Relative Distance Research Articles

Spatial Relationship Analysis of Geographic Elements in Sketch Maps at the Meso and Micro Spatial Scales

Sketch maps are an abstract and conceptual expression of humans’ cognition of geographic space. Humans perceive geographical space at different spatial scales. However, few researchers have considered the spatial relationships of geographic elements in sketch maps at multiple spatial scales. Considering the meso and micro spatial scales, this study analyses the accuracy of the spatial relationships depicted in 52 sketch maps of urban areas, including qualitative orientation, order, qualitative distance, and topological relationships. We utilized OpenStreetMap (OSM) to assess the accuracy of the four spatial relationship representations in the sketch maps. This study evaluates the reliability of spatial relationships in capturing the invariant spatial information of geographic elements in sketch maps. It helps to understand the differences in human cognition of multi-scale space.

Semantic Similarity Analysis via Syntax Dependency Structure and Gate Recurrent Unit

Sentences are composed of words, phrases, and clauses. The relationship between them is usually tree-like. In the hierarchical structure of the sentence, the dependency relationships between different components affect the syntactic structure. Syntactic structure is very important for understanding the meaning of the whole sentence. However, the gated recursive unit (GRU) models cannot fully encode hierarchical syntactic dependencies, which leads to its poor performance in various natural language tasks. In this paper, a model called relative syntactic distance bidirectional gated recursive unit (RSD-BiGRU) is constructed to capture syntactic structure dependencies. The model modifies the gating mechanism in GRU through relative syntactic distance. It also offers a transformation gate to model the syntactic structure more directly. Embedding sentence meanings with sentence structure dependency into dense vectors. This model is used to conduct semantic similarity experiments on the QQP and SICK datasets. The results show that the sentence representation obtained by RSD-BiGRU model contains more semantic information. This is helpful for semantic similarity analysis tasks.

Impact of optical navigation variables on the accuracy of robot-assisted surgery: a study of the TIANJI Robot system.

This study aims to explore factors related to optical navigation that interfere with the accuracy of robot-assisted surgery, specifically focusing on the TIANJI Robot system. A measurement model was created to assess the accuracy of the TIANJI Robot system in simulated screw placement. Deviation between actual and planned positions was measured using a three-coordinate machine. Various experiments were conducted to investigate the impact of different optical navigation factors on screw placement accuracy. Deviations were measured at different distances (ranging from 1.2 to 2.2m) between the optical navigation stereo camera and the tracker, with each distance being tested 50 times. The distance between the optical camera and patient tracker was set at 1.4m. Deviations were also measured at different angles between the camera and robot tracker, repeated over 25 times for each angle. Data were analyzed using mean and standard deviation, with line charts illustrating deviation changes based on distance and angle details. Within the range of the TIANJI Robot system's optical navigation (1.2-2.2m), deviation increased as distance increased (χ2 = 479.107, P < 0.001). The robotic system demonstrated high and consistent accuracy (mean deviation: 0.332mm ± 0.067mm) when the relative angle between the optical camera and tracker was below 40°. The accuracy of the TIANJI Robot system was found to be influenced by relative distance and angle between the optical camera and tracker during screw placement procedures. Surgeons are recommended to set a relative distance of 1.4-1.5m between the optical camera and patient tracker, with a relative angle below 40° when placing and adjusting optical tracking devices.

A novel redistribution-based feature selection for text classification

Text classification is the process of automatically categorizing text documents into predefined labels, gaining increasing importance with the growing volume of data. In the vector space model, terms within documents must be quantified to be used with classifiers. The abundance of unique terms associated with documents can lead to unwieldy vectors. To address this issue, feature selection, reducing the number of terms by removing irrelevant terms, is a common solution. In this paper, a novel feature selection method called Amount of ReDistribution to Establish Neutrality, ARDEN, is proposed for the task of text classification. ARDEN, is designed with statistical distance perspective by measuring the distance of a term to its neutral counterpart, which represents the least distinguishing term having uniform document frequencies across all classes. ARDEN introduces a method to measure the related distance, providing insights into the degree of deviation of the term from neutrality, thus capturing its distinguishing power. ARDEN is experimentally tested against state-of-the-art feature selection methods. The results suggest that it is a competitive feature selection method, exhibiting superior performance in terms of summary statistics, μ and δ′. Furthermore, ARDEN is designed for multi-class text classification problems, which does not require a globalization function. Additionally, the proposed method is compared against Wasserstein statistical distance metric, and clearly outperforms it. Finally, we also propose δ′ evaluation criterion in order to facilitate the interpretation of experimental outcomes across multiple feature sizes.

