Virtual Target Research Articles

Steady-State Visual Evoked Potential-Based Brain-Computer Interface System for Enhanced Human Activity Monitoring and Assessment.

Advances in brain-computer interfaces (BCIs) have enabled direct and functional connections between human brains and computing systems. Recent developments in artificial intelligence have also significantly improved the ability to detect brain activity patterns. In particular, using steady-state visual evoked potentials (SSVEPs) in BCIs has enabled noticeable advances in human activity monitoring and identification. However, the lack of publicly available electroencephalogram (EEG) datasets has limited the development of SSVEP-based BCI systems (SSVEP-BCIs) for human activity monitoring and assisted living. This study aims to provide an open-access multicategory EEG dataset created under the SSVEP-BCI paradigm, with participants performing forward, backward, left, and right movements to simulate directional control commands in a virtual environment developed in Unity. The purpose of these actions is to explore how the brain responds to visual stimuli of control commands. An SSVEP-BCI system is proposed to enable hands-free control of a virtual target in the virtual environment allowing participants to maneuver the virtual target using only their brain activity. This work demonstrates the feasibility of using SSVEP-BCIs in human activity monitoring and assessment. The preliminary experiment results indicate the effectiveness of the developed system with high accuracy, successfully classifying 89.88% of brainwave activity.

A high-effective swarm intelligence-based multi-robot cooperation method for target searching in unknown hazardous environments

To solve target searching problems for multi-robot cooperation with inaccurate target distance perception in unknown hazardous environments, a hybrid adaptive robotic particle swarm optimizer (RPSO) and grey wolf optimizer (GWO) based algorithm with continuous virtual target guidance is proposed for high effective path planning in the searching. In the initial searching stages, both the wolf behavior-generated position and the gbest position and the pbesti positions from RPSO are employed to guide the motions of robots. With the information provided by these initial robot movement paths, a geometric model is established to generate potential targets. The K-means cluster algorithm is introduced to estimate a virtual target position online from potential targets, with new robot-presenting route information to update the history path information. Then the virtual position is employed as one of the direction components to help the robots approach the actual target. In addition, to avoid mobile robots falling into local convergence, a heuristic moving direction determination scheme is utilized to make robots circumvent obstacles in swarm motions, as well as a mutual repulsion algorithm to keep them in a scattering state. Simulation experiments on different types of unknown environments with varied robot numbers and adaptability testing for a dynamic target are carried out to verify the feasibility of the proposed target searching method with comparisons to the other three famous target searching algorithms. It is verified from the results that the presented method can not only contribute a 100% success rate in all runs of searching for a stochastic dynamic target under a limited maximal velocity, but also produce both the shortest path length and minimum iterations in terms of statistical metrics over the comparative methods.

A mechanistic systems biology model of brain microvascular endothelial cell signaling reveals dynamic pathway-based therapeutic targets for brain ischemia

Ischemic stroke is a significant threat to human health. Currently, there is a lack of effective treatments for stroke, and progress in new neuron-centered drug target development is relatively slow. On the other hand, studies have demonstrated that brain microvascular endothelial cells (BMECs) are crucial components of the neurovascular unit and play pivotal roles in ischemic stroke progression. To better understand the complex multifaceted roles of BMECs in the regulation of ischemic stroke pathophysiology and facilitate BMEC-based drug target discovery, we utilized a transcriptomics-informed systems biology modeling approach and constructed a mechanism-based computational multipathway model to systematically investigate BMEC function and its modulatory potential. Extensive multilevel data regarding complex BMEC pathway signal transduction and biomarker expression under various pathophysiological conditions were used for quantitative model calibration and validation, and we generated dynamic BMEC phenotype maps in response to various stroke-related stimuli to identify potential determinants of BMEC fate under stress conditions. Through high-throughput model sensitivity analyses and virtual target perturbations in model-based single cells, our model predicted that targeting succinate could effectively reverse the detrimental cell phenotype of BMECs under oxygen and glucose deprivation/reoxygenation, a condition that mimics stroke pathogenesis, and we experimentally validated the utility of this new target in terms of regulating inflammatory factor production, free radical generation and tight junction protection in vitro and in vivo. Our work is the first that complementarily couples transcriptomic analysis with mechanistic systems-level pathway modeling in the study of BMEC function and endothelium-based therapeutic targets in ischemic stroke.

