Space Research Articles

Adaptive Constraint Relaxation-Based Evolutionary Algorithm for Constrained Multi-Objective Optimization

The key problem to solving constrained multi-objective optimization problems (CMOPs) is how to achieve a balance between objectives and constraints. Unfortunately, most existing methods for CMOPs still cannot achieve the above balance. To this end, this paper proposes an adaptive constraint relaxation-based evolutionary algorithm (ACREA) for CMOPs. ACREA adaptively relaxes the constraints according to the iteration information of population, whose purpose is to induce infeasible solutions to transform into feasible ones and thus improve the ability to explore the unknown regions. Completely ignoring constraints can cause the population to waste significant resources searching for infeasible solutions, while excessively satisfying constraints can trap the population in local optima. Therefore, balancing constraints and objectives is a crucial approach to improving algorithm performance. By appropriately relaxing the constraints, it induces infeasible solutions to be transformed into feasible ones, thus obtaining more information from infeasible solutions. At the same time, it also establishes an archive for the storage and update of solutions. In the archive update process, a diversity-based ranking is proposed to improve the convergence speed of the algorithm. In the selection process of the mating pool, common density selection metrics are incorporated to enable the algorithm to obtain higher-quality solutions. The experimental results show that the proposed ACREA algorithm not only achieved the best Inverse Generation Distance (IGD) value in 54.6% of the 44 benchmark test problems and the best Hyper Volume (HV) value in 50% of them, but also obtained the best results in seven out of nine real-world problems. Clearly, CP-TSEA outperforms its competitors.

Enabling Adaptive Sampling for Intra-Window Join: Simultaneously Optimizing Quantity and Quality

>Sampling is one of the most widely employed approximations in big data processing. Among various challenges in sampling design, sampling for join is particularly intriguing yet complex. This perplexing problem starts with a classical case where the join of two Bernoulli samples shrinks its output size quadratically and exhibits a strong dependency on the input data, presenting a unique challenge that necessitates adaptive sampling to guarantee both the quantity and quality of the sampled data. The community has made strides in achieving this goal by constructing offline samples and integrating support from indexes or key frequencies. However, when dealing with stream data, due to the need for real-time processing and high-quality analysis, methods developed for processing static data become unavailable. Consequently, a fundamental question arises: Is it possible to achieve adaptive sampling in stream data without relying on offline techniques? To address this problem, we propose FreeSam, which couples hybrid sampling with intra-window join, a key stream join operator. Our focus lies on two widely used metrics: output size, ensuring quantity, and variance, ensuring quality. FreeSam enables adaptability in both the desired quantity and quality of data sampling by offering control on the two-dimensional space spanned by these metrics. Meanwhile, adjustable trade-offs between quality and performance make FreeSam practical for use. Our experiments show that, for every 1% increase in latency limitation, FreeSam can yield a 3.83% increase in the output size while maintaining the level of the estimator's variance. Additionally, we give FreeSam a multi-core implementation and ensure predictability of its latency through both an analytic model and a neural network model. The accuracy of these models is 88.05% and 96.75% respectively.

Distinct Inhibitory Control Mechanisms Underlie Selective Social Inferences

ABSTRACT We investigated the role of distinct inhibitory processes as 4- to 6-year-olds from the Northeastern United States (N = 48, Mage = 68.27 months, 22 boys, 26 girls; 63% White, 6% Black, 4% Asian, 2% Hispanic, 8% more than one race, with 17% not reporting) and adults evaluated accurate or deceptive information from human or non-human (arrow) cues indicating particular locations in space. We achieved this by tracking the trajectories of our participants’ manual responses in 3D space over time. This approach allowed us to isolate inhibitory mechanisms that monitor for conflict between stimuli and representations from inhibitory mechanisms that resolve that conflict to make a response. Regardless of the cue’s accuracy, the participants we tested needed more inhibitory resources to monitor human cues that indicated the location of a hidden object than arrows. The inhibitory resources the children we tested needed to control their response (i.e., respond where the cue was pointing when it was accurate or respond alternatively when it was deceptive) was stable across the trials when the cue was a human pointing. When it was an arrow, older children needed fewer resources. These data suggest that there is continuity in participants' use of the resources necessary to monitor human information for deception, but development in the resources needed to resolve such information when learning selectively.