Autonomous imaging scheduling networks of small celestial bodies flyby based on deep reinforcement learning

AbstractDuring the flyby mission of small celestial bodies in deep space, it is hard for spacecraft to take photos at proper positions only rely on ground-based scheduling, due to the long communication delay and environment uncertainties. Aimed at imaging properly, an autonomous imaging policy generated by the scheduling networks that based on deep reinforcement learning is proposed in this paper. A novel reward function with relative distance variation in consideration is designed to guide the scheduling networks to obtain higher reward. A new part is introduced to the reward function to improve the performance of the networks. The robustness and adaptability of the proposed networks are verified in simulation with different imaging missions. Compared with the results of genetic algorithm (GA), Deep Q-network (DQN) and proximal policy optimization (PPO), the reward obtained by the trained scheduling networks is higher than DQN and PPO in most imaging missions and is equivalent to that of GA but, the decision time of the proposed networks after training is about six orders of magnitude less than that of GA, with less than 1e−4 s. The simulation and analysis results indicate that the proposed scheduling networks have great potential in further onboard application.

Confidence relative off-targets distance-based multi-dimensional transparency evaluation of distribution station area

With the large-scale integration of distributed renewable energy into low-voltage distribution station areas, rapidly growing services put forward new requirements on transparent monitoring. In order to accurately and objectively quantify the transparency of various distribution station areas and measure the impact of different dimensional indexes on the transparency evaluation of distribution station areas, this paper proposes a confidence relative off-target distance-based multi-dimensional transparency evaluation method. First, a multi-dimensional transparency evaluation index system with electrical and communication integration is constructed. Second, an improved gray target model combining both positive and negative target vectors is established to realize bi-directional quantitative analysis. Then, the relative off-target distance is calculated based on the endowment coefficient by leveraging both subjective and objective weights. Finally, the confidence of the relative off-target distance is calculated based on fuzzy entropy to improve the reliability of transparency evaluation. The simulation results show that this method can effectively distinguish the transparency gap between different distribution station areas, identify the single dimensional index with the greatest contribution or potential, and verify the effectiveness of the proposed method applied to the transparency evaluation of distribution station areas.

Temporal analysis of relative distances (TARDIS) is a robust, parameter-free alternative to single-particle tracking.

In single-particle tracking, individual particles are localized and tracked over time to probe their diffusion and molecular interactions. Temporal crossing of trajectories, blinking particles, and false-positive localizations present computational challenges that have remained difficult to overcome. Here we introduce a robust, parameter-free alternative to single-particle tracking: temporal analysis of relative distances (TARDIS). In TARDIS, an all-to-all distance analysis between localizations is performed with increasing temporal shifts. These pairwise distances represent either intraparticle distances originating from the same particle, or interparticle distances originating from unrelated particles, and are fitted analytically to obtain quantitative measures on particle dynamics. We showcase that TARDIS outperforms tracking algorithms, benchmarked on simulated and experimental data of varying complexity. We further show that TARDIS performs accurately in complex conditions characterized by high particle density, strong emitter blinking or false-positive localizations, and is in fact limited by the capabilities of localization algorithms. TARDIS' robustness enables fivefold shorter measurements without loss of information.

Examining Pragmatic Knowledge in Speech Acts of Request, Compliment Response, and Apology among Moroccan EFL Students

This research delves into the pragmatic competencies in interlanguage of Moroccan EFL learners, focusing on the speech acts of requests, apologies, and compliment responses. Utilizing Discourse Completion Tasks (DCT) as the cornerstone of data collection, the study scrutinizes 16 diverse situations involving advanced-level EFL learners in Morocco. The study not only reinforces existing theories about the role of social and relational factors in shaping linguistic behavior but also uncovers the nuanced interplay of variables such as power dynamics, relational distance, and imposition in influencing these speech acts. Significantly, the research reveals that Moroccan EFL learners are not mere passive recipients of established linguistic norms; they actively adapt their language choices in response to a complex array of sociocultural and sociolinguistic variables. The study concludes with a call for more explicit and targeted instruction in interlanguage pragmatics within the Moroccan EFL educational setting to better prepare students for authentic interactions. These insights have far-reaching pedagogical implications, particularly for the crafting of EFL curricula that are attuned to the nuanced sociolinguistic variables that influence language use.

Exploring the Location of Corneal Pigmented Arc and Myopia Control Efficacy in Orthokeratology-Treated Children Using Pentacam Measurements.

To determine the location and intensity of the corneal pigmented arc in orthokeratology (ortho-k)-treated children and its relationship with annual axial length (AL) change using Pentacam. This retrospective cohort study enrolled children aged 9 to 15 years who had been followed up for at least one year after ortho-k treatment for myopia control. A Pentacam was used to determine the location and intensity of pigmented arc after lens wear. Annual AL changes were further used as the outcome measurement to determine their relationships with the location and intensity of pigmented arc using generalized estimating equations (GEE). In total, 62 eyes from 33 patients (mean age 10.9 years) were included in our final analysis. The mean follow-up time was 30.6 months. The mean annual AL changes were 0.10 mm. Age statistically correlated with annual AL change (GEE, P= 0.033). In addition, the annual AL change was negatively associated with the relative vertical distance of the lowest density of pigmented arc point based on the visual center, pupil center, and corneal thinnest point after adjustment with age ( P =0.005, P =0.004, and P< 0.001, respectively). Pentacam could be a useful tool for evaluating the location and intensity of the corneal pigmented arc. In addition, there was a negative correlation between the vertical distance of the pigmented arc and annual AL change. These findings may provide important information regarding myopia control, next-generation ortho-k design, and prescription.

Research on a UAV spray system combined with grid atomized droplets.

UAVs for crop protection hold significant potential for application in mountainous orchard areas in China. However, certain issues pertaining to UAV spraying need to be addressed for further technological advancement, aimed at enhancing crop protection efficiency and reducing pesticide usage. These challenges include the potential for droplet drift, limited capacity for pesticide solution. Consequently, efforts are required to overcome these limitations and optimize UAV spraying technology. In order to balance high deposition and low drift in plant protection UAV spraying, this study proposes a plant protection UAV spraying method. In order to study the operational effects of this spraying method, this study conducted a UAV spray and grid impact test to investigate the effects of different operational parameters on droplet deposition and drift. Meanwhile, a spray model was constructed using machine learning techniques to predict the spraying effect of this method. This study investigated the droplet deposition rate and downwind drift rate on three types of citrus trees: traditional densely planted trees, dwarf trees, and hedged trees, considering different particle sizes and UAV flight altitudes. Analyzing the effect of increasing the grid on droplet coverage and deposition density for different tree forms. The findings demonstrated a significantly improved droplet deposition rate on dwarf and hedged citrus trees compared to traditional densely planted trees and adopting a fixed-height grid increased droplet coverage and deposition density for both the densely planted and trellised citrus trees, but had the opposite effect on dwarfed citrus trees. When using the grid system. Among the factors examined, the height of the sampling point exhibited the greatest influence on the droplet deposition rate, whereas UAV flight height and droplet particle size had no significant impact. The distance in relation to wind direction had the most substantial effect on droplet drift rate. In terms of predicting droplet drift rate, the BP neural network performed inadequately with a coefficient of determination of 0.88. Conversely, REGRESS, ELM, and RBFNN yielded similar and notably superior results with a coefficient of determination greater than 0.95. Notably, ELM demonstrated the smallest root mean square error.

Top-class women's soccer performance: peak demands and distribution of the match activities relative to maximal intensities during official matches.

The aims of the current study were to determine the most demanding passages of match play (MDP) and the distribution of match activities relative to maximum intensities during official matches in top-class women soccer players. Twenty-eight women players competing in European championship and international UEFA competitions were monitored during 38 official matches (277 individual samples). Maximum relative (m · min-1) total distance (TD), high-speed running (HSRD), very high-speed running (VHSRD), sprint, acceleration and deceleration distances were calculated across different durations (1-5, 10, 15, 90 min) using a rolling average analysis. Maximum intensities (1-minpeak) were used as the reference value to determine the distribution of relative intensity across the whole-match demands (90-minavg). Time and distance higher than 90-minavg (> 90-minavg) were also calculated. MDP showed moderate to very large [effect size (ES): 0.63/5.20] differences between 1-minpeak vs all durations for each parameter. The relative (m · min-1) 1-minpeak was greater than 90-minavg of about +63% for TD, +358% for HSRD, +969% for VHSRD, +2785% for sprint, +1216% for acceleration, and +768% for deceleration. The total distance covered > 90-minavg was ~66.6% of the total distance covered during the 90-minavg for TD, ~84.8% for HSRD, ~97.4% for VHSRD, ~100% for sprint, ~99.1% for acceleration and ~98.2% for deceleration. The relative distance > 90-minavg was higher (P < 0.05) than the 90-minavg for each metric (ES: 2.22 to 7.58; very large). The present results may help coaches and sport scientists to replicate the peak demands during training routine in top-class women soccer players.

Strong misalignment tolerance wireless power transfer with active adjustment of magnetic shielding

The variation of mutual-inductance is the essential reason for fluctuation of transmission power and efficiency during wireless power transfer (WPT) misalign. To maintain output power stability, current methods, such as primary regulation, secondary conversion, magnetic coupling mechanism (MCM) optimization, and compensation topology design, have not changed the characteristic of mutual-inductance changing with misalignment. A strong misalignment tolerance WPT system based on the influence of high permeability magnetic materials on equivalent electrical parameters of MCM is proposed. When the primary and secondary sides of MCM shift, the relative distance between magnetic shielding and coil (RDMSC) is adjusted to maintain the stability of mutual-inductance. The transmission efficiency and power are not affected by misalignment. Alternatively, RDMSC can be actively adjusted to meet the various needs of diverse loads at different times. Simulations and experiments are conducted. The effectiveness of the proposed scheme that RDMSC is actively adjusted to overcome misalignment is verified. This is a new method based on active adjustment of spatial electromagnetic coupling, which provides a new idea for WPT to overcome the influence of misalignment and maintain stable output.

Structural and electronic changes in the Ni13@Ag42 nanoparticle under surface oxidation: the role of silver coating.

Icosahedral Ni13@Ag42 is a stable nanoparticle formed by a magnetic nickel core surrounded by a silver coating that provides physical protection to the 3d metal cluster as well as antibacterial properties. In this work, we report density functional theoretical calculations to delve into a comprehensive analysis of how surface oxidation impacts the structural, electronic, magnetic, and reactivity properties of this interesting nanoparticle. To elucidate the role played by the silver coating, we compare the results with those found for the bare Ni13 cluster also subjected to surface oxidation. When Ni13 is covered by silver, we find a markedly robust behavior of the magnetic moment of the resulting nanoparticle, which remains nearly constant upon oxidation up to the rates explored, and the same holds for its overall reactivity. The obtained trends are rationalized in terms of the complex interplay between Ni-Ag and Ag-O interactions which impact the relative inter-atomic distances, charge transfer effects, spin polarization and magnetic couplings.

Extensible Machine Learning for Encrypted Network Traffic Application Labeling via Uncertainty Quantification

With the increasing prevalence of encrypted network traffic, cyber security analysts have been turning to machine learning (ML) techniques to elucidate the traffic on their networks. However, ML models can become stale as new traffic emerges that is outside of the distribution of the training set. In order to reliably adapt in this dynamic environment, ML models must additionally provide contextualized uncertainty quantification to their predictions, which has received little attention in the cyber security domain. Uncertainty quantification is necessary both to signal when the model is uncertain about which class to choose in its label assignment and when the traffic is not likely to belong to any pre-trained classes. We present a new, public dataset of network traffic that includes labeled, Virtual Private Network (VPN)-encrypted network traffic generated by 10 applications and corresponding to 5 application categories. We also present an ML framework that is designed to rapidly train with modest data requirements and provide both calibrated, predictive probabilities as well as an interpretable "out-of-distribution" (OOD) score to flag novel traffic samples. We describe calibrating OOD scores using p-values of the relative Mahalanobis distance. We demonstrate that our framework achieves an F1 score of 0.98 on our dataset and that it can extend to an enterprise network by testing the model: (1) on data from similar applications, (2) on dissimilar application traffic from an existing category, and (3) on application traffic from a new category. The model correctly flags uncertain traffic and, upon retraining, accurately incorporates the new data.

Cooperative Operation Control of Virtual Coupling High-Speed Trains With Input Saturation and Full-State Constraints

In this paper, the distributed cooperative control of virtual coupling high-speed trains (HSTs) subject to full-state constraints, actuator limitations, dynamical uncertainties and environmental disturbances is investigated. Targeting at the full-state constraints in cooperative operation of HSTs, a distributed nonlinear state-dependent function (DNSDF) is first proposed to convert the state-constrained problem of the leader-following consensus control to the boundedness problem of DNSDF. Then, the distributed control law of each train is designed by combining the command filtering backstepping method and the adaptive neural network approximation technique. Meanwhile, combined with the DNSDF, a novel auxiliary dynamical system (ADS) is designed to compensate for the adverse effects of actuator input saturation, and thus ensure the closed-loop stability of the HSTs system when the state constraints and the input saturation are considered simultaneously. By utilizing the Lyapunov theory, the convergence of the proposed controller is analyzed. Finally, the feasibility and effectiveness of the proposed control scheme are verified by simulations. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This work was motivated by the problem of cooperative control for virtual coupling HSTs with different initial states, actuator input saturation, full-state constraints, etc. The proposed approach addresses the train position and speed constraints by applying DNSDF, which can ensure train coordinated operation with relative braking distance and further reduce the tracking interval between trains. Specifically, the upper bound of the train position constraint varies with the real-time position of the preceding train and the relative speed of the adjacent trains, rather than a constant value. More importantly, an ADS is designed based on the DNSDF, which guarantees the stability of the controller when the input saturation and state constraints exist simultaneously, and avoids the chattering phenomenon of the train control input. The simulation results show that the trains can operate cooperatively at any initial speed within the speed limitations. In future, we will focus on the cooperative control of HSTs with communication delay, and the energy saving optimization cooperative control of HSTs.

Influences of Cloud Vertical Overlapping on the Calculated Cloud Albedo and Their Validation with Satellite Observations

Abstract Cloud albedo is expected to influence cloud radiative forcing in addition to cloud fraction, and inadequate description of the cloud overlapping effects on the cloud fraction may influence the simulated cloud fraction, and thus the relative cloud radiative forcing (RCRF) and cloud albedo. In this study, we first present a new formula by extending that presented previously to consider multilayer clouds directly in the relationship between cloud albedo, cloud fraction, and RCRF, and then quantitatively evaluate the effects of different cloud vertical overlapping structures, represented by the decorrelation length scales (Lcf), on the simulated cloud albedos. We use the BCC_AGCM2.0_CUACE/Aero model with simultaneous validation by observations from the Clouds and the Earth’s Radiation Energy System (CERES) satellite. When Lcf &lt; 4 km (i.e., the cloud overlap is closer to the random overlap), the simulated cloud albedos are generally in good agreement with the satellite-based albedos for December–February and June–August; when Lcf ≥ 4 km (i.e., the cloud vertical overlap is closer to the maximum overlap), the difference between simulated and observed cloud albedos became larger, due mainly to significant differences in cloud fractions and RCRF. Further quantitative analysis shows that the relative Euclidean distance, which represents the degree of overall model–observation disagreement, increases with the Lcf for all three variables (cloud albedo, cloud fraction, and RCRF), indicating the importance of cloud vertical overlapping in determining the accuracy of the calculated cloud albedo for multilayer clouds. Significance Statement The purpose of this study is presenting a new formula to consider multilayer clouds directly in the relationship between cloud albedo, cloud fraction, and relative cloud radiative forcing (RCRF). This is important because the effects of different cloud vertical overlapping structures, represented by the decorrelation length scales (Lcf), can affect the simulated cloud albedos. Our results provide a guide on the importance of cloud vertical overlapping in determining the accuracy of the calculated cloud albedo for multilayer clouds.

