Unknown Space Research Articles

Unique biogenesis and kinetics of hornwort Rubiscos revealed by synthetic biology systems

Hornworts are the only land plants that employ a pyrenoid to optimize Rubisco’s CO2 fixation. Yet, hornwort Rubisco remains poorly characterized. Here we assemble the hornwort Anthoceros agrestis Rubisco (AaRubisco) using the Arabidopsis thaliana SynBio expression system and observed the formation of stalled intermediates, prompting us to develop a new SynBio system with A. agrestis cognate chaperones. We successfully assembled AaRubisco and Rubisco from three other hornwort species. Unlike A. thaliana Rubisco, AaRubisco assembly is not dependent on RbcX or Raf2. Kinetic characterization reveals that hornwort Rubiscos exhibit a range of catalytic rates (3-10 s-1), but with similar affinity (∼30 μM) and specificity (∼70) for CO2. In other words, hornwort Rubiscos do not comply with the long held canonical catalytic trade-off observed in other land plants, providing experimental support that Rubisco kinetics may be phylogenetically constrained. Unexpectedly, we observed a 50% increase in AaRubisco catalytic rates when RbcX was removed from our SynBio system, without any reduction in specificity. Structural biology, biochemistry and proteomic analysis suggest that subtle differences in Rubisco large subunit interactions, when RbcX is absent during biogenesis, increases the accessibility of active sites and catalytic turnover rate. This study uncovered a previously unknown Rubisco kinetic parameter space and provides a SynBio chassis to expand the survey of other Rubisco kinetics. Our discovery could thus reshape the approaches for engineering Rubisco with superior kinetics.

Ai] Explore!: An Educational Game for Developing Programming Skills and Algorithmic Thinking

The game [ai] explore! was developed to introduce the algorithm behind the popular science exhibition. The idea behind the [ai] explore! exhibition was to show the application of mathematics and computer science in solving real engineering problems by creating the exhibit using the mathematical algorithm Heat Equation Driven Area Coverage (HEDAC). The algorithm is based on scientific research and has many applications: exploration and search in unknown space, painting, planning agricultural spraying, scanning 3D objects, etc. The exhibits show the process by which the algorithm paints objects with a virtual brush and explores their shape. By surveying the high school students, it was shown that new mathematical and engineering results can be explained and turned into a playable game. After playing the game, students respond positively to the game presented and state that they have a good understanding of the HEDAC algorithm presented and how it works. This motivated us to investigate [ai] explore! as an educational game for teaching mathematics and computer science. The aim of the research described in this paper is to investigate the potential of the developed game as an educational game. The game-based learning (GBL) approach is increasingly used to increase student motivation and engagement in STEM lessons. The main research question is: How do primary school teachers and students perceive the usefulness of the game [ai] explore! in supporting teaching and learning of computer science concepts? We have developed and implemented teaching scenarios on how the game [ai] explore! can be used as an educational game in computer science lessons. We show that the game has great potential to teach primary school students programming. In addition, the game promotes algorithmic thinking and computational thinking skills in general, which are valuable not only in computer science but also in other fields such as mathematics, where a systematic and analytical approach to problem-solving is required.

Autonomous Lunar Rover Localization while Fully Scanning a Bounded Obstacle-Rich Workspace.

This article addresses the scanning path plan strategy of a rover team composed of three rovers, such that the team explores unknown dark outer space environments. This research considers a dark outer space, where a rover needs to turn on its light and camera simultaneously to measure a limited space in front of the rover. The rover team is deployed from a symmetric base station, and the rover team's mission is to scan a bounded obstacle-rich workspace, such that there exists no remaining detection hole. In the team, only one rover, the hauler, can locate itself utilizing stereo cameras and Inertial Measurement Unit (IMU). Every other rover follows the hauler, while not locating itself. Since Global Navigation Satellite System (GNSS) is not available in outer space, the localization error of the hauler increases as time goes on. For rover's location estimate fix, one occasionally makes the rover home to the base station, whose shape and global position are known in advance. Once a rover is near the station, it uses its Lidar to measure the relative position of the base station. In this way, the rover fixes its localization error whenever it homes to the base station. In this research, one makes the rover team fully scan a bounded obstacle-rich workspace without detection holes, such that a rover's localization error is bounded by letting the rover home to the base station occasionally. To the best of our knowledge, this article is novel in addressing the scanning path plan strategy, so that a rover team fully scans a bounded obstacle-rich workspace without detection holes, while fixing the accumulated localization error occasionally. The efficacy of the proposed scanning and localization strategy is demonstrated utilizing MATLAB-based simulations.

Self-consistent solution of the Frank–Bilby equation for interfaces containing disconnections

The quantized Frank–Bilby equation can be used to identify interfacial line defect array configurations which relax the misorientation and/or misfit of a coherent crystalline interface. These line defect arrays may be comprised of dislocations and/or disconnections, which are interfacial steps with dislocation character. When an interface contains disconnections, solution of the quantized Frank–Bilby equation is complicated by the fact that the habit plane orientation is not known in advance because it depends on the unknown spacing of the disconnection array. We present a root-finding-based method for addressing this issue, enabling a self-consistent solution for arbitrary defect content. Our method has been implemented in an open-source code which enumerates all possible solutions given a list of candidate line defects. Two cases are presented employing the code: a misoriented FCC twin boundary and an FCC/BCC phase boundary with the Nishiyama-Wasserman orientation relationship. Both cases exhibit more than 10,000 solutions to the Frank–Bilby equation, with several hundred solutions categorized as “low energy” and thus plausible configurations for the actual interface. The resulting set of solutions can be utilized to predict and understand the properties of a given interface.

Optimization Effectiveness of Multi-Intelligence Consistent Energy System Based on Digital Advertising and Data Analysis

This paper introduces a novel approach to address the issue of overestimation of state-action values in reinforcement learning algorithms based on the Q-framework. It integrates a dual-layer Q-learning algorithm with a grey wolf intelligent optimization method, enabling rapid search for optimal allocations in unknown search spaces. This integration results in the development of a multi-agent collaborative Area-Grid Coordination (A-GC) strategy, termed the grey wolf double Q (GWDQ) strategy, tailored for multi-area energy interconnection scenarios. The proposed GWDQ strategy is evaluated through simulation experiments on comprehensive energy system models, including mixed gas turbine systems, Combined Cooling, Heating, and Power (CCHP) systems, and a multi-area energy-interconnected Northeast power grid model. A centralized architecture is established to analyze the optimization effects of digital advertising. The performance of the GWDQ strategy is compared with traditional reinforcement learning algorithms through simulation and empirical data validation. Results indicate that the GWDQ strategy exhibits stronger learning capabilities, improved stability, and enhanced control performance compared to traditional methods. It demonstrates superior optimization for digital advertising and enables swift acquisition of optimal coordination in A-GC processes across multiple regions. Additionally, the paper analyzes the environmental and economic impacts of the proposed strategy.

Local Evaluation of Large-scale Remote Sensing Machine Learning-generated Building and Road Dataset: The Case of Rwanda

AbstractAccurate and up-to-date building and road data are crucial for informed spatial planning. In developing regions in particular, major challenges arise due to the limited availability of these data, primarily as a result of the inherent inefficiency of traditional field-based surveys and manual data generation methods. Importantly, this limitation has prompted the exploration of alternative solutions, including the use of remote sensing machine learning-generated (RSML) datasets. Within the field of RSML datasets, a plethora of models have been proposed. However, these methods, evaluated in a research setting, may not translate perfectly to massive real-world applications, attributable to potential inaccuracies in unknown geographic spaces. The scepticism surrounding the usefulness of datasets generated by global models, owing to unguaranteed local accuracy, appears to be particularly concerning. As a consequence, rigorous evaluations of these datasets in local scenarios are essential for gaining insights into their usability. To address this concern, this study investigates the local accuracy of large RSML datasets. For this evaluation, we employed a dataset generated using models pre-trained on a variety of samples drawn from across the world and accessible from public repositories of open benchmark datasets. Subsequently, these models were fine-tuned with a limited set of local samples specific to Rwanda. In addition, the evaluation included Microsoft’s and Google’s global datasets. Using ResNet and Mask R‑CNN, we explored the performance variations of different building detection approaches: bottom-up, end-to-end, and their combination. For road extraction, we explored the approach of training multiple models on subsets representing different road types. Our testing dataset was carefully designed to be diverse, incorporating both easy and challenging scenes. It includes areas purposefully chosen for their high level of clutter, making it difficult to detect structures like buildings. This inclusion of complex scenarios alongside simpler ones allows us to thoroughly assess the robustness of DL-based detection models for handling diverse real-world conditions. In addition, buildings were evaluated using a polygon-wise comparison, while roads were assessed using network length-derived metrics.Our results showed a precision (P) of around 75% and a recall (R) of around 60% for the locally fine-tuned building model. This performance was achieved in three out of six testing sites and is considered the lowest limit needed for practical utility of RSML datasets, according to the literature. In contrast, comparable results were obtained in only one out of six sites for the Google and Microsoft datasets. Our locally fine-tuned road model achieved moderate success, meeting the minimum usability threshold in four out of six sites. In contrast, the Microsoft dataset performed well on all sites. In summary, our findings suggest improved performance in road extraction, relative to building extraction tasks. Moreover, we observed that a pipeline relying on a combination of bottom-up and top-down segmentation, while leveraging open global benchmark annotation dataset as well as a small number of samples for fine-tuning, can offer more accurate RSML datasets compared to an open global dataset. Our findings suggest that relying solely on aggregated accuracy metrics can be misleading. According to our evaluation, even city-level derived measures may not capture significant variations in performance within a city, such as lower accuracy in specific neighbourhoods. Overcoming the challenges of complex areas might benefit from exploring alternative approaches, including the integration of LiDAR data, UAV images, aerial images or using other network architectures.