Fractal Characterization on Three-Dimensional Tortuosity of Fault Tectonic

Faults, as a kind of fracture tectonics, play a role in reservoir closure or provide oil and gas transportation channels. The accurate understanding of the distribution characteristics of faults is significant for oil and gas exploration. The traditional fractal dimension for fault number (Df3) cannot comprehensively characterize the complexity and heterogeneity of fault network distribution. In this paper, a fractal characterization method on three-dimensional (3D) tortuosity of fault tectonics is proposed based on 3D seismic exploration. The methodology is described in detail to establish the model on the fractal dimension for the 3D tortuosity of fault tectonics. The results show the proposed method of estimation of the DT3 displaying high accuracy and rationality. Compared with the traditional fractal dimension Df3, the proposed DT3 can comprehensively characterize the fractal characteristics of faults network systems in the 3D space. This study achieves a breakthrough in the fractal characterization of the 3D tortuosity of fault tectonics. It is worth further study for establishing an analytical fractal equation based on the DT3 and oil or gas transfer, which can provide the theoretical foundation and technical support for oil and gas exploration.

Magnetic Field Control Using an Electromagnetic Actuation System with Combined Air‐Core and Metal‐Core Coils

Magnetic fields are widely utilized for remote control of magnetic objects, with various actuation systems developed for manipulating miniature robots in research and biomedical applications. When designing a manipulation system providing a uniform magnetic field, it is also important to consider both the accessibility of the workspace and the integration of imaging tools. This article presents an electromagnetic coil system that manipulates magnetic objects within a 3D space, enhancing the magnetic field in an upward orientation. The system includes eight metal‐core coils arranged hemispherically to ensure unimpeded access to the workspace and imaging tools, along with two air‐core coils. Easier control and repeatability of the magnetic field are achieved using two joysticks and sequential programming. The versatility of the system is demonstrated by using it to manipulate a magnetic guidewire and to guide micromagnets to various targets. Additionally, using this system, oscillating magnetic fields effectively control swarms of magnetic nanoparticles, enabling operations such as dispersion, assembly, and ribbon‐like shape formation. Furthermore, the manipulation of cell‐based microrobots (cell‐bots) showcases the system's capability to handle single and multiple cell‐bots, facilitating their collection while preserving cell viability. These experiments underscore the system's potential for fundamental biomedical research and various applications.

Precise Synthesis of Dual-single-atom Electrocatalysts through Pre-coordination-directed in situ Confinement for CO2Reduction.

Dual-single-atom catalysts (DSACs) are the next paradigm shift in single-atom catalysts because of the enhanced performance brought about by the synergistic effects between adjacent bimetallic pairs. However, there are few methods for synthesizing DSACs with precise bimetallic structures. Herein, a pre-coordination strategy is proposed to precisely synthesize a library of DSACs. This strategy ensures the selective and effective coordination of two metals via phthalocyanines with specific coordination sites, such as -F- and -OH-. Subsequently, in-situ confinement inhibits the migration of metal pairs during high-temperature pyrolysis, and obtains the DSACs with precisely constructed metal pairs. Despite changing synthetic parameters, including transition metal centers, metal pairs, and spatial geometry, the products exhibit similar atomic metal pairs dispersion properties, demonstrating the universality of the strategy. The pre-coordination strategy synthesized DSACs shows significant CO2 reduction reaction performance in both flow-cell and practical rechargeable Zn-CO2 batteries. This work not only provides new insights into the precise synthesis of DSACs, but also offers guidelines for the accelerated discovery of efficient catalysts.

