Trajectory Duration Research Articles

Measuring Trajectory Similarity Based on the Spatio-Temporal Properties of Moving Objects in Road Networks

Advancements in navigation and tracking technologies have resulted in a significant increase in movement data within road networks. Analyzing the trajectories of network-constrained moving objects makes a profound contribution to transportation and urban planning. In this context, the trajectory similarity measure enables the discovery of inherent patterns in moving object data. Existing methods for measuring trajectory similarity in network space are relatively slow and neglect the temporal characteristics of trajectories. Moreover, these methods focus on relatively small volumes of data. This study proposes a method that maps trajectories onto a network-based space to overcome these limitations. This mapping considers geographical coordinates, travel time, and the temporal order of trajectory segments in the similarity measure. Spatial similarity is measured using the Jaccard coefficient, quantifying the overlap between trajectory segments in space. Temporal similarity, on the other hand, incorporates time differences, including common trajectory segments, start time variation and trajectory duration. The method is evaluated using real-world taxi trajectory data. The processing time is one-quarter of that required by existing methods in the literature. This improvement allows for spatio-temporal analyses of a large number of trajectories, revealing the underlying behavior of moving objects in network space.

Evaluating the influence of the mandibular canal trajectory on the duration of postoperative paraesthesia in patients undergoing inferior alveolar nerve lateralisation: a prospective cohort study

This prospective cohort study aims to evaluate the influence of the mandibular canal trajectory on the duration of postoperative paraesthesia in patients undergoing inferior alveolar nerve lateralisation (IANL). Twenty patients received a total of 50 dental implants, and their postoperative paraesthesia duration, implant success rate, and anatomical variables were assessed. All patients experienced temporary neurosensory disturbances postoperatively during the first week, but none reported permanent issues at the 12-month follow up. The median paraesthesia duration was 120 days, and no significant differences were detected between genders, anaesthesia types, or patient satisfaction. No significant association was found between the mandibular canal trajectory and postoperative paraesthesia duration. The implant success rate was 100%, with all implants integrating successfully. Our findings suggest that IANL is a safe and effective method for dental implant placement in atrophic mandibles.

Trajectory Optimization for Autonomous Berthing of a Twin-Propeller Twin-Rudder Ship

Autonomous berthing is a crucial technology for autonomous ships, requiring optimal trajectory planning to prevent collisions and minimize time and control efforts. This paper presents a two-phase, two-point boundary value problem (TPBVP) strategy for creating an optimal berthing trajectory for a twin-propeller, twin-rudder ship with autonomous berthing capabilities. The process is divided into two phases: the approach and the terminal. Tunnel thruster use is limited during the approach but fully employed during the terminal phase. This strategy permits concurrent optimization of the total trajectory duration, individual phase trajectories, and phase transition time. The efficacy of the proposed method is validated through two simulations. The first explores a scenario with phase transition, and the second generates a trajectory relying solely on the approach phase. The results affirm our algorithm's effectiveness in deciding transition necessity, identifying optimal transition timing, and optimizing the trajectory accordingly. The proposed two-phase TPBVP approach holds significant implications for advancements in autonomous ship navigation, enhancing safety and efficiency in berthing operations.

UAV-Assisted Online Machine Learning Over Multi-Tiered Networks: A Hierarchical Nested Personalized Federated Learning Approach

We investigate training machine learning (ML) models across a set of geo-distributed, resource-constrained clusters of devices through unmanned aerial vehicles (UAV) swarms. The presence of time-varying data heterogeneity and computational resource inadequacy among device clusters motivate four key parts of our methodology: (i) stratified UAV swarms of leader, worker, and coordinator UAVs, (ii) hierarchical nested personalized federated learning ( HN-PFL), a distributed ML framework for personalized model training across the worker-leader-core network hierarchy, (iii) cooperative UAV resource pooling to address computational inadequacy of devices by conducting model training among the UAV swarms, and (iv) model/concept drift to model time-varying data distributions. In doing so, we consider both micro (i.e., UAV-level) and macro (i.e., swarm-level) system design. At the micro-level, we propose network-aware HN-PFL, where we distributively orchestrate UAVs inside swarms to optimize energy consumption and ML model performance with performance guarantees. At the macro-level, we focus on swarm trajectory and learning duration design, which we formulate as a sequential decision making problem tackled via deep reinforcement learning. Our simulations demonstrate the improvements achieved by our methodology in terms of ML performance, network resource savings, and swarm trajectory efficiency.