Safe and Efficient Exploration Path Planning for Unmanned Aerial Vehicle in Forest Environments

This study presents an enhanced exploration path planning for unmanned aerial vehicles. The primary goal is to increase the chances of survival of missing people in forest environments. Exploration path planning is an essential methodology for exploring unknown three-dimensional spaces. However, previous studies have mainly focused on underground environments, not forest environments. The existing path planning methods for underground environments are not directly applicable to forest environments. The reason is that multiple open spaces exist with various obstacles, such as trees, foliage, undergrowth, and rocks. This study mainly focused on improving the safety and efficiency to be suitable for forests rather than underground environments. Paths closer to obstacles are penalized to enhance safety, encouraging exploration at a safer distance from obstacles. A potential field function is applied based on explored space to minimize overlapping between existing and new paths to increase efficiency. The proposed exploration path planning method was validated through an extensive simulation analysis and comparison with state-of-the-art sampling-based path planning. Finally, a flight experiment was conducted to verify further the feasibility of the proposed method using onboard real hardware implementation in a cluttered and complex forest environment.

Multi-objective optimization with Q-learning for cruise and power allocation control parameters of connected fuel cell hybrid vehicles

Fuel cell hybrid vehicles (FCHVs) are significant for achieving zero carbon emissions. Connected FCHVs can leverage traffic information to collaboratively optimize cruise and power allocation control, enhancing various performance aspects. For urban driving scenarios, this paper introduces a multi-strategy series control architecture for longitudinal cruise and power allocation control in connected FCHVs. However, particle swarm optimization (PSO) algorithms face challenges in high-dimensional decision and objective spaces when optimizing multiple strategies. Additionally, manually preset PSO parameters hinder particle evolution from dynamically adapting to unknown multi-objective spaces, thereby limiting the development of multiple performance metrics. To address this issue, this paper proposes a Q-learning multi-objective PSO (QMOPSO) algorithm. This algorithm tackles high-dimensional optimization challenges by improving population initialization distribution and subpopulation division, and enables particles to dynamically adjust exploration strategies, thereby maximizing multiple objective performances. The results indicate that compared to a control scheme optimized with PSO under predefined driving conditions, the multi-strategy series control framework optimized with the QMOPSO algorithm improves tracking stability by 50.20%, driving comfort by 1.77%, fuel economy by 6.10%, and reduces power source degradation by 2.04% in urban driving scenarios. Compared to PSO and multi-objective PSO algorithms, the QMOPSO algorithm demonstrates superior trade-offs. This research provides a collaborative optimization solution for FCHVs in connected environments.

State space reconstruction of Markov chains via autocorrelation structure

Understanding the state space of observed Markov processes is essential for advancing causal inference in a wide range of scientific fields. This paper demonstrates how the previously unknown state space can be reconstructed by exploring the spectrum of the time-delay embedding matrix derived from the autocorrelation sequence of the observed series. It also highlights that the eigenvector associated with the smallest eigenvalue can provide valuable insights into the hidden data generation process itself. The presented results provide a deeper understanding of the complex dynamics of Markov chains and hold promise for enhancing various scientific applications.