Precise Synthesis of Dual‐single‐atom Electrocatalysts through Pre‐coordination‐directed in situ Confinement for CO2 Reduction

Dual‐single‐atom catalysts (DSACs) are the next paradigm shift in single‐atom catalysts because of the enhanced performance brought about by the synergistic effects between adjacent bimetallic pairs. However, there are few methods for synthesizing DSACs with precise bimetallic structures. Herein, a pre‐coordination strategy is proposed to precisely synthesize a library of DSACs. This strategy ensures the selective and effective coordination of two metals via phthalocyanines with specific coordination sites, such as –F– and –OH–. Subsequently, in‐situ confinement inhibits the migration of metal pairs during high‐temperature pyrolysis, and obtains the DSACs with precisely constructed metal pairs. Despite changing synthetic parameters, including transition metal centers, metal pairs, and spatial geometry, the products exhibit similar atomic metal pairs dispersion properties, demonstrating the universality of the strategy. The pre‐coordination strategy synthesized DSACs shows significant CO2 reduction reaction performance in both flow‐cell and practical rechargeable Zn‐CO2 batteries. This work not only provides new insights into the precise synthesis of DSACs, but also offers guidelines for the accelerated discovery of efficient catalysts.

Being-Time, or How Traditional Japanese Thought Collided with Western Philosophy and Modern Physics at Hiroshima

The atom bomb that annihilated Hiroshima, Japan, on August 6, 1945, proved Albert Einstein’s theory of relativity. Mass became energy and the classic Western dialectic of three-dimensional space and linear time was displaced by the integrated concept of spacetime. On that day, modern physics also collided with the traditional Japanese understanding that space and time are interdependent phenomena. This collision speaks to conceptual parallels relating Buddhist thought, modern Japanese philosophy, phenomenology, and the physics of spacetime. The thirteenth-century Zen Buddhist monk Dōgen said that all phenomena are made possible by the universal principle of emptiness and that our existence arises with each moment in what he termed Being-Time, where past, present, and future co-exist. The Japanese philosopher Nishida Kitarō, along with Tanabe Hajime and Watsuji Tetsurō, saw reality as a multi-dimensional field of space-in-time where the individual subject no longer stood apart from the objective world but instead arose dynamically as contingent activities rather than an autonomous thing. The German philosopher Martin Heidegger rejected the rational binary of subject and object in Western thought and grounded our “being” in phenomena that came from their temporal being-in-the-world rather than representing some preexisting objective reality. The physicist Albert Einstein radically rethought the dialectic of three-dimensional space and linear time, used to schematize the physical world in the West since antiquity, and theorized the relativity of spacetime, in which all phenomena occur in space in relation to their place in time. Interwoven, these intellectual threads stripped the bombing of Hiroshima of its historical inevitability. The story of human experience lost its customary sense of going <i>somewhere</i> predetermined and was revealed instead to be an endlessly discrete accumulation of moments that could go <i>anywhere</i> at every transient and directionless moment in time.

A Flexible Hierarchical Framework for Implicit 3D Characterization of Bionic Devices.

In practical applications, integrating three-dimensional models of bionic devices with simulation systems can predict their behavior and performance under various operating conditions, providing a basis for subsequent engineering optimization and improvements. This study proposes a framework for characterizing three-dimensional models of objects, focusing on extracting 3D structures and generating high-quality 3D models. The core concept involves obtaining the density output of the model from multiple images to enable adaptive boundary surface detection. The framework employs a hierarchical octree structure to partition the 3D space based on surface and geometric complexity. This approach includes recursive encoding and decoding of the octree structure and surface geometry, ultimately leading to the reconstruction of the 3D model. The framework has been validated through a series of experiments, yielding positive results.

Redefining Spectral Data Analysis with Immersive Analytics: Exploring Domain-Shifted Model Spaces for Optimal Model Selection.