Associations between sleep trajectories up to 54 months and cognitive school readiness in 4 year old preschool children.

This study explores the association between the duration and variation of infant sleep trajectories and subsequent cognitive school readiness at 48-50 months. Participants were 288 multi-ethnic children, within the Growing Up in Singapore Towards healthy Outcomes (GUSTO) cohort. Caregiver-reported total, night and day sleep durations were obtained at 3, 6, 9, 12, 18, 24 using the Brief Infant Sleep Questionnaire and 54 months using the Child Sleep Habits Questionnaire. Total, night and day sleep trajectories with varying durations (short, moderate, or long) and variability (consistent or variable; defined by standard errors) were identified. The cognitive school readiness test battery was administered when the children were between 48 and 50 months old. Both unadjusted adjusted analysis of variance models and adjusted analysis of covariance models (for confounders) were performed to assess associations between sleep trajectories and individual school readiness tests in the domains of language, numeracy, general cognition and memory. In the unadjusted models, children with short variable total sleep trajectories had poorer performance on language tests compared to those with longer and more consistent trajectories. In both unadjusted and adjusted models, children with short variable night sleep trajectories had poorer numeracy knowledge compared to their counterparts with long consistent night sleep trajectories. There were no equivalent associations between sleep trajectories and school readiness performance for tests in the general cognition or memory domains. There were no significant findings for day sleep trajectories. Findings suggest that individual differences in longitudinal sleep duration patterns from as early as 3 months of age may be associated with language and numeracy aspects of school readiness at 48-50 months of age. This is important, as early school readiness, particularly the domains of language and mathematics, is a key predictor of subsequent academic achievement.

Properties of the dissipation functions for passive and active systems.

The dissipation function for a system is defined as the natural logarithm of the ratio between probabilities of a trajectory and its time-reversed trajectory, and its probability distribution follows a well-known relation called the fluctuation theorem. Using the generic Langevin equations, we derive the expressions of the dissipation function for passive and active systems. For passive systems, the dissipation function depends only on the initial and the final values of the dynamical variables of the system, not on the trajectory of the system. Furthermore, it does not depend explicitly on the reactive or dissipative coupling coefficients of the generic Langevin equations. In addition, we study a one-dimensional case numerically to verify the fluctuation theorem with the form of the dissipation function we obtained. For active systems, we define the work done by active forces along a trajectory. If the probability distribution of the dynamical variables is symmetric under time reversal, in both cases, the average rate of change of the dissipation function with trajectory duration is nothing but the average entropy production rate of the system and reservoir.

MEC-Assisted Real-Time Data Acquisition and Processing for UAV With General Missions

As a promising technology, unmanned aerial vehicles (UAVs)-enabled information gathering has received intensive interest in both academia and industry in recent years. However, the familiar paradigm, namely, data collection can hardly fulfill the active and real-time information gathering demands of many thriving smart applications, such as smart grid, where the UAV needs to serve as an intelligent aerial entity, gathering and analysing the information of surroundings actively and timely. To this aim, this article studies the paradigm <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">data acquisition</i> , where the UAV dynamically gathers information by on-board sensors. We consider a general mission scenario where a UAV acquires data in real time, which needs to be timely processed with the assistance of a mobile edge computing server. To address the real-time characteristics, we construct a novel theoretical <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">data acquisition rate</i> model, based on which the real-time processing quality of requirement (QoR) is defined. We respectively minimize the UAV energy consumption and mission completion time, under the constraints including real-time processing QoR and UAV mobility, by jointly optimizing the UAV trajectory, resources allocation, and time duration. To tackle these highly complex problems, we propose efficient algorithms by means of successive convex approximation and block coordinates descent techniques, where a closed-form solution for the UAV transmission and computation resources allocation is rigorously derived. Moreover, we consider a practical multi-location trajectory optimization where the UAV needs to traverse multiple target locations or areas in addition to the initial and final locations. The simulation results demonstrate the significant system performance gains brought by the proposed design and the effectiveness of the algorithms.