Adversarial compact wrapping classifier learning for open set recognition

Determining the limited and compact class acceptance region has become a key bottleneck in classifier design since Open Set Recognition (OSR) requires the classifier to successfully identify those of interest and reject other exceptions. The imperfect match between actual feature distribution of a class and its classification acceptance area is a critical barrier to performance enhancement in recent existing OSR algorithms. Therefore, we propose adversarial compact wrapping classifier learning for OSR, which achieves better feature representation and classification surface division through alternating learning of classifiers and compact wrapping points (CWPs). The core of the algorithm model lies in the confrontational game between CWP and the classifier. First, a new deep and compact hyperspherical crown classifier is designed, which expands neurons to achieve limited and compact classification surface division, improves the classification performance of known classes, and reduces the risk of open space. Then, the CWP algorithm is used to construct negative class sample points that tightly wrap the class feature distribution, and a joint optimization framework of CWP and classifier is constructed. The classifier improves the discrepancy between the class acceptance area of the classification and the actual class distribution area through adversarial learning with CWP, further controls the risk of unknown open spaces, and optimizes the learned feature distribution. It is worth noting that we have developed the CWP to allow concave feature distributions. Experimental results show that the proposed algorithm is significantly superior to recent comparison algorithms on various benchmark datasets and significantly improves OSR performance on the TinyImageNet dataset.

Adaptive Space Search-based Molecular Evolution Optimization Algorithm.

In drug development process, a significant portion of budget and research time are dedicated to the lead compound optimization procedure in order to identify potential drugs. This procedure focuses on enhancing the pharmacological and bioactive properties of compounds by optimizing their local substructures. However, due to the vast and discrete chemical structure space and the unpredictable element combinations within this space, the optimization process is inherently complex. Various structure enumeration-based combinatorial optimization methods have shown certain advantages. However, they still have limitations. Those methods fail to consider the differences between molecules and struggle to explore the unknown outer search space. In this study, we propose an adaptive space search-based molecular evolution optimization algorithm (ASSMOEA). It consists of three key modules: construction of molecule-specific search space, molecular evolutionary optimization, and adaptive expansion of molecule-specific search space. Specifically, we design a fragment similarity tree in molecule-specific search space, and apply a dynamic mutation strategy in this space to guide molecular optimization. Then we utilize an encoder-encoder structure to adaptively expand the space. Those three modules are circled iteratively to optimize molecules. Our experiments demonstrate that ASSMOEA outperforms existing methods in terms of molecular optimization. It not only enhances the efficiency of the molecular optimization process, but also exhibits a robust ability to search for correct solutions. The code is freely available on the web at https://github.com/bbbbb-b/MEOAFST. Supplementary data are available at Bioinformatics online.

Development of organic aggregation-induced emission fluorescent materials based on machine learning models and experimental validation

Organic fluorescent materials have attracted significant attention due to their unique chemical and optoelectronic properties. Unfortunately, traditional organic fluorescent materials are limited by aggregation-caused quenching (ACQ), which restricts their application potential in various fields. The discovery of aggregation-induced emission (AIE) materials has revolutionized the field by providing a solution to this issue. Despite considerable efforts to design and synthesize novel AIE materials, the development process still relies on tedious trial-and-error methods. Therefore, advanced cross-disciplinary techniques must be introduced to establish AIE property prediction models and enhance the development efficiency of organic AIE fluorescent materials. Machine learning (ML) is a powerful tool that has been widely applied across diverse fields to accelerate material development through complex relationship mapping of large data sets. This study presents an ML-based approach aimed at accelerating the development of organic AIE fluorescent materials. We assembled a dataset of 3,074 molecules with AIE/ACQ properties, developed a prediction model using the LightGBM ensemble learning algorithm, and used combined molecular fingerprints as input. The model achieved a remarkable independent test-set accuracy of up to 0.974 and its extrapolation ability was validated by out-of-sample validation (accuracy = 0.963). In addition, experimental validation was performed to assess the reliability of the ML prediction model in an unknown molecular space, showing a satisfactory agreement between the model predictions and the experimental results. Our work highlights the potential of ML for rapidly and accurately predicting the AIE property of unknown molecules and screening AIE materials, thereby accelerating the development of organic AIE fluorescent materials.

