PathFinder Model Research Articles

A dyeing clustering algorithm based on ant colony path-finding mechanism

Clustering has a wide range of applications in various industries. In different research ideas, bionic algorithms have heuristic help for clustering research. However, most of the clustering algorithms based on bionics often have problems of insufficient information extraction, too many required parameters, and limited application scope. Therefore, these algorithms have lower accuracy than traditional clustering algorithms. To solve these problems, this paper proposes a new ant colony clustering algorithm, which is called Dyeing Clustering Algorithm based on Ant Colony Path-finding. This algorithm combines the idea of dyeing with pheromones for the first time and realizes clustering by using ants to crawl and dye each point, bringing new inspiration to the artificial intelligence research. This can better capture data features, improve fault tolerance and achieve a better clustering effect. In addition, the algorithm is constructed as a dynamic system to deeply mine data information with the strategy of swarm intelligence, including the ant colony generation model, path-finding model, path-finding termination model, and dyeing clustering model. Besides, to evaluate the algorithm, we selected 2 simulation data sets, 5 artificial data sets, 5 real-world data sets, 13 comparison algorithms, and 8 evaluation indexes. From the comprehensive multi-round experiments, seven of the eight evaluation indexes of the Dyeing Clustering Algorithm based on Ant Colony Path-finding algorithm are more advantageous than other comparison algorithms, which demonstrates its enormous potential in industrial applications.

Optimal Deception Asset Deployment in Cybersecurity: A Nash Q-Learning Approach in Multi-Agent Stochastic Games

In the face of an increasingly intricate network structure and a multitude of security threats, cyber deception defenders often employ deception assets to safeguard critical real assets. However, when it comes to the intranet lateral movement attackers in the cyber kill chain, the deployment of deception assets confronts the challenges of lack of dynamics, inability to make real-time decisions, and not considering the dynamic change of an attacker’s strategy. To address these issues, this study introduces a novel maze pathfinding model tailored to the lateral movement context, in which we try to find out the attacker’s location to deploy deception assets accurately for interception. The attack–defense process is modeled as a multi-agent stochastic game, by comparing it with random action policy and Minimax-Q algorithm, we choose Nash Q-learning to solve the deception asset’s deployment strategy to achieve the optimal solution effect. Extensive simulation tests reveal that our proposed model exhibits good convergence properties. Moreover, the average defense success rate surpasses 70%, attesting to the model’s efficacy.

Spatio-temporal registration of plants non-rigid 3-D structure

Monitoring plants growth dynamics requires continuously tracing their evolution over time. When using point cloud data, such a process requires associating the individual organs among scans, spatially aligning them, and accounting for their evolution, decay, or split. It is common to address this challenge by abstracting the point cloud into its skeletal form and defining point correspondence by Euclidean measures. As the paper demonstrates, standard skeletonization approaches do not capture the actual plant topology, and Euclidean measures do not document its evolving form. To address this alignment challenge, we propose in this paper a registration model that traces high-degree deformations and accommodates the complex plant topology. We develop an embedded deformation graph-based solution and introduce manifold measures to trace the plant non-isometric development. We demonstrate how a path-seeking strategy and invariant features capture the plant topological form, and then use a probabilistic linear assignment solution to associate organs across scans. By minimizing deviations from rigidity, our registration form maintains elasticity, and by solving locally rigid transformations, regularized by structure-related constraints, we secure smoothness and optimality. We also demonstrate how data arrangement and linear path-finding models make our solution computationally efficient. Our model is applied on high quality laser triangulation data, commonly tested in 4-D plant registration studies, but is also verified on low resolution and noisy pointsets reconstructed from a limited number of images by multiview stereo (MVS). Our results demonstrate 0.1mm levels of accuracy when applied to plant species exhibiting complex geometric structures. They improve by tenfold or more state-of-the-art results and transform correctly the plants form. Paper related resources are available at PLANT4D.

A fuel-efficient reliable path finding algorithm in stochastic networks under spatial correlation

Transport activities are regarded as a major source of fuel consumption and CO2 emission production. To reduce the negative impact of traffic-related CO2 emission, this paper proposes a reliable path-finding algorithm for improving fuel efficiency in stochastic networks under the uncertainty of travel time and fuel consumption with the consideration of spatial correlation. A reliable constrained path-finding model is developed and formulated to minimize the fuel consumption budget while guaranteeing the specified on-time arrival probability. A heuristic label setting algorithm is developed to precisely solve the formulated problem. The proposed algorithm overcomes the time-consuming drawbacks of traditional path enumeration algorithms. The applicability and efficiency of the proposed algorithm are verified on real-world traffic data acquired from the Beijing and Xi‘an networks in China. The experiments demonstrate that our proposed algorithm can significantly reduce fuel consumption compared to existing studies. The experiment in Beijing shows that using the proposed algorithm can reduce 0.9 kg of CO2 emissions on average per trip compared to existing studies.

Retracted: A Simulation Analyzing Approach to Estimating the Probability of Airborne Infection Risks in Railway Station Platform Coupling with the Wells‐Riley Model and Pathfinder Model

Retracted: A Simulation Analyzing Approach to Estimating the Probability of Airborne Infection Risks in Railway Station Platform Coupling with the Wells‐Riley Model and Pathfinder Model

Pathfinder v1.0.1: a Bayesian-inferred simple carbon–climate model to explore climate change scenarios

Abstract. The Pathfinder model was developed to fill a perceived gap within the range of existing simple climate models. Pathfinder is a compilation of existing formulations describing the climate and carbon cycle systems, chosen for their balance between mathematical simplicity and physical accuracy. The resulting model is simple enough to be used with Bayesian inference algorithms for calibration, which enables assimilation of the latest data from complex Earth system models and the IPCC sixth assessment report, as well as a yearly update based on observations of global temperature and atmospheric CO2. The model's simplicity also enables coupling with integrated assessment models and their optimization algorithms or running the model in a backward temperature-driven fashion. In spite of this simplicity, the model accurately reproduces behaviours and results from complex models – including several uncertainty ranges – when run following standardized diagnostic experiments. Pathfinder is an open-source model, and this is its first comprehensive description.

224 “OLDER PEOPