Exploration Tasks Research Articles

Comprehensive Learning in Mathematics: The Contribution of the Poly-Universe Tool

Learning with understanding is essential to enabling students to solve problems that they will inevitably face in the future. Students will be more successful, if they are engaged in thought-provoking activities that encourage them to grow in their mathematical understanding and identity. With this study we intended to understand how the application of exploratory tasks for teaching and learning mathematics in primary school, using the Poly-Universe tool, promotes a comprehensive learning of mathematics. We concluded that the activities involving the Poli-Universe, a tool to teach mathematics are motivating, as the students' interest and motivation during this study was notorious. The use of objects with such attractive characteristics, that appeal to visualisation, permitted the exploration of different figures and its characteristics and the comprehension of some geometric concepts. As well as positive interaction and collaboration of the students, the discussion and the development of their geometric reasoning were remarkable.

Exposure to Polystyrene Nanoplastics Led to Learning and Memory Deficits in Zebrafish by Inducing Oxidative Damage and Aggravating Brain Aging.

Nanoplastics (NPs) may pass through the blood-brain barrier, giving rise to serious concerns about their potential toxicity to the brain. In this study, the effects of NPs exposure on learning and memory, the primary cognitive functions of the brain, are assessed in zebrafish with classic T-maze exploration tasks. Additionally, to reveal potential affecting mechanisms, the impacts of NPs exposure on brain aging, oxidative damage, energy provision, and the cell cycle are evaluated. The results demonstrate that NP-exposed zebrafish takes significantly longer for their first entry and spends markedly less time in the reward zone in the T-maze task, indicating the occurrence of learning and memory deficits. Moreover, higher levels of aging markers (β-galactosidase and lipofuscin) are detected in the brains of NP-exposed fish. Along with the accumulation of reactive free radicals, NP-exposed zebrafish suffer significant levels of brain oxidative damage. Furthermore, lower levels of Adenosine triphosphate (ATP) and cyclin-dependent kinase 2 and higher levels of p53 are observed in the brains of NP-exposed zebrafish, suggesting that NPs exposure also results in a shortage of energy supply and an arrestment of the cell cycle. These findings suggest that NPs exposure may pose a severe threat to brain health, which deserves closer attention.

AUV Path Planning Considering Ocean Current Disturbance Based on Cloud Desktop Technology.

In the field of ocean energy detection, Autonomous Underwater Vehicles (AUVs) offer significant advantages in terms of manpower, resource, and energy efficiency. However, the unpredictable nature of the ocean environment, particularly the real-time changes in ocean currents, poses navigational risks for AUVs. Therefore, effective path planning in dynamic environments is crucial for AUVs to perform specific tasks. This paper addresses the static path planning problem and proposes a model called the noise net double DQN network with prioritized experience replay (N-DDQNP). The N-DDQNP model combines a noise network and a prioritized experience replay mechanism to address the limited exploration and slow convergence speed issues of the DQN algorithm, which are caused by the greedy strategy and uniform sampling mechanism. The proposed approach involves constructing a double DQN network with a priority experience replay and an exploration mechanism using the noise network. Second, a compound reward function is formulated to take into account ocean current, distance, and safety factors, ensuring prompt feedback during the training process. Regarding the ocean current, the reward function is designed based on the angle between the current direction and the AUV's heading direction, considering its impact on the AUV's speed. As for the distance factor, the reward is determined by the Euclidean distance between the current position and the target point. Furthermore, the safety factor considers whether the AUV may collide with obstacles. By incorporating these three factors, the compound reward function is established. To evaluate the performance of the N-DDQNP model, experiments were conducted using real ocean data in various complex ocean environments. The results demonstrate that the path planning time of the N-DDQNP model outperforms other algorithms in different ocean current scenarios and obstacle environments. Furthermore, a user console-AUV connection has been established using spice cloud desktop technology. The cloud desktop architecture enables intuitive observation of the AUV's navigation posture and the surrounding marine environment, facilitating safer and more efficient underwater exploration and marine resource detection tasks.

Deep Neural Network model for Underwater Image Enhancement

In recent years, there has been a growing interest in the field of underwater image enhancement, driven by its significance in underwater robotics and ocean engineering. Initially, research efforts focused on physics-based approaches, but with advancements in technology, the utilization of deep convolutional neural networks (CNNs) and generative adversarial networks (GANs) has become prevalent. These state-of-the-art algorithms have shown impressive results; however, their computational complexity and memory requirements pose challenges to their practical implementation on portable devices used for underwater exploration tasks. Furthermore, these models are often trained on either synthetic or limited real-world datasets, limiting their applicability in real-world scenarios. In this paper, we propose a novel deep neural network architecture that maintains high performance while reducing the number of parameters compared to existing state-of-the-art models. Our approach aims to address the computational and memory limitations associated with underwater image enhancement algorithms. By leveraging the strengths of our architecture, we demonstrate its generalization capability by evaluating its performance on a combination of synthetic and real-world datasets. This approach enhances the practicality and applicability of our model in real-world underwater scenarios.

Characteristics of Chaotic Weak Reflection in Carbonate Fracture-Cavity Reservoirs: A Case Study of Ordovician Reservoirs in Block 10 of the Tahe Oilfield, Tarim Basin, China.

The identification of carbonate reservoirs is a critical task in oil and gas exploration. Chaotic weak reflection is an important type of reflection characteristic in carbonate reservoirs. Widespread distribution of chaotic weak reflection has been observed in the Ordovician Yijianfang Formation in the Tahe area. However, there is still a lack of systematic research on the distribution, characteristics, reservoir space, and reservoir types of chaotic weak reflection in the Tahe area. To address this issue, this study conducted a comprehensive analysis of multiscale data and found that chaotic weak reflections are distributed within 100 ms below the top interface of the Middle Ordovician. Seismic profiles exhibit a "string of pearls" with a "tail" or "pearl-like" widening feature. On well logs, all three porosity curves exhibit localized peaks, with increases in acoustic (AC) and compensated neutron (CNL) and decreases in density (DEN), while the deep and shallow lateral resistivity (RD, RS) curves show positive amplitude differences. In the FMI, they are represented by dark sinusoidal curves and dark small patches. Based on these response characteristics, two types of reservoir spaces are identified for chaotic weak reflections: high-angle vertical fractures and caves with heights less than 7 m, which can form independent reservoirs or fracture-cavity complexes. Three types of reservoirs are distinguished: fracture, cavity, and cavity-fracture types.

Navigational and thematic exploration–exploitation trade-offs during web search: effects of prior domain knowledge, search contexts and strategies on search outcome

ABSTRACT While a clear distinction is established between simple lookup tasks and complex exploratory tasks, few studies have documented the differences between complex lookup tasks and simple exploratory tasks. To address this empirical gap, which has implications for system adaptability, the present study focused on these differences and the impact of users’ prior domain knowledge (low vs. high) on exploration-exploitation search strategies (thematic and navigational). The effects of prior domain knowledge on information search performance were also investigated, as well as the extent to which the navigational and thematic search strategies predicted users’ search scores and learning outcomes. Sixty students (35 in psychology, 25 in computer science) were asked to perform two complex lookup tasks and two simple exploratory tasks within and outside their knowledge domain. Results showed that when users performed tasks in their knowledge domain, they formulated more specific queries, achieved higher search scores, and produced higher quality factual knowledge. Complex lookup tasks led to more thematic exploitation and more navigational exploration than simple exploratory tasks. The search strategies predicted output performances differently, depending on the search domain (in vs. out). In light of these results, solutions helping users to improve their search strategies are given.

Reinforcement learning-based hybrid differential evolution for global optimization of interplanetary trajectory design

The global optimization and design of interplanetary trajectories is one of the most important tasks in deep space exploration. Its search space is often characterized by multi-constraints, extreme non-linearity and sensitivity to initial conditions. To cope with these difficulties, an improved reinforcement learning-based hybrid differential evolution (DE) named RL_HDE is proposed in this paper. In RL_HDE, a novel multi-mutation strategy LSHADE based on an adaptive Q-Learning framework is proposed for global exploration. To further balance the global exploration and local exploitation of RL_HDE, a new parameter adaptive strategy based on Q-Learning is designed to control two trigger parameters. The performance of RL_HDE is verified by the well-known Global Trajectory Optimization Problems (GTOP), which are developed by Advanced Concepts Team of European Space Agency (ESA-ACT). A comparison of RL_HDE with three sets of algorithms, including ten state-of-the-art hybrid evolutionary algorithms, four interplanetary trajectory design algorithms, and seven reinforcement learning-based hybridizations. Experimental statistical results demonstrate that RL_HDE outperforms other competitors in terms of convergence efficiency and accuracy. RL_HDE has better performance for solving complex interplanetary trajectory design problems such as Cassini2 and Messenger-full.

Modified bee colony optimization with opposition learning algorithm on use of medical data clustering

Clustering has gained popularity in the data mining field as one of the primary approaches for obtaining data distribution and data analysis. The medical data analysis for different diseases is a great challenge in current research. The benefits of opposition based learning such as faster convergence rate and better approximate result in finding global optimum can be helpful in this area. To achieve faster convergence and better clustering results for medical data, in this work, the authors have proposed an approach utilising modified bee colony optimization with opposition based learning and k-medoids technique. The initial centroid plays an important role in the bee colony optimization based clustering. The proposed approach uses k-medoids algorithm for this task. In order to facilitate faster convergence, it adds the opposite bees which are located at exactly the opposite location of the initial bees. The exploration task is performed by both of these kinds of bees to find potential solutions. This increases the algorithm’s capacity for exploration and, consequently, the rate of convergence. Five distinct medical datasets collected from the UCI library are investigated to demonstrate the algorithm’s efficacy. The implementation results demonstrate that the algorithm gives better convergence rate and clustering quality compared to some the existing algorithms.

A Precise Semantic Segmentation Model for Seabed Sediment Detection Using YOLO-C

Semantic segmentation methods have been successfully applied in seabed sediment detection. However, fast models like YOLO only produce rough segmentation boundaries (rectangles), while precise models like U-Net require too much time. In order to achieve fast and precise semantic segmentation results, this paper introduces a novel model called YOLO-C. It utilizes the full-resolution classification features of the semantic segmentation algorithm to generate more accurate regions of interest, enabling rapid separation of potential targets and achieving region-based partitioning and precise object boundaries. YOLO-C surpasses existing methods in terms of accuracy and detection scope. Compared to U-Net, it achieves an impressive 15.17% improvement in mean pixel accuracy (mPA). With a processing speed of 98 frames per second, YOLO-C meets the requirements of real-time detection and provides accurate size estimation through segmentation. Furthermore, it achieves a mean average precision (mAP) of 58.94% and a mean intersection over union (mIoU) of 70.36%, outperforming industry-standard algorithms such as YOLOX. Because of the good performance in both rapid processing and high precision, YOLO-C can be effectively utilized in real-time seabed exploration tasks.

Kinematic data profile and clinical outcomes in robotic inguinal hernia repairs: a pilot study.