Enhanced adaptive-convergence in Harris’ hawks optimization algorithm

This paper presents a novel enhanced adaptive-convergence in Harris’ hawks optimization algorithm (EAHHO). In EAHHO, considering that Harris’ hawks will adopt different perching strategies and chasing styles according to the value of the escaping energy parameter E, nonlinear adaptive-convergence factor a is designed and adjusted to enhance the convergence and robustness of the algorithm. Moreover, the convergence and stability of EAHHO are proved mathematically by using the Markov chain theory and Lyapunov stability theory respectively. Moreover, numerical simulation results of 14 HHOs with different nonlinear convergence factors on 23 benchmark functions show that the nonlinear convergence factor of EAHHO is applicable to challenging problems with unknown search spaces, and the comparisons with the selected well-established algorithms on 56 test functions demonstrate that EAHHO performs competitively and effectively. Finally, the experiment results show that EAHHO algorithm also has a good performance to solve the optimization problems with relatively high dimensions and graph size of Internet of Vehicles routing problem.

Spatial Resolution Requirements for FDTD Modeling of Geoelectric Fields

AbstractTo ensure the robustness of both civilian and military infrastructure, it is important to protect electric power grids, smart grids, and other electrotechnologies from known and possibly as‐of‐yet unknown space weather hazards. Space weather can generate intense geoelectric fields at the surface of the Earth, as well as large voltage gradients across long distances of the Earth. These voltage gradients can lead to geomagnetically induced currents (GICs), which are known to produce hazards to electric power grids. The finite‐difference time‐domain (FDTD) method is a powerful and versatile method that has already been applied to the study of geoelectric fields. The advantages of FDTD over other methods are that it can account for more geometrical complexities and realistic time waveforms and that it directly solves for geoelectric fields. Snell's Law predicts that any electromagnetic waves incident on the ground should essentially propagate straight downwards into the low resistivity ground. For this reason, vertical FDTD grid resolutions of 1/3 of a skin depth were usually chosen, while the horizontal grid resolution was relaxed. We find, however, that there is another important consideration for choosing an FDTD grid resolution applied to real‐world scenarios: localized field variations due to currents generated by ground features. It turns out the grid resolution requirements are much stricter when taking this physics into account.

Robotics for Heritage Surveying: preliminary test on Leica BLK ARC & Spot® toward autonomous 3D mapping

Abstract. Today, robotics technologies are revolutionizing surveying and construction in AEC fields, enhancing precision, safety, and efficiency and also in the realm of Cultural Heritage knowledge and protection, the integration of cutting-edge technologies is reshaping consolidated surveying methods for documentation, especially in risk scenarios. One such innovation making waves is the use of mobile mapping systems, where automation and expediency are the determining factors in technological development, both in the direction of in indoor positioning (visual/LiDAR SLAM, UWB, etc.) and 3D mapping of known and unknown spaces. The BLK ARC by Leica, equipping the Agile Mobile Robot Spot® by Boston Dynamics, as a dynamic sensing platform, is here presented and discussed. BLK ARC is part of a diverse landscape of mobile mapping systems, each offering unique features and specifications tailored to different surveying needs, from handheld devices and wearable systems, to vehicle-mounted systems, the options vary in dimension, weight, price, and technical capabilities and the recent companies and research are largely focusing on them. A preliminary evaluation takes into consideration the dual dynamic-static 3D data-types, considering local and global accuracy of the 3D data delivered from the first experimental tests in a case study. Different metrics have been considered including acquisition time, precision, resolution, density, accuracy, roughness, and completeness of the acquired data.

Fast 3D Reconstruction for Unknown Space Targets via Neural Radiance Field

In this study, a novel residual space object 3D model reconstruction approach is proposed. First, several low-cost CubeSats are assigned to carry out multi-view image capture in the vicinity of the space target, yielding a series of images with relative pose information. Then, a fast 3D reconstruction approach based on NeRF is introduced, in which real-time performance and reconstruction quality are improved for fast on-board 3D reconstruction. Finally, a simulation test is designed to verify the performance of the proposed method, results show that the proposed NeRF-based method is superior to traditional method in terms of reconstruction speed and texture richness.

