky-mode spectroscopy in the visible spectral range. The measured data demonstrate optical resonances with multiscale intensity fluctuations in good qualitative agreement with numerical simulations. The proposed approach provides a convenient strategy to design multifractal photonic membranes, enabling rapid exploration of extended scattering structures with tailored disorder for enhanced light-matter interactions.

Towards field deployment of MAVs in adaptive exploration of GPS-denied subterranean environments

Exploration and safe navigation in previously unknown GPS-denied obstructed areas are major challenges for autonomous robots when deployed in subterranean environments. In response, this work establishes an Exploration-Planning framework developed for the real-world deployment of Micro Aerial Vehicles (MAVs) in subterranean exploration missions. The fundamental task for an autonomous MAV to navigate in an unknown area, is to decide where to look while navigating such that the MAV will acquire more information about the surrounding. The work presented in this article focuses around 3D exploration of large-scale caves or multi-branched tunnel like structures, while still prioritizing the Look-Ahead and Move-Forward approach for fast navigation in previously unknown areas. In order to achieve such exploration behaviour, the proposed work utilizes a two-layer navigation approach. The first layer deals with computing traversable frontiers to generate the look ahead poses in the constrained field of view, aligned with the MAV’s heading vector that leads to rapid continuous exploration. The proposed frontier distribution based switching goal selection approach allows the MAV to explore various terrains, while still regulating the MAV’s heading vector. The second layer of the proposed scheme deals with global cost based navigation of the MAV to the potential junction in a multi-branched tunnel system leading to a continuous exploration of partially seen areas. The proposed framework is a combination of a novel frontier goal selection approach, risk-aware expandable grid based path planning, nonlinear model predictive control and artificial potential fields based on local reactive navigation, obstacle avoidance, and control for the autonomous deployment of MAVs in extreme environments.

State of the art and prospects for the development of onboard radio systems for deep space communications

Currently, due to the rapid exploration of interplanetary space, more and more missions are being sent to deep space, and they promise to generate an exponentially increasing amount of data in the form of complex scientific measurements, high-resolution images and video. As a response to these challenges, scientists' attention is focused on the possibilities of laser, or optical, communication due to its potential to exceed the bandwidth of radio channels. And although broadband laser communication for near-Earth orbit and satellites in lunar orbit has been verified, deep space creates new problems for it, which will not soon allow abandoning the radio range. Therefore, this article discusses both the results achieved and the prospects for using the radio band in promising deep space communication systems. The purpose of the work was to assess the current state and prospects for the development of onboard deep-space radio communication systems. Based on the analysis of performance of foreign space-based transponders, the current state and prospects for the development of on-board radio systems for deep space communications are determined. The frequency ranges of space communications are considered, predictive estimates for the development of signal-code structures are obtained and the prospects for using various modulation methods are defined. The SDST transponder application is considered in detail by the example of telecommunications unit of the Mars Reconnaissance Orbiter (MRO) data-relay satellite. The proposed forecast estimates can be used in the development of radio systems for deep space communications.

Modeling benefits and tradeoffs of green infrastructure: Evaluating and extending parsimonious models for neighborhood stormwater planning

Green infrastructure is often proposed to complement conventional urban stormwater management systems that are stressed by extreme storms and expanding impervious surfaces. Established hydrological and hydraulic models inform stormwater engineering but are time- and data-intensive or aspatial, rendering them inadequate for rapid exploration of solutions. Simple spreadsheet models support quick site plan assessments but cannot adequately represent spatial interactions beyond a site. The present study builds on the Landscape Green Infrastructure Design (L-GrID) Model, a process-based spatial model that enables rapid development and exploration of green infrastructure scenarios to mitigate neighborhood flooding. We first explored how well L-GrID could replicate flooding reports in a neighborhood in Chicago, Illinois, USA, to evaluate its potential for green infrastructure planning. Although not meant for prediction, L-GrID was able to replicate the flooding reported and helped identify strategies for flood control. Once evaluated for this neighborhood, we extended the model to include water quality through the representation of dispersion and settling mechanisms for two pollutant surrogates—total nitrogen and total suspended solids. With the extended model, Landscape Green Infrastructure Design Model-Water Quality (L-GrID-WQ), we examined benefits, costs, and tradeoffs for different green infrastructure strategies. Bioswales were slightly more effective than other green infrastructure types in reducing flooding extent and downstream runoff and pollution, through increased infiltration and settling capacity. Permeable pavers followed in effectiveness and are suggested where spatial constraints may limit the installation of bioswales. Although green infrastructure supports both flooding and pollution control, small tradeoffs between these functions emerged across spatial layouts: strategies based on only curb-cuts better controlled pollution, while layouts that followed the path of water flow better controlled flooding. By illuminating such tradeoffs, L-GrID-WQ can support green infrastructure planning that prioritizes unique concerns in different areas of a landscape.