Surgical training requires clinical knowledge and technical skills to operate safely and optimize clinical outcomes. Technical skills are hard to measure. The Intuitive Data Recorder (IDR), (Sunnyvale, CA) allows for the measurement of technical skills using objective performance indicators (OPIs) from kinematic event data. Our goal was to determine whether OPIs improve with surgeon experience and whether they are correlated with clinical outcomes for robotic inguinal hernia repair (RIHR). The IDR was used to record RIHRs from six surgeons. Data were obtained from 98 inguinal hernia repairs from February 2022 to February 2023. Patients were called on postoperative days 5-10 and asked to take the Carolina Comfort Scale (CCS) survey to evaluate acute clinical outcomes. A Pearson test was run to determine correlations between OPIs from the IDR with a surgeon's yearly RIHR experience and with CCS scores. Linear regression was then run for correlated OPIs. Multiple OPIs were correlated with surgeon experience. Specifically, for the task of peritoneal flap exploration, we found that 23 OPIs were significantly correlated with surgeons' 1-year RIHR case number. Total angular motion distance of the left arm instrument had a correlation of - 0.238 (95% CI - 0.417, - 0.042) for RIHR yearly case number. Total angular motion distance of right arm instrument was also negatively correlated with RIHR in 1year with a correlation of - 0.242 (95% CI - 0.420, - 0.046). For clinical outcomes, wrist articulation of the surgeon's console positively correlated with acute sensation scores from the CCS with a correlation of 0.453 (95% CI 0.013, 0.746). This study defines multiple OPIs that correlate with surgeon experience and with outcomes. Using this knowledge, surgical simulation platforms can be designed to teach patterns to surgical trainees that are associated with increased surgical experience and with improved postoperative outcomes.

RUDE-AL: Roped UGV Deployment Algorithm of an MCDPR for Sinkhole Exploration.

The presence of sinkholes has been widely studied due to their potential risk to infrastructure and to the lives of inhabitants and rescuers in urban disaster areas, which is generally addressed in geotechnics and geophysics. In recent years, robotics has gained importance for the inspection and assessment of areas of potential risk for sinkhole formation, as well as for environmental exploration and post-disaster assistance. From the mobile robotics approach, this paper proposes RUDE-AL (Roped UGV DEployment ALgorithm), a methodology for deploying a Mobile Cable-Driven Parallel Robot (MCDPR) composed of four mobile robots and a cable-driven parallel robot (CDPR) for sinkhole exploration tasks and assistance to potential trapped victims. The deployment of the fleet is organized with node-edge formation during the mission's first stage, positioning itself around the area of interest and acting as anchors for the subsequent release of the cable robot. One of the relevant issues considered in this work is the selection of target points for mobile robots (anchors) considering the constraints of a roped fleet, avoiding the collision of the cables with positive obstacles through a fitting function that maximizes the area covered of the zone to explore and minimizes the cost of the route distance performed by the fleet using genetic algorithms, generating feasible target routes for each mobile robot with a configurable balance between the parameters of the fitness function. The main results show a robust method whose adjustment function is affected by the number of positive obstacles near the area of interest and the shape characteristics of the sinkhole.

Velocity Control of a Multi-Motion Mode Spherical Probe Robot Based on Reinforcement Learning

As deep space exploration tasks become increasingly complex, the mobility and adaptability of traditional wheeled or tracked probe robots with high functional density are constrained in harsh, dangerous, or unknown environments. A practical solution to these challenges is designing a probe robot for preliminary exploration in unknown areas, which is characterized by robust adaptability, simple structure, light weight, and minimal volume. Compared to the traditional deep space probe robot, the spherical robot with a geometric, symmetrical structure shows better adaptability to the complex ground environment. Considering the uncertain detection environment, the spherical robot should brake rapidly after jumping to avoid reentering obstacles. Moreover, since it is equipped with optical modules for deep space exploration missions, the spherical robot must maintain motion stability during the rolling process to ensure the quality of photos and videos captured. However, due to the nonlinear coupling and parameter uncertainty of the spherical robot, it is tedious to adjust controller parameters. Moreover, the adaptability of controllers with fixed parameters is limited. This paper proposes an adaptive proportion–integration–differentiation (PID) control method based on reinforcement learning for the multi-motion mode spherical probe robot (MMSPR) with rolling and jumping. This method uses the soft actor–critic (SAC) algorithm to adjust the parameters of the PID controller and introduces a switching control strategy to reduce static error. As the simulation results show, this method can facilitate the MMSPR’s convergence within 0.02 s regarding motion stability. In addition, in terms of braking, it enables an MMSPR with random initial speed brake within a convergence time of 0.045 s and a displacement of 0.0013 m. Compared with the PID method with fixed parameters, the braking displacement of the MMSPR is reduced by about 38%, and the convergence time is reduced by about 20%, showing better universality and adaptability.