Cooperative Containment Strategies to Pursue One Faster Evader with Collision Avoidance

On the surface of complex sea areas with obstacles, the encirclement mission against one hostile unmanned surface vehicle (USV) with high capability is usually extremely challenging. Especially when the evasion USV is endowed with a higher speed, the pursuit USVs are more likely to fail. In this work, the containment strategies by multiple low-speed pursuit USVs to capture one high-speed evasion USV considering collision avoidance are studied. On the basis of the conventional artificial potential field (APF) method, we first design a virtual target point to replace the actual evasion USV, which will help the pursuit USVs better implement the surrounding behavior. Thereafter, we reset the triggering conditions of the APF method to avoid the unnecessary obstacle avoidance actions. The proposed strategies can help the pursuit USVs better accomplish the containment strategies while bypassing the obstacles. Simulation experiments have verified the effectiveness of the designed cooperative containment strategies.

Path planning of mobile robot based on improved PRM and APF

In the field of mobile robot path planning, the artificial potential field (APF) method has been widely researched and applied due to its intuitiveness and efficiency. However, the APF algorithm often encounters challenges such as local minima and unreachable goals in complex environments. To address these issues, this paper proposes innovative path planning algorithm that integrates the advantages of the probabilistic roadmaps method (PRM), by introducing Sobol sampling and elliptical constraints to enhance PRM. The improved PRM not only reduces redundant nodes but also enhances the quality of sampling points. Furthermore, this paper uses the path nodes from the improved PRM as virtual target points for the APF algorithm, and effectively solves the inherent flaws of the APF algorithm through the segmented processing of the attractive force function and the introduction of a relative distance factor in the repulsive force function. Simulation results show that the algorithm reduces planning time, node count, and path length, demonstrate significant improvements in efficiency and performance. In addition, experiments with omnidirectional mobile robots further confirm the effectiveness and reliability of the algorithm in practical applications.

ARISE-The Accuracy Evaluation of a Patient-Specific 3D-Printed Biopsy System Based on MRI Data: A Cadaveric Study.

Brain biopsy is required for the accurate specification and further diagnosis of intracranial findings. The conventional stereotactic frames are used clinically for biopsies and offer the highest possible precision. Unfortunately, they come with some insurmountable technical and logistical limitations. The aim of the present work is to determine the clinical precision in the needle biopsy of the human brain using a new patient-specific stereotactic navigation device based on 3D printing. MRI data sets of human cadaver heads were used to plan 32 intracranial virtual biopsy targets located in different brain regions. Based on these data, 16 individualized stereotactic frames were 3D-printed. After the intraoperative application of the stereotactic device to the cadaver's head, the actual needle position was verified by postoperative CT. Thirty-two brain areas were successfully biopsied. The target point accuracy was 1.05 ± 0.63 mm, which represents the difference between the planned and real target points. The largest target point deviation was in the coronal plane at 0.60 mm; the smallest was in the transverse plane (0.45 mm). Three-dimensional-printed, personalized stereotactic frames or platforms are an alternative to the commonly used frame-based and frameless stereotactic systems. They are particularly advantageous in terms of accuracy, reduced medical imaging, and significantly simplified intraoperative handling.

A Study of Virtual Water Trade among G20 Countries from a Value-Added Trade Perspective

Abstract: From a value-added trade perspective, this study utilizes the world input–output tables and the water footprint data of each industry in each country in the Eora database to explore the virtual water resources of 19 countries (the G20 countries excluding the European Union) in 2016. We calculated nine value chain decompositions and the pattern of virtual water flows and then explored the implied virtual water use due to the trade of intermediate goods and final goods, and we also analyzed the unequal trade of virtual water and added value among countries. The results indicate the following. Firstly, in most countries, the largest portion of virtual water is attributed to exports of intermediate inputs that are produced in the source country and fully utilized by the direct import countries, followed by the foreign value-added component of intermediate goods, while the smallest share of virtual water is returned to the country. Secondly, in value-added trade, excluding the rest of the world (ROW), China, France, Italy, Japan, Mexico, South Korea, South Africa, Saudi Arabia, and Germany are net importers in the virtual water trade between G20 countries, and the USA is the largest net exporter of virtual water. Thirdly, intermediate product trade is the dominant form of implied virtual water trade among countries, which leads to a net flow ratio of implied virtual water of about 80% to 90%. Lastly, the Virtual Water Inequality Index shows that thirteen country combinations, including Brazil and Argentina, exhibit significant inequality, and most countries are in a relatively equal state. In addition, the virtual water and added value of the relatively economically developed regions benefit more from the virtual water trade. Therefore, it is crucial for countries to reduce their consumption of virtual water when trading intermediate products to develop high-value-added and low-water-consumption industries and to choose appropriate virtual water trade targets.

Decoding the dancing of the tongue: A model-based learning approach to phonetic targets in coarticulationa).