Genetic Distance and Kinship Relationship of Walik Kembang Sula Bird (Ptilinopus melanosphila) Based on mtDNA CO1 in North Maluku, Indonesia

Genetic Distance and Kinship Relationship of Walik Kembang Sula Bird (Ptilinopus melanosphila) Based on mtDNA CO1 in North Maluku, Indonesia

The rational co-doping strategy of transition metal and non-metal atoms on g-CN for highly efficient hydrogen evolution by DFT and machine learning

As a clean energy source, hydrogen has attracted high interest in developing efficient hydrogen evolution reaction (HER) catalysts due to its sustainable and renewable characteristics. In this work, we have systematically investigated the HER activity of the g-CN two-dimensional materials. The catalytic activity in HER is enhanced by doping non-metallic atoms (C, N, B, Si) and transition metal atoms (Sc, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn) in the vacancies of g-CN. Based on the first principle calculation, we screened 40 structures and found that the ΔGH* of Sc@C3–CN, V@C3–CN, Mn@C3–CN, Sc@N3–CN, and Ti@Si3–CN was close to zero. Among them, Ti@Si3–CN has the lowest Gibbs free energy change (−0.01 eV) and has excellent HER performance. In addition, we explored HER activity's origin by using machine learning (ML) algorithms. The results show that the atomic relative distance in the TM@X3-CN structure significantly affects the catalytic activity.

Trajectory plan for an ultra-short distance on-orbit service based on the Gaussian pseudo-spectral method

Considering the ultra-short distance approaching plan of on-orbit service, the trajectory plan strategy based on the Gaussian pseudo-spectral method is researched. Given the target spinning, safety, fuel and maneuver factors, a model which contains a secure area as well as a prohibited area is built. Furthermore, the method to plan a safe on-orbit service trajectory and the solution based on the Gaussian pseudo-spectral method are also presented. Finally, simulations are performed to prove the validity of the plan, and analyze the influence of optimal function and maneuver time. The results demonstrate that the planned trajectory is capable of avoiding the prohibited area and finding the best trajectory in terms of the relative distance, fuel cost and maneuver time respectively.

基于蚁群-改进人工势场法的农业搬运机器人路径规划分析

Aiming at the problems of low efficiency and slow function convergence of the ant colony algorithm in path planning of agricultural transport robots, a fusion algorithm combined with the artificial potential field method was proposed. Firstly, the function of each parameter was analyzed according to the mathematical model of the traditional ant colony algorithm, followed by the simulation analysis of the optimal parameters through grid map modeling in MATLAB and data recording. Secondly, the deficiency of the classical artificial potential field method in agriculture, i.e., it could not arrive at the endpoint or realize local locking, was improved by introducing the intermediate point and the relative distance of the target. Finally, the features of the two algorithms were combined and the improved artificial potential field method was integrated with the traditional ant colony algorithm so that the improved artificial potential field method could play a dominant role in the initial path planning stage of agricultural transport robots while the ant colony algorithm exerted the main effect in the later stage with the increase in the pheromone concentration. It was verified through simulation analysis, it was verified that the fusion algorithm of ant colony algorithm and improved artificial potential field method outperforms traditional ant colony algorithm in terms of farthest path, optimal path, running time, and iteration number.