Studying health anxiety related attentional bias during online health information seeking: Impacts of stages and task types

Seeking online health information may reinforce the anxiety of those who are already overly anxious about their health. This study explored how people with health anxiety may behave differently in terms of their attentional biases when seeking health information online. We conducted an eye-tracking experiment with 17 participants in the high health anxious group and 17 participants in the low health anxious group, who performed three types of information-seeking tasks (factual, interpretive, and exploratory) on a Chinese health website. We observed that both groups mainly allocated their attention to the stages of evaluating the list of search results and synthesizing information to make health decisions. They showed similar attention tracks at the earlier search stages and health anxiety was found to associate with attentional biases towards certain website stimuli. However, the high health anxious group showed more active eye movements than their low health anxious counterparts. Attentional biases from the high health anxious group mainly occurred at the later stage of processing rather than the initial orientation stages. As for task types, the high health anxious group presented more extensive attentional biases when performing the interpretive task, compared to the explorative and factual tasks. The findings provide novel insights into the attentional biases of people with health anxiety as they search online for health information, which have implications on designing more effective information interventions for vulnerable groups of health information consumers. The findings can also help clinicians interpret patients’ anxiety-related sensations and provide intervening recommendations for clients in use of online health information.

Adolescents and family history: Memories, testimonies, narratives, and perspectives

Kouseri

A Wavelet Basis ANN and 5-Class Decision Factor AI Algorithm

The accuracy and reliability of continuous space curve estimation is the key to global exploration. An improved artificial intelligence algorithm is proposed for continuous space analysis. First, a wavelet basis ANN algorithm is proposed to determine the discretization strategy in continuous space. The hidden layer node transfer function in a BP neural network is replaced by a wavelet basis function, and the modified BP neural network is composed of a wavelet neural network. Second, an improved wolf algorithm is established. The core wolf system ensures the precision of the whole exploration task. Finally, main and auxiliary double cores and a five-class decision factor are used to establish a population classification model to solve the convergence of the algorithm.

Assessment of the hydrocarbon potential of the Crimean peninsula: state and prospects

The active development of the Crimean region requires the provision of an independent energy system.Currently, there are 36 hydrocarbon fields in Crimea: 12 with natural gas, 9 with oil, 4 with oil and gas, 2 with oil and gascondensate and 9 gascondensate fields (according to www.mygeomap.ru portal by February 10, 2023). Despite the fact that the vast majority of fields are small and some of them werealready under the development it is necessary to consider their potential and production possibility at least for selling products on the domestic market within Crimea peninsula.In the second half of the last century, extensive exploration work was almost completely stopped because small deposits in the drilled structures have been found. Most of the traps of the Mezocenozoic section are complicated bythe faults.The possibility of discovering unexplored yet hydrocarbon resources especially in the lower stratigraphic complexes can be solved due to modern technologies distribution in the area. The contribution of deeper Permian-Triassic and Jurassic complexesto of modern hydrocarbon accumulations formation is poore evaluated and need to be analyzed in more details. Proper setting of geological exploration tasks and conducting modern regional and local seismic surveys will significantly increase the possibility of discovering new hydrocarbon deposits.

EMExplorer: an episodic memory enhanced autonomous exploration strategy with Voronoi domain conversion and invalid action masking