A model synthesizing average frequency components from select sentences in an electromagnetic articulography database has been crafted. This revealed the dual roles of the tongue: its dorsum acts like a carrier wave, and the tip acts as a modulation signal within the articulatory realm. This model illuminates anticipatory coarticulation's subtleties during speech planning. It undergoes rigorous, two-stage optimization: statistical estimation and refinement to depict carryover and anticipation. The model's base, rooted in physiological insights, deciphers carryover targets while its upper layer captures anticipation. Optimization has pinpointed unique phonetic targets for each phoneme, providing deep insights into virtual target formation during speech planning. These simulations, aligning closely with empirical data and marked by a mere 0.18 cm average error, along with extensive listening tests attest to the model's accuracy and enhanced speech synthesis quality.

Using augmented reality to assess spatial neglect: The Free Exploration Test (FET).

To capture the distortion of exploratory activity typical of patients with spatial neglect, traditional diagnostic methods and new virtual reality applications use confined workspaces that limit patients' exploration behavior to a predefined area. Our aim was to overcome these limitations and enable the recording of patients' biased activity in real, unconfined space. We developed the Free Exploration Test (FET) based on augmented reality technology. Using a live stream via the back camera on a tablet, patients search for a (non-existent) virtual target in their environment, while their exploration movements are recorded for 30 s. We tested 20 neglect patients and 20 healthy participants and compared the performance of the FET with traditional neglect tests. In contrast to controls, neglect patients exhibited a significant rightward bias in exploratory movements. The FET had a high discriminative power (area under the curve = 0.89) and correlated positively with traditional tests of spatial neglect (Letter Cancellation, Bells Test, Copying Task, Line Bisection). An optimal cut-off point of the averaged bias of exploratory activity was at 9.0° on the right; it distinguished neglect patients from controls with 85% sensitivity. FET offers time-efficient (execution time: ∼3 min), easy-to-apply, and gamified assessment of free exploratory activity. It supplements traditional neglect tests, providing unrestricted recording of exploration in the real, unconfined space surrounding the patient.

A virtual target controls fixation better than a remembered target

A virtual target controls fixation better than a remembered target

Altered Movement Coordination during Functional Reach Tasks in Patients with Chronic Low Back Pain and Its Relationship to Numerical Pain Rating Scores.

Identifying the effects of pain catastrophizing on movement patterns in people with chronic low back pain (CLBP) has important clinical implications for treatment approaches. Prior research has shown people with CLBP have decreased lumbar-hip ratios during trunk flexion movements, indicating a decrease in the contribution of lumbar flexion relative to hip flexion during trunk flexion. In this study, we aim to explore the relationship between pain catastrophizing and movement patterns during trunk flexion in a CLBP population. Participants with CLBP (N = 98, male = 59, age = 39.1 ± 13.0) completed a virtual reality standardized reaching task that necessitated a progressively larger amount of trunk flexion. Specifically, participants reached for four virtual targets to elicit 15°, 30°, 45°, and 60° trunk flexion in the mid-sagittal plane. Lumbar flexion was derived from the motion data. Self-report measures of numerical pain ratings, kinesiophobia, and pain catastrophizing were obtained. Pain catastrophizing leads to decreased lumbar flexion angles during forward reaching. This effect is greater in females than males.

Fish couple forecasting with feedback control to chase and capture moving prey.

Predator-prey interactions are fundamental to ecological and evolutionary dynamics. Yet, predicting the outcome of such interactions-whether predators intercept prey or fail to do so-remains a challenge. An emerging hypothesis holds that interception trajectories of diverse predator species can be described by simple feedback control laws that map sensory inputs to motor outputs. This form of feedback control is widely used in engineered systems but suffers from degraded performance in the presence of processing delays such as those found in biological brains. We tested whether delay-uncompensated feedback control could explain predator pursuit manoeuvres using a novel experimental system to present hunting fish with virtual targets that manoeuvred in ways that push the limits of this type of control. We found that predator behaviour cannot be explained by delay-uncompensated feedback control, but is instead consistent with a pursuit algorithm that combines short-term forecasting of self-motion and prey motion with feedback control. This model predicts both predator interception trajectories and whether predators capture or fail to capture prey on a trial-by-trial basis. Our results demonstrate how animals can combine short-term forecasting with feedback control to generate robust flexible behaviours in the face of significant processing delays.