Post-consultation acute respiratory tract infection recovery: a latent class-informed analysis of individual patient data.

There is a lack of evidence regarding post-consultation symptom trajectories for patients with respiratory tract infections (RTIs) and whether patient characteristics can be used to predict illness duration. To describe symptom trajectories in patients with RTIs, and assess baseline characteristics and adverse events associated with trajectories. The study included data about 9103 adults and children from 12 primary care studies. A latent class-informed regression analysis of individual patient data from randomised controlled trials and observational cohort studies was undertaken. Post-consultation symptom trajectory (severity and duration), re-consultation with same or worsening illness, and admission to hospital were assessed. In total, 90% of participants recovered from all symptoms by 28 days, regardless of antibiotic prescribing strategy (none, immediate, and delayed antibiotics). For studies of RTI with cough as a dominant symptom (n = 5314), four trajectories were identified: 'rapid (6 days)' (90% of participants recovered within 6 days) in 52.0%; 'intermediate (10 days)' (28.9%); 'slow progressive improvement (27 days)' (12.5%); and 'slow improvement with initial high symptom burden (27 days)' (6.6%). For cough, being aged 16-64 years (odds ratio [OR] 2.57, 95% confidence interval [CI] = 1.72 to 3.85 compared with <16 years), higher presenting illness baseline severity (OR 1.51, 95% CI = 1.12 to 2.03), presence of lung disease (OR 1.78, 95% CI = 1.44 to 2.21), and median and above illness duration before consultation (≥7 days) (OR 1.99, 95% CI = 1.68 to 2.37) were associated with slower recovery (>10 days) compared with faster recovery (≤10 days). Re-consultations and admissions to hospital for cough were higher in those with slower recovery (ORs: 2.15, 95% CI = 1.78 to 2.60 and 7.42, 95% CI = 3.49 to 15.78, respectively). Older patients presenting with more severe, longer pre-consultation symptoms and chronic lung disease should be advised they are more likely to experience longer post-consultation illness durations, and that recovery rates are similar with and without antibiotics.

Optimal Control for Kinematic Bicycle Model With Continuous-Time Safety Guarantees: A Sequential Second-Order Cone Programming Approach

The optimal control problem for the kinematic bicycle model is considered where the trajectories are required to satisfy the safety constraints in the continuous-time sense. Based on the differential flatness property of the model, necessary and sufficient conditions in the flat space are provided to guarantee safety in the state space. The optimal control problem is relaxed to the problem of solving three second-order cone programs (SOCPs) sequentially, which find the safe path, the trajectory duration, and the speed profile, respectively. Solutions of the three SOCPs together provide a sub-optimal solution to the original optimal control problem. Simulation examples and comparisons with state-of-the-art optimal control solvers are presented to demonstrate the effectiveness of the proposed approach.

Key for a Hidden Quantum State.

Quantum trajectories are crucial to understanding the evolution of open systems. We consider an open cavity mode undergoing up and down multistate quantum jumps due to the emission and absorption of photons. We prove that among all subtrajectories, starting simultaneously from different photon number states, only one survives a long single-run evolution. A random Fock state terminating the subtrajectory becomes known for the ergodic case via the key-the processed record of the input and output photocounts, and the trajectory duration. Based on this result, we propose a robust protocol to infer the Fock state, a valuable resource for quantum applications.