Retraining surrogate models in increasingly restricted design spaces: a novel building energy model calibration method

Surrogate (i.e. meta) models can approximate building energy models (BEMs) accurately and quickly, hence they have been widely used in BEM calibration studies. Typically, the surrogate models are trained a single time over the entire unknown building parameter space with a design such as Latin hypercube sampling. In this article, a multiple polynomial regression surrogate model is, instead, retrained with increasingly restricted designs. In each training repetition, the bounds of the design narrow around the unknown building parameter values that minimize the error between the surrogate model’s predictions and the measured energy. This ‘cascading surrogate’ calibration method finds CVRMSE values that are much lower than those of a powerful black box optimizer in a case study with simulated ‘measured’ data. However, the method has similar performance to the black box optimizer in a case study with real hourly measured energy, probably since the BEM was not configured accurately enough.

Enhancing reinforcement learning based adversarial malware generation to evade static detection

The anti-detection capabilities of adversarial malware examples have drawn the attention of antivirus vendors and researchers. In black-box scenarios where internal information of the target model cannot be accessed, with the ability of the reinforcement learning (RL) agents to adjust strategies based on the feedback from the environment, existing RL-based methods enable evasion of Windows PE malware detectors. However, obtaining evasion rewards as positive feedback in the black-box setting proves challenging, resulting in low training efficiency. To address this issue, we introduce the intrinsic curiosity reward into the framework to motivate the agent to explore unknown state spaces and learn effective evasion strategies. Additionally, we employ the generative adversarial network (GAN) to obtain varying synthetic data, which replaces random or benign bytes as adversarial payloads for the agent's action content, improving attack capabilities and reducing the risk of hard-coded adversarial perturbations being anchored. We compare the attack performance with other RL-based baseline methods, and experimental results show that our framework is more flexible and effective, achieving a 63%-85% attack success rate against EMBER, FireEye and MalConv. Even if defensive measures are taken, the proposed method still has a certain attack capability, and the success rate remains between 48%-67%.

A Novel Hybrid Binary Bat Algorithm for Global Optimization

In this article, a novel hybrid binary bat algorithm named HBBA is proposed for global optimization problems. First, to avoid simultaneous updating of bat velocity's dimensional components, i.e., elements of velocity vector, a random black hole model is modified to adapt to binary algorithm for updating in unknown spaces for each dimensional component individually. Through this way, the search ability of bats around the current group best is increased greatly. Second, a time-varying v-shaped transfer function, rather than a time-invariant one as in closely related works, is proposed to map velocity in continuous search space to a binary one. This accelerates the speed to switch individuals' positions, i.e., solutions in binary space. Third, a chaotic map is utilized to replace monotonous parameters in original binary bat algorithm, which is beneficial for avoiding premature convergence. Simulation results demonstrate the effectiveness of the proposed algorithm by three types of benchmark functions and unit commitment problem.

“Do You Know Where the Dragons Are?” - Introducing a Novel Enactive Framework to Map the Wide Unknown of Social Spaces With Neurodivergent Young People

Experiences of being are fundamentally different for neurodivergent young people. Differences in brain connectivity and function conferred by neurodivergence give rise to differences in construal processes underlying intersubjective engagement and shared meaning. Normative therapeutic and educational interventions refer to ‘social communication disorders’, ‘cognitive inflexibility’ and ‘social anxiety’. Peers call them ‘weird’. Parents struggle with guilt, exasperation and despair. The world demands social conformity. “Toby” (11yrs) requested a map and sword to slay the fearsome dragons lurking in the unknown spaces between himself and others- between here and there; intention and action; words and meaning; friend and foe. This paper describes a contextualizing model in a novel systematized approach to meeting the social engagement challenges of neurodivergent young people, devised in collaboration with those young people as Experts by Experience. The approach was formulated on the principle that social competence in neurodivergence should be shaped around self- discovery of authentic modes of being and relating, rather than imposed normative criteria. The 12 Social Spaces model integrates theoretical and clinical principles of Personal Construct Psychology, Enactivism, play theory, neurocognition and social competence, to provide a contextualizing framework of twelve interactional spaces to scaffold social cognition during evolving play between neurodivergent young people.