Test Method for Single Satellite’s Inter-Satellite Link Pointing and Tracking via Ground Station

An inter-satellite link is a key technology that improves control accuracy, transmission efficiency, and autonomous capability of constellations. A satellite’s pointing and tracking abilities mainly determine the inter-satellite link’s performance, which should be validated through an in-orbit test. However, during the construction of the constellation, the distribution of satellites does not satisfy the constraints of establishing the inter-satellite link. A test method for inter-satellite link pointing and tracking is developed with respect to a single satellite. A practical mission scenario for testing inter-satellite links’ performance is constructed. A virtual satellite is introduced as the target satellite to establish an inter-satellite link with the local satellite. The orbit of the virtual target satellite between two ground stations is characterized based on the Newton–Raphson method. By comparing the predicted and actual time differences between two ground stations receiving the signals from the local satellite, the inter-satellite link pointing and tracking abilities are evaluated independently. Numerical simulations verify the design of the virtual satellite. The single satellite test method for inter-satellite link pointing and tracking abilities is available.

AR/VR assisted integrated framework of autonomous disassembly system for industrial products

An immersive Augmented Reality (AR) technology enhances productivity and flexibility in manufacturing by providing real-time instructions within workers’ field of view, aiding in complex tasks. According to cognitive load and attention-based theories, AR guidance can reduce cognitive strain, enhancing task performance. The affordance of AR in industrial processes like assembly or disassembly for repair/maintenance has been exploited broadly. However, the prevailing studies on automated user interfaces for AR instruction development, specifically virtual scene creation and target generation for object mapping rendering, still need to be explored. Notably, production industries currently rely on outsourcing skilled professionals to create AR overlaying instructions using manufacturer input. However, the exactness of the disassembly repository is substantial to avoid defects in AR guidance particulars. This underscores the significance of generalized automated AR instruction development and emphasizes the demands for further research. In this work, the autonomous AR-integrated framework is developed for the disassembly context, empowering manufacturers to acquire desired outcomes across various modules. These modules include the validation of the manual user disassembly sequence repository during the prefatory phase of AR instruction creation and the provision of Disassembly Sequence Planning (DSP) input. Additionally, the framework supports the simulation/rendering of disassembly efforts in a virtual platform and the generation of rendering virtual display scenes for appropriate AR-guided object mapping. Furthermore, it enables the generation of essential model targets for registering the AR scenes. Subsequently, the extracted repositories (virtual scenes and model targets) are input into the Unity AR tool to create AR mapping guidance for disassembly operations. The performance of the autonomous disassembly module is evaluated through three case studies with different AR visualizations. Furthermore, the potential of the proposed framework is exploited by extracting feedback from experts regarding flexibility, compatibility, completion time, and cognitive level.

Conformational Space Profiling Enhances Generic Molecular Representation for AI-Powered Ligand-Based Drug Discovery.

The molecular representation model is a neural network that converts molecular representations (SMILES, Graph) into feature vectors, and is an essential module applied across a wide range of artificial intelligence-driven drug discovery scenarios. However, current molecular representation models rarely consider the three-dimensional conformational space of molecules, losing sight of the dynamic nature of small molecules as well as the essence of molecular conformational space that covers the heterogeneity of molecule properties, such as the multi-target mechanism of action, recognition of different biomolecules, dynamics in cytoplasm and membrane. In this study, a new model named GeminiMol is proposed to incorporate conformational space profiles into molecular representation learning, which extracts the feature of capturing the complicated interplay between the molecular structure and the conformational space. Although GeminiMol is pre-trained on a relatively small-scale molecular dataset (39290 molecules), it shows balanced and superior performance not only on 67 molecular properties predictions but also on 73 cellular activity predictions and 171 zero-shot tasks (including virtual screening and target identification). By capturing the molecular conformational space profile, the strategy paves the way for rapid exploration of chemical space and facilitates changing paradigms for drug design.

High-Precision Calibration Method and Error Analysis of Infrared Binocular Target Ranging Systems

Infrared binocular cameras, leveraging their distinct thermal imaging capabilities, are well-suited for visual measurement and 3D reconstruction in challenging environments. The precision of camera calibration is essential for leveraging the full potential of these infrared cameras. To overcome the limitations of traditional calibration techniques, a novel method for calibrating infrared binocular cameras is introduced. By creating a virtual target plane that closely mimics the geometry of the real target plane, the method refines the feature point coordinates, leading to enhanced precision in infrared camera calibration. The virtual target plane is obtained by inverse projecting the centers of the imaging ellipses, which are estimated at sub-pixel edge, into three-dimensional space, and then optimized using the RANSAC least squares method. Subsequently, the imaging ellipses are inversely projected onto the virtual target plane, where its centers are identified. The corresponding world coordinates of the feature points are then refined through a linear optimization process. These coordinates are reprojected onto the imaging plane, yielding optimized pixel feature points. The calibration procedure is iteratively performed to determine the ultimate set of calibration parameters. The method has been validated through experiments, demonstrating an average reprojection error of less than 0.02 pixels and a significant 24.5% improvement in calibration accuracy over traditional methods. Furthermore, a comprehensive analysis has been conducted to identify the primary sources of calibration error. Ultimately, this achieves an error rate of less than 5% in infrared stereo ranging within a 55-m range.