Memorized Rapidly Exploring Random Tree Optimization (MRRTO): An Enhanced Algorithm for Robot Path Planning

Abstract With the advancement of the robotics world, many path-planning algorithms have been proposed. One of the important algorithms is the Rapidly Exploring Random Tree (RRT) but with the drawback of not guaranteeing the optimal path. This paper solves this problem by proposing a Memorized RRT Optimization Algorithm (MRRTO Algorithm) using memory as an optimization step. The algorithm obtains a single path from the start point, and another from the target point to store only the last visited new node. The method for computing the nearest node depends on the position, when a new node is added, the RRT function checks if there is another node closer to the new node rather than that is closer to the goal point. Simulation results with different environments show that the MRRTO outperforms the original RRT Algorithm, graph algorithms, and metaheuristic algorithms in terms of reducing time consumption, path length, and number of nodes used.

Characterizing hazardous substances of shale gas wastewater from the upper Yangtze River: A focus on heavy metals and organic compounds

In the last decade, rapid shale gas exploration in upper Yangtze River ecological zone in China has led to increasing concerns about the environmental impact of shale gas wastewater (SGW). However, our understanding of the types of potential hazardous substances of SGW remains limited. In this study, eight SGW samples from three shale gas regions in upper Yangtze River: the Sichuan Basin, the Guizhou Plateau, and the Three Gorges Area were collected, and their general water quality, trace metals, and organic compounds were comprehensively analyzed. Our in-depth analysis detected 55 kinds of trace heavy metals, with 24 exceeding detection limits. Most of them were of the concentration below 100 μg/L. Concentrations of primary pollutants, including Cd, Cr, As, Pb, and Ni, remained below Integrated Wastewater Discharge Standard (GB 8978–1996), indicating minimal environmental risk. The organic analysis identified 45 to 104 kinds of volatile and semi-volatile organic compounds in SGW samples from different regions. SGW samples from the Sichuan Basin exhibited a balanced proportion of aliphatic and aromatic compounds, with oxygen and nitrogen-substituted heteroatomic compounds prevailing, while SGW samples from the Guizhou Plateau and the Three Gorges Area were dominated by aromatic compounds, particularly hydrocarbons. Several organic substances exhibited high response strengths across multiple SGW samples, including isoquinoline, dibenzylamine, 2,4-di-tert-butylphenol, 1,2,3,4-tetrahydro-naphthalene, and 1,2,3,4-tetrahydro-6-methyl-naphthalene. The Globally Harmonized System (GHS) of Classification and Labelling of Chemicals classified most high-response organics as high acute and chronic aquatic hazards. Our findings indicate that high salinity and a variety of high-risk organic pollutants, rather than heavy metals, are the primary pollutants in SGW, underscoring the urgency of safety management of SGW.

Integrating uncertainty into deep learning models for enhanced prediction of nanocomposite materials’ mechanical properties

In this paper, we present a novel deep-learning framework that incorporates quantified uncertainty for predicting the mechanical properties of nanocomposite materials, specifically taking into account their morphology and composition. Due to the intricate microstructures of nanocomposites and their dynamic changes under diverse conditions, traditional methods, such as molecular dynamics simulations, often impose significant computational burdens. Our machine learning models, trained on comprehensive material datasets, provide a lower computational cost alternative, facilitating rapid exploration of design spaces and more reliable predictions. We employ both convolutional neural networks and feedforward neural networks for our predictions, training separate models for yield strength and ultimate tensile strength. Furthermore, we integrate uncertainty quantification into our models, thereby providing confidence intervals for our predictions and making them more reliable. This study paves the way for advancements in predicting the properties of nanocomposite materials and could potentially be expanded to cover a broad spectrum of materials in the future.