Modern developments in autonomous chemometric machine learning technology strive to relinquish the need for human intervention. However, such algorithms developed and used in chemometric multivariate calibration and classification applications exclude crucial expert insight when difficult and safety-critical analysis situations arise, e.g., spectral-based medical decisions such as noninvasively determining if a biopsy is cancerous. The prediction accuracy and interpolation capabilities of autonomous methods for new samples depend on the quality and scope of their training (calibration) data. Specifically, analysis patterns within target data not captured by the training data will produce undesirable outcomes. Alternatively, using an immersive analytic approach allows insertion of human expert judgment at key machine learning algorithm junctures forming a sensemaking process performed in cooperation with a computer. The capacity of immersive virtual reality (IVR) environments to render human comprehensible three-dimensional space simulating real-world encounters, suggests its suitability as a hybrid immersive human-computer interface for data analysis tasks. Using IVR maximizes human senses to capitalize on our instinctual perception of the physical environment, thereby leveraging our innate ability to recognize patterns and visualize thresholds crucial to reducing erroneous outcomes. In this first use of IVR as an immersive analytic tool for spectral data, we examine an integrated IVR real-time model selection algorithm for a recent model updating method that adapts a model from the original calibration domain to predict samples from shifted target domains. Using near-infrared data, analyte prediction errors from IVR-selected models are reduced compared to errors using an established autonomous model selection approach. Results demonstrate the viability of IVR as a human data analysis interface for spectral data analysis including classification problems.

Growth and Quality Analysis of Baby Corn (Zea mays L.) and Fodder as Influenced by NPK and Zn Fertility under Varying Spatial Geometry in Indo-Gangetic Plain Zone of Bihar

Growth and Quality Analysis of Baby Corn (Zea mays L.) and Fodder as Influenced by NPK and Zn Fertility under Varying Spatial Geometry in Indo-Gangetic Plain Zone of Bihar

Алгоритмы группового управления подводными подвижными объектами

The problem of formation control is relevant when searching for objects, surveying a given area, and group monitoring. In the underwater environment, this problem is complicated by restrictions on the frequency of receiving navigation data and the speed of information exchange between underwater robots. The purpose of this article is to develop and study group control algorithms that ensure the given formation and the maintenance of this formation during movement. The article provides an overview of the results in the field of group control of underwater vehicles, provides a mathematical model of the object, navigation system, underwater environment and communication system. The equations of kinematics and dynamics of a solid body in three-dimensional space, supplemented by equations of actuators, are used as a mathematical model. Algorithms for the distribution of moving objects in formation and nonlinear control algorithms for moving in a line are proposed. When constructing the devices, it is assumed that there is a group leader who transmits his coordinates to the rest of the underwater robots. Motion control is synthesized by the method of positional trajectory control in the form of a function of external coordinates. A study was carried out using numerical methods, during which the process of the formation and movement along the trajectory described by straight line segments was studied. The influence of errors in the navigation system and the frequency of data updates on the error of maintaining a given position by a separate underwater robot is investigated.

Development of Augmented Reality-Based Radiation Protection Application for Healthcare Professionals

Background: Radiodiagnosis is widely performed in medical institutions. All medical professionals, including nurses, are at risk of radiation exposure. This study developed an educational application for radiation medical professionals to visualize the distribution of scattered radiation using augmented reality.Materials and Methods: A Monte Carlo simulation code was used to simulate mobile chest and abdominal radiography. The calculation results were incorporated into an augmented reality application. The results of the Monte Carlo calculations were validated by comparing them with radiation measurements. An augmented reality application for tablet devices was developed in Unity that visualizes the scattered radiation dose.Results and Discussion: The application was developed by visualizing the distribution of scattered radiation in mobile radiography in augmented reality in three-dimensional real space. The calculation results were validated, and the error between the displayed radiation dose values and the measured radiation dose values was generally less than 10%.Conclusion: The developed application allows users to overlay quantitative values of imperceptible radiation exposure doses onto any real-world environment. This enables users to intuitively understand the relationship between the distance from a radiation source and the received dose, thereby contributing to a better understanding of radiation protection in clinical settings.

An evaluation of source-blending impact on the calibration of SKA EoR experiments

ABSTRACT Twenty-one-centimetre signals from the Epoch of Reionization (EoR) are expected to be detected in the low-frequency radio window by the next-generation interferometers, particularly the Square Kilometre Array (SKA). However, precision data analysis pipelines are required to minimize the systematics within an infinitesimal error budget. Consequently, there is a growing need to characterize the sources of errors in EoR analysis. In this study, we identify one such error origin, namely source blending, which is introduced by the overlap of objects in the densely populated observing sky under SKA1-Low’s unprecedented sensitivity and resolution, and evaluate its two-fold impact in both the spatial and frequency domains using a novel hybrid evaluation (HEVAL) pipeline combining end-to-end simulation with an analytic method to mimic EoR analysis pipelines. Sky models corrupted by source blending induce small but severe frequency-dependent calibration errors when coupled with astronomical foregrounds, impeding EoR parameter inference with strong additive residuals in the two-dimensional power spectrum space. We report that additive residuals from poor calibration against sky models with blending ratios of 5 and 0.5 per cent significantly contaminate the EoR window. In contrast, the sky model with a 0.05 per cent blending ratio leaves little residual imprint within the EoR window, therefore identifying a blending tolerance at approximately 0.05 per cent. Given that the SKA observing sky is estimated to suffer from an extended level of blending, strategies involving de-blending, frequency-dependent error mitigation, or a combination of both, are required to effectively attenuate the calibration impact of source-blending defects.

A Transformer-Based Image-Guided Depth-Completion Model with Dual-Attention Fusion Module.

Depth information is crucial for perceiving three-dimensional scenes. However, depth maps captured directly by depth sensors are often incomplete and noisy, our objective in the depth-completion task is to generate dense and accurate depth maps from sparse depth inputs by fusing guidance information from corresponding color images obtained from camera sensors. To address these challenges, we introduce transformer models, which have shown great promise in the field of vision, into the task of image-guided depth completion. By leveraging the self-attention mechanism, we propose a novel network architecture that effectively meets these requirements of high accuracy and resolution in depth data. To be more specific, we design a dual-branch model with a transformer-based encoder that serializes image features into tokens step by step and extracts multi-scale pyramid features suitable for pixel-wise dense prediction tasks. Additionally, we incorporate a dual-attention fusion module to enhance the fusion between the two branches. This module combines convolution-based spatial and channel-attention mechanisms, which are adept at capturing local information, with cross-attention mechanisms that excel at capturing long-distance relationships. Our model achieves state-of-the-art performance on both the NYUv2 depth and SUN-RGBD depth datasets. Additionally, our ablation studies confirm the effectiveness of the designed modules.

Finite-time formation trajectory tracking control for unmanned underwater vehicles with time delays and Markov-switch topology

ABSTRACT The formation trajectory tracking control strategy of unmanned underwater vehicles is proposed for communication time delay and communication topology random switching. The methods ensure that formation systems are finite-time bounded under discrete time. Firstly, based on the consistency theory and the exact feedback linearised dynamics model of unmanned underwater vehicle, a control strategy is proposed for discrete formation system with communication time delay. Secondly, a cooperative control method is designed for communication problems of Markovian switching topology and time delay. Then, definition of finite-time bounded and linear matrix inequality technique are applied to sufficient conditions of system stability for the above problem. Finally, the formation motion of discrete unmanned underwater vehicle system in three-dimensional space is simulated, fully demonstrating the feasibility of the method.

Design, synthesis, characterization, theoretical calculations, molecular docking studies, and biological evaluation of new Fe(II) and Cu(II) complexes of 2-acetylpyridine derivative sulfonyl hydrazone Schiff base

Sulfonylhydrazones and their metal complexes are known to have potential biological activity. In this study, new Fe(II) (1) and Cu(II) (2) complexes of the 2-acetylpyridine derivative sulfonyl hydrazone (LH) were prepared. The new metal complexes were characterized by elemental analysis, IR spectroscopy, UV spectroscopy, and magnetic moment measurements. The molecular structure of (2) was elucidated by X-ray diffraction analysis, and the crystal package was obtained in three-dimensional space. To support the intermolecular interactions obtained from the X-ray diffraction results, Hirshfeld surface analysis and two-dimensional fingerprint maps of (2) were generated. The optimized molecular structures, total energies, molecular orbital energy values, molecular electrostatic potential maps, percentage distributions of the atomic orbitals to the molecular orbital energy levels, and global reactivity parameters of LH, 1, and 2 are obtained by using DFT/B3LYP/6–311G(d, p) for LH and DFT/B3LYP/LanL2DZ for 1 and 2 is 1:2. Elemental analysis showed that the stoichiometric metal/ligand ratio for 1 and 2. All data indicate that the ligand coordinates with the metal atoms via pyridine imine /nitrogens and sulfonyl oxygen. The well-diffusion approach was also used to test the antibacterial properties of the ligand and complexes against harmful microbes. The compounds were tested for DNA cleavage using agarose gel electrophoresis. Complex 1 was found to be effective on the plasmid DNA of pBR322. Finally, molecular docking studies of LH, 1, and 2 with A-DNA (PDB ID:3V9D), and B-DNA (PDB ID:1BNA) were presented to compare the experimental and theoretical results.

Enhancing brain image quality with 3D U-net for stripe removal in light sheet fluorescence microscopy

Light Sheet Fluorescence Microscopy (LSFM) is increasingly popular in neuroimaging for its ability to capture high-resolution 3D neural data. However, the presence of stripe noise significantly degrades image quality, particularly in complex 3D stripes with varying widths and brightness, posing challenges in neuroscience research. Existing stripe removal algorithms excel in suppressing noise and preserving details in 2D images with simple stripes but struggle with the complexity of 3D stripes. To address this, we propose a novel 3D U-net model for Stripe Removal in Light sheet fluorescence microscopy (USRL). This approach directly learns and removes stripes in 3D space across different scales, employing a dual-resolution strategy to effectively handle stripes of varying complexities. Additionally, we integrate a nonlinear mapping technique to normalize high dynamic range and unevenly distributed data before applying the stripe removal algorithm. We validate our method on diverse datasets, demonstrating substantial improvements in peak signal-to-noise ratio (PSNR) compared to existing algorithms. Moreover, our algorithm exhibits robust performance when applied to real LSFM data. Through extensive validation experiments, both on test sets and real-world data, our approach outperforms traditional methods, affirming its effectiveness in enhancing image quality. Furthermore, the adaptability of our algorithm extends beyond LSFM applications to encompass other imaging modalities. This versatility underscores its potential to enhance image usability across various research disciplines.

Two-dimensional reinforcement learning model-free fault-tolerant control for batch processes against multi- faults

Aiming at the characteristics of batch process changing along with time and batch directions, the existence of unmodeled dynamics, and the partial failure of actuators or/and sensors, we propose a novel 2D reinforcement learning (RL) fault tolerant control strategy without considering model parameters. Firstly, a two-Dimensional (2D) augmented state space model and 2D Q function-based fault tolerant control (FTC) framework is established. The 2D Bellman equation can be acquired by analyzing the relationship between the 2D value function and the 2D Q function. Based on the extended model and Q-learning concept of RL, a data-driven FTTC independent of model parameters is designed, and a 2D data-driven Q-learning algorithm is proposed. Finally, taking the pressure holding phase in the injection process as the experimental object, the control effect is compared with that of the traditional model-based FTC, and better tracking performance and unbiasedness to the probing noise can be proved.

Correlations between an Urban Three-Dimensional Pedestrian Network and Service Industry Layouts Based on Graph Convolutional Neural Networks: A Case Study of Xinjiekou, Nanjing

Urban high-density development has led to the emergence of complex three-dimensional pedestrian networks. As a crucial component of city centers, these networks significantly influence the spatial distribution of service industries. Understanding the correlation between pedestrian networks and service industry layouts is vital for effective planning and development. This study proposes a technical framework for analyzing the relationship between three-dimensional pedestrian networks and service industry layouts. Using the Xinjiekou central area in Nanjing as a case study, we constructed a three-dimensional pedestrian network model using the sDNA method. Focusing on catering formats, we introduced a method to study the spatial distribution characteristics of service industries in three-dimensional spaces and employed a graph convolutional network model to systematically analyze the correlation between pedestrian network closeness and betweenness with catering formats. The results indicate that pedestrian network closeness is significantly positively correlated with the number and average spending of catering formats, while betweenness shows almost no correlation. High-closeness areas, due to their traffic convenience and walkability, are more conducive to the concentration of catering formats and higher spending levels. Our findings provide valuable insights for catering format location decisions and the optimization of three-dimensional pedestrian networks, contributing to sustainable urban development.