Generalized Path Planning for UTM Systems With a Space-Time Graph

Motivated by the increased use of UAS in commercial applications, in this paper we tackle the problem of path planning when requests are submitted by UAS managed by different operators. We propose the new problem of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">generalized path planning</i> for UAS Traffic Management, where the UAS path is described by operators with a sequence of waypoint groups and a solution trajectory must pass through a waypoint in each group. This problem is typical for applications where multiple charging stations and pickup/drop-off locations are distributed in a flight area. Our solution builds upon prior work on discretized space-time graph path planning and proposes a novel multi-source/multi-destination graph search algorithm that generates collision-free trajectories for pre-flight CDR. Our efficient algorithm has runtime proportional to the number of groups and avoids combinatorial explosion. We apply our mechanism to the energy-constrained UAS package delivery problem with multiple warehouses and battery charging stations. Simulation results show that our algorithm is efficient and scalable with the number of requests and graph size. The addition of charging stations and the option for multiple warehouses increases the request admission ratio and reduces the overall trajectory duration, effectively improving both the planner’s quality of service and the efficiency of airspace usage.

Low length/weight growth trajectories of early-term infants during the first year: evidence from a longitudinal study in China

ObjectiveTo identify common length, weight and body mass index (BMI) growth trajectories of term infants during infancy, and to determine their association with early-term infants.DesignProspective longitudinal study.SettingWuhan, China.PatientsA total of...

Optimization of UAV-Aided Millimeter-Wave IoT Systems

Due to their maneuverability, unmanned aerial vehicles (UAVs) have grown into a promising enabler of the Internet of Things (IoTs). In addition to the benefits of the bandwidth and communication quality of millimeter-wave (mmWave) systems, a UAV-aided mmWave multiple-input and multiple-output (MIMO) communication system is investigated in this paper for the data collection of IoT systems, in which single-antenna IoT devices are divided into several clusters, and the UAV aided mmWave base station (UAV-BS) collects data from each cluster using the time division scheme. The joint optimization of the beam selection, UAV trajectory, user clustering, power allocation and transmission duration is studied in this paper to improve the data collection efficiency. The solution of the problem is then given in three steps. Firstly, the incremental K-means clustering and ant colony optimization algorithm are utilized to handle the UAV trajectory planning and user clustering problem. Secondly, an incremental beam selection scheme is employed to ensure that all the devices in each cluster can communicate with the UAV. Thirdly, an iterative algorithm is proposed by alternately optimizing the power allocation and transmission duration of the IoT devices. Finally, the simulation results demonstrate the effectiveness of the proposed solution for the UAV-aided mmWave communication system.

Learning physical properties of anomalous random walks using graph neural networks

Single particle tracking allows probing how biomolecules interact physically with their natural environments. A fundamental challenge when analysing recorded single particle trajectories is the inverse problem of inferring the physical model or class of models of the underlying random walks. Reliable inference is made difficult by the inherent stochastic nature of single particle motion, by experimental noise, and by the short duration of most experimental trajectories. Model identification is further complicated by the fact that main physical properties of random walk models are only defined asymptotically, and are thus degenerate for short trajectories. Here, we introduce a new, fast approach to inferring random walk properties based on graph neural networks (GNNs). Our approach consists in associating a vector of features with each observed position, and a sparse graph structure with each observed trajectory. By performing simulation-based supervised learning on this construct [], we show that we can reliably learn models of random walks and their anomalous exponents. The method can naturally be applied to trajectories of any length. We show its efficiency in analysing various anomalous random walks of biological relevance that were proposed in the AnDi challenge []. We explore how information is encoded in the GNN, and we show that it learns relevant physical features of the random walks. We furthermore evaluate its ability to generalize to types of trajectories not seen during training, and we show that the GNN retains high accuracy even with few parameters. We finally discuss the possibility to leverage these networks to analyse experimental data.

Sampling nearshore Infaunal ‘weeds’ rather than ‘trees’: Does this orthodoxy undervalue importance of sedimentary biomes?