Phase Selection Rules of Multi-Principal Element Alloys.

Computational prediction of phase stability of multi-principal element alloys (MPEAs) holds a lot of promise for rapid exploration of the enormous design space and autonomous discovery of superior structural and functional properties. Regardless of many plausible works that rely on phenomenological theory and machine learning, precise prediction is still limited by insufficient data and the lack of interpretability of some machine learning algorithms, e.g., convolutional neural network. In this work, a comprehensive approach is presented, encompassing the development of a complete dataset that contains 72387 density functional theory calculations, as well as a predictive global phenomenological descriptor. The phase selection descriptor, based on atomic electronegativity and valence electron concentration, significantly outperforms the widely used valence electron concentration, excelling in both accuracy (with an f1 score of 63% compared to 47%) and its ability to predict the HCP phase (0.48 recall compared to 0). The comprehensive data mining on the global design space of 61425 quaternary MPEAs made from 28 possible metals, together with the phenomenological theory and physical interpretation, will set up a solid computational science foundation for data-driven exploration of MPEAs.

Intuitive and Efficient Approach to Determine the Band Structure of Covalent Organic Frameworks from Their Chemical Constituents.

The optical, electronic, and (photo) catalytic properties of covalent organic frameworks (COFs) are largely determined by their electronic structure and, specifically, by their Frontier conduction and valence bands (VBs). In this work, we establish a transparent relationship between the periodic electronic structure of the COFs and the orbital characteristics of their individual molecular building units, a relationship that is challenging to unravel through conventional solid-state calculations. As a demonstration, we applied our method to five COFs with distinct framework topologies. Our approach successfully predicts their first-principles conduction and VBs by expressing them as a linear combination of the Frontier molecular orbitals localized on the COF fragments. We demonstrate that our method allows for the rapid exploration of the impact of chemical modifications on the band structures of COFs, making it highly suitable for further application in the quest to discover new functional materials.

Research on Multiple-AUVs Collaborative Detection and Surrounding Attack Simulation.

Due to limitations in operational scope and efficiency, a single Autonomous Underwater Vehicle (AUV) falls short of meeting the demands of the contemporary marine working environment. Consequently, there is a growing interest in the coordination of multiple AUVs. To address the requirements of coordinated missions, this paper proposes a comprehensive solution for the coordinated development of multi-AUV formations, encompassing long-range ferrying, coordinated detection, and surrounding attack. In the initial phase, detection devices are deactivated, employing a path planning method based on the Rapidly Exploring Random Tree (RRT) algorithm to ensure collision-free AUV movement. During the coordinated detection phase, an artificial potential field method is applied to maintain AUV formation integrity and avoid obstacles, dynamically updating environmental probability based on formation movement. In the coordinated surroundings attack stage, predictive capabilities are enhanced using Long Short-Term Memory (LSTM) networks and reinforcement learning. Specifically, LSTM forecasts the target's position, while the Deep Deterministic Policy Gradient (DDPG) method controls AUV formation. The effectiveness of this coordinated solution is validated through an integrated simulation trajectory.

Research of the Possibilities of Using a Rapidly Exploring Random Tree Algorithm in Solving Problems of Movement Planning of Autonomous Robots

The article is devoted to the analysis of features and evaluation of the prospects for using the RRT method in the problem of motion planning for autonomous robots. It is noted that the expansion of the areas of application of modern robotics is inextricably linked with an increase in the level of functionality and improvement of the designs of the created samples, for which the placement of a manipulator on a mobile platform becomes a typical layout. Based on a review of the literature and generalization of experimental data, it is shown that the use of the RRT method and its modifications opens up the fundamental possibility of developing a universal motion planner for mobile and manipulative robots, including systems with a manipulator on a mobile platform, as well as systems with a redundant or reconfigurable structure. Based on the results of the analysis, it was found that the effectiveness of the RRT method is largely determined by the declared parameter of the growth factor. A decentralized variant of the counter-growth RRT method is proposed, which makes it possible to plan the movements of autonomous mobile robots in the process of their mutual approach and subsequent docking. The fundamental possibility of automatic docking of autonomous robots in an environment with obstacles is confirmed by the results of a full-scale experiment.