The path planning algorithm for UUV based on the fusion of grid obstacles of artificial potential field

The artificial potential field approach (APFA) has the advantages of short computation time and easy implementation, widely used in path planning. The problems of local oscillation and susceptibility to local minima for APFA are considered in three dimensions (3D) path planning application of unmanned underwater vehicle (UUV), and the artificial potential field grid method (APFG) is proposed. The core principles of APFG include three key methods: firstly, obstacles are rasterized in a 3D map, and repulsive points are attached to the grid surface to alter the range of repulsive influence, thereby compensating for the issue of longer planned paths caused by the idealized modeling in traditional APFA; secondly, the repulsion function of the grid surface is modified to ensure that the potential field is parallel to the grid surface, thus addressing the problem of local oscillation in UUV path planning. Additionally, the introduction of virtual target points helps prevent the UUV from getting trapped in local optima, effectively overcoming the drawbacks of local oscillation and convergence to local minima in 3D path planning for UUV. The simulations show that APFG effectively overcomes the shortcomings of local oscillations and the tendency to fall into local minima for UUV in 3D path planning, and the path planning is smooth and efficient.

End-to-end deep reinforcement learning and control with multimodal perception for planetary robotic dual peg-in-hole assembly

The planetary construction is necessary for long-term scientific deep space exploration and resource utilization in the future. The planetary robotic assembly control is a key technology that must be broken through in future planetary surface construction. The paper focuses on the most representative dual peg-in–hole assembly, which has sufficiently complex contact interaction, wide range of applications and good method portability. To address the challenges brought by the unstructured planetary environment and the features of the construction tasks, the paper proposes an end-to-end deep reinforcement learning and control method with multimodal perception for planetary robotic assembly tasks. A staged reward function based on the visual virtual target point for policy learning is designed. The effectiveness and feasibility of the proposed control method have been verified through simulation experiments and ground real robot experiments. It provides a feasible control method of robotic operations for future planetary surface construction.

A Pilot Study to Evaluate the Relationships between Supine Proprioception Assessments and Upright Functional Mobility.

Long-duration bedrest impairs upright postural and locomotor control, prompting the need for assessment tools to predict the effects of deconditioning on post-bedrest outcome measures. We developed a tilt board mounted vertically with a horizontal air-bearing sled as a potential supine assessment tool for a future bedrest study. The purpose of this pilot study was to examine the association between supine proprioceptive assessments on the tilt board and upright functional mobility. Seventeen healthy participants completed variations of a supine tilt board task and an upright functional mobility task (FMT), which is an established obstacle avoidance course. During the supine tasks, participants lay on the air-bearing sled with axial loading toward the tilt board. Participants tilted the board to capture virtual targets on an overhead monitor during 30 s trials. The tasks included two dynamic tasks (i.e., double-leg stance matching mediolateral tilt targets over ±3° or ±9° ranges) and two static tasks (i.e., single-leg stance maintaining a central target position). The performances during the dynamic tasks were significantly correlated with the FMT time to completion. The dominant-leg static task performance showed a moderate trend with the FMT time to completion. The results indicate that supine proprioceptive assessments may be associated with upright ambulation performance, and thus, support the proposed application in bedrest studies.

Docking Score ML: Target-Specific Machine Learning Models Improving Docking-Based Virtual Screening in 155 Targets.

In drug discovery, molecular docking methods face challenges in accurately predicting energy. Scoring functions used in molecular docking often fail to simulate complex protein-ligand interactions fully and accurately leading to biases and inaccuracies in virtual screening and target predictions. We introduce the "Docking Score ML", developed from an analysis of over 200,000 docked complexes from 155 known targets for cancer treatments. The scoring functions used are founded on bioactivity data sourced from ChEMBL and have been fine-tuned using both supervised machine learning and deep learning techniques. We validated our approach extensively using multiple data sets such as validation of selectivity mechanism, the DUDE, DUD-AD, and LIT-PCBA data sets, and performed a multitarget analysis on drugs like sunitinib. To enhance prediction accuracy, feature fusion techniques were explored. By merging the capabilities of the Graph Convolutional Network (GCN) with multiple docking functions, our results indicated a clear superiority of our methodologies over conventional approaches. These advantages demonstrate that Docking Score ML is an efficient and accurate tool for virtual screening and reverse docking.