This study compares species composition, abundance, biomass, and several other factors by which sedentary megafaunal and macrofaunal assemblages at depths between 14 m and 55 m on the Hueneme Shelf in southern California, USA, can be characterized. It details fundamental differences between these assemblages in this study area and demonstrates how little we know about a diverse range of attributes that are important in describing and evaluating infaunal assemblages. It provides strong support for a recent claim that: “The soft-sediment seafloor of the open continental shelf is among the least-known biomes on Earth, despite its high diversity and importance to fisheries and biogeochemical cycling” (Tomašových and Kidwell, 2017).Basic and applied research after the 1950s transformed infaunal ecology from a discipline focused primarily on long-lived sedentary megafauna (analogous to ‘trees’ on land) into a field concentrating on opportunistic or ephemeral macrofauna (analogous to ‘weeds’ on land). Based on findings of this study, macrofaunal assemblages play a subordinate role in ecosystem dynamics. Most research in recent decades has overlooked contributions by sedentary megafauna. This focus leads to a poor understanding of the dynamics of these widespread ecosystems, severely undervalues their ecological importance and production, underestimates impacts of anthropogenic activities on unconsolidated sediments as well as the duration of recovery trajectories following disturbances.Sedentary megafaunal and macrofaunal assemblages were quite different in virtually all aspects assessed during this analysis. Although macrofauna in this study had far greater species richness and were far more abundant than sedentary megafauna, conservative estimates for megafaunal biomass and secondary production indicate these organisms contribute substantially more energy to higher trophic levels than macrofauna. In addition, the megafaunal assemblages are characterized by many influential ‘ecosystem engineers’. Consequently, the differences in numbers of species and individuals are irrelevant with regard to determining value to the ecosystem. Megafauna in this area also exhibited more sensitivity in environmental factors. They live substantially longer than macrofauna, likely leading to greater stability in megafaunal assemblages. These differences indicate that current approaches focusing on macrofauna severely undervalue infaunal resources and contributions to higher trophic levels. They imply that studies of megafaunal assemblages should provide considerably greater insight into ecosystem dynamics and secondary production than macrofaunal assemblages and would have greater power to predict or assess environmental degradation or change, and document or predict recovery trajectories.

Minimizing eclipses via synodic resonant orbits with applications to EQUULEUS and MMX

For deep-space missions that remain in the vicinity of a target body, solar eclipses might become a source of major concern due to thermal and power constraints. This paper presents a method for minimizing the eclipse duration of periodic trajectories in three-body systems using synodic resonant periodic orbits. The proposed methodology successfully captures the global eclipse structure of synodic resonant periodic orbits in terms of driving parameters such as elongation and phase angles. Two-dimensional Eclipse maps are introduced to identify optimal orbit insertion conditions that avoid or minimize eclipse intervals. The validity and applicability of the proposed method is tested in the full-ephemeris model (DE430) of the Earth–Moon and Mars–Phobos systems, respectively. As a result of these investigations, we propose new science minimum-eclipse trajectories that are under consideration for the upcoming JAXA missions EQUULEUS and MMX.

Development of a trajectory planning algorithm for a 4-DoF rockbreaker based on hydraulic flow rate limits

In this paper, a novel trajectory planning methodology is proposed for use within a semi-automated hydraulic rockbreaker system. The objective of the proposed method is to minimize the trajectory duration while hydraulic fluid flow rate limits are respected. Within the trajectory planning methodology, a point-to-point path planning approach based on the decoupling of the motion of the rockbreaker’s first joint is compared with an alternative approach based on Cartesian straight-line motion. Each of these path types is parameterized as a function of time based on an imposed trajectory profile that ensures smooth rockbreaker motions. A constrained nonlinear optimization problem is formulated and solved with the trajectory duration as the objective function while constraints are applied to ensure that flow rate limits through the rockbreaker’s proportional valves and hydraulic pump are not exceeded. The proposed methodology is successfully implemented to compute a set of representative trajectories, with the path planning approach based on the decoupling of the motion of the rockbreaker’s first joint consistently producing shorter trajectory durations.

UAV-Supported Clustered NOMA for 6G-Enabled Internet of Things: Trajectory Planning and Resource Allocation

The sixth-generation (6G) communication requires supporting massive Internet of Things (IoT) devices and extremely differentiated IoT applications for the air&#x2013;space&#x2013;ground integrated network. Relying on the aerial superiority, unmanned aerial vehicle (UAV) is capable of acting as an aerial base station (BS) and supporting IoT deployment in remote and disaster areas. A UAV-supported clustered nonorthogonal multiple access (C-NOMA) system is put forward in this article. Specifically, the UAV provides services to IoT terminals as an aerial BS based on the wireless-powered communication (WPC) technique. According to this system, we propose a synergetic scheme for UAV trajectory planning and subslot allocation. Our goal is to maximize the uplink average achievable sum rate of IoT terminals by synergistically planning UAV trajectory and subslot duration, while guaranteeing the uplink achievable sum rate and the UAV mobility constraints. As the formulated problem suffers nonconvexity and complication, an efficient iterative algorithm is proposed to address it. First, for fixed UAV trajectory, all the terminals are clustered and a subslot allocation algorithm based on the Lagrange multiplier and bisection method is proposed. Then, for a fixed clustering state and subslot duration, we optimize the UAV trajectory. Finally, we solve these two subproblems alternatively until the objective function converges. The effectiveness of the proposed scheme in the UAV-supported C-NOMA system is verified by the numerical results.

Research and application of variable DOF compliant five-bar mechanism based on novel compliant torsion joint in vehicle side door latch

In this paper, a novel compliant joint with torsion spring (CJTS) is proposed in order to realize the power release (Note: The “power release” feature means that the closure system with a latch assembly including a pawl-ratchet release mechanism is controlled by at least one electric actuator.) feature of the vehicle side door latch. A new type of variable degree of freedom (DOF) compliant five-bar mechanism (VDCM) based on the novel compliant joint is constructed. The novel compliant mechanism is characterized by multiple motion modes of planar five-bar mechanism and four-joint, crank-shaper, crank-oscillating block mechanism. These motion modes can be switched through the different choices of driving joints and limiting stoppers. This compliant mechanism is effectively implanted into a vehicle side door latch as a sub-branch to perform power release function. The force-adaptive characteristic of the VDCM ensures compatibility with extant manual release branches. Drifting displacement of the CJTS’s torsion spring rotation center in the groove is proposed as a compliant index to describe the compatibility and force-adaptive characteristics under various working modes. The relationship between torsion spring stiffness and mechanism characteristic point motion trajectory or position recovery time duration or motion accuracy is studied. The results show that the introduction of compatibility and force-adaptive characteristics is able to reduce the shaking forces exerted on the mechanism frame. The shaking forces will be further reduced by changing equivalent mass center position of the component. Furthermore, the practicability of the novel compliant mechanism is experimentally validated on the force-displacement test platform. The work in this paper provides a reference for the multi-mode motion mechanical design in a confined space of the latch.

Duration of Care Trajectories in Persons With Dementia Differs According to Demographic and Clinical Characteristics

ObjectivesTo estimate (1) the duration of no formal care, home care, and institutional care after dementia diagnosis, and (2) the effect of age, sex, living situation, dementia medication, migration background, and income on this dementia care duration. DesignLongitudinal retrospective study using routinely recorded general practice electronic health records linked with population-based healthcare and mortality data. Setting and participantsIn total, 11,012 community-dwelling persons who received an incident dementia diagnosis and were listed in a Dutch general practitioner database from 448 general practices in the Netherlands. MethodsUsing multistate modeling analyses, we estimated the mean duration of care types (no/home/institutional care) for different ages based on simulations of transition rates and examined the influence of demographic and clinical factors on these durations. ResultsFrom dementia diagnosis onward in 85-year-old men, the mean duration without formal care was 0.7 years, of home care 1.7, and institutional care 1.1 years. In 85-year-old women, the duration without formal care was 0.8 years, of home care 2.3, and institutional care 2.3 years. Total care duration was 3.5 years in 85-year-old men and 5.4 years in 85-year-old women. In men, the duration of home care was longer compared with no formal care and institutional care. The duration of no formal care was longer in persons not living alone, without prescribed dementia medication, with a non‒Western migration background, or with a higher income. The duration of home or institutional care was longer in women, persons without polypharmacy, in those living alone, or those with a Western background. Conclusions and implicationsOur findings help to increase understanding of long-term dementia care trajectories and show that demographic and clinical factors determine the duration of care types. Our results can contribute to the organization of healthcare resource planning and monitoring of the effects of healthcare policy and interventions.