Autonomous exploration is a critical technology to realize robotic intelligence as it allows unsupervised preparation for future tasks and facilitates flexible deployment. In this paper, a novel Deep Reinforcement Learning (DRL) based autonomous exploration strategy is proposed to efficiently reduce the unknown area of the workspace and provide accurate 2D map construction for mobile robots. Different from existing human-designed exploration techniques that usually make strong assumptions about the scenarios and the tasks, we utilize a model-free method to directly learn an exploration strategy through trial-and-error interactions with complex environments. To be specific, the Generalized Voronoi Diagram (GVD) is first utilized for domain conversion to obtain a high-dimensional Topological Environmental Representation (TER). Then, the Generalized Voronoi Networks (GVN) with spatial awareness and episodic memory is designed to learn autonomous exploration policies interactively online. For complete and efficient exploration, Invalid Action Masking (IAM) is employed to reshape the configuration space of exploration tasks to cope with the explosion of action space and observation space caused by the expansion of the exploration range. Furthermore, a well-designed reward function is leveraged to guide the learning of policies. Extensive baseline tests and comparative simulations show that our strategy outperforms the state-of-the-art strategies in terms of map quality and exploration speed. Sufficient ablation studies and mobile robot experiments demonstrate the effectiveness and superiority of our strategy.

Mixed-reality for quadruped-robotic guidance in SAR tasks

Abstract In recent years, exploration tasks in disaster environments, victim localization and primary assistance have been the main focuses of Search and Rescue (SAR) Robotics. Developing new technologies in Mixed Reality (M-R) and legged robotics has taken a big step in developing robust field applications in the Robotics field. This article presents MR-RAS (Mixed-Reality for Robotic Assistance), which aims to assist rescuers and protect their integrity when exploring post-disaster areas (against collapse, electrical, and toxic risks) by facilitating the robot’s gesture guidance and allowing them to manage interest visual information of the environment. Thus, ARTU-R (A1 Rescue Tasks UPM Robot) quadruped robot has been equipped with a sensory system (lidar, thermal, and RGB-D cameras) to validate this proof of concept. On the other hand, Human-Robot interaction is executed by using the Hololens glasses. This work’s main contribution is the implementation and evaluation of a Mixed-Reality system based on a ROS-Unity solution, capable of managing at a high level the guidance of a complex legged robot through different interest zones (defined by a Neural Network and a vision system) of a post-disaster environment (PDE). The robot’s main tasks at each point visited involve detecting victims through thermal, RGB imaging, and neural networks and assisting victims with medical equipment. Tests have been carried out in scenarios that recreate the conditions of PDE (debris, simulation of victims, etc.). An average efficiency improvement of 48% has been obtained when using the immersive interface and a time optimization of 21.4% compared to conventional interfaces. The proposed method has proven to improve rescuers’ immersive experience of controlling a complex robotic system.

Scalable Spatial Memory for Scene Rendering and Navigation

Neural scene representation and rendering methods have shown promise in learning the implicit form of scene structure without supervision. However, the implicit representation learned in most existing methods is non-expandable and cannot be inferred online for novel scenes, which makes the learned representation difficult to be applied across different reinforcement learning (RL) tasks. In this work, we introduce Scene Memory Network (SMN) to achieve online spatial memory construction and expansion for view rendering in novel scenes. SMN models the camera projection and back-projection as spatially aware memory control processes, where the memory values store the information of the partial 3D area, and the memory keys indicate the position of that area. The memory controller can learn the geometry property from observations without the camera's intrinsic parameters and depth supervision. We further apply the memory constructed by SMN to exploration and navigation tasks. The experimental results reveal the generalization ability of our proposed SMN in large-scale scene synthesis and its potential to improve the performance of spatial RL tasks.

MEinVR: Multimodal interaction techniques in immersive exploration

Immersive environments have become increasingly popular for visualizing and exploring large-scale, complex scientific data because of their key features: immersion, engagement, and awareness. Virtual reality offers numerous new interaction possibilities, including tactile and tangible interactions, gestures, and voice commands. However, it is crucial to determine the most effective combination of these techniques for a more natural interaction experience. In this paper, we present MEinVR, a novel multimodal interaction technique for exploring 3D molecular data in virtual reality. MEinVR combines VR controller and voice input to provide a more intuitive way for users to manipulate data in immersive environments. By using the VR controller to select locations and regions of interest and voice commands to perform tasks, users can efficiently perform complex data exploration tasks. Our findings provide suggestions for the design of multimodal interaction techniques in 3D data exploration in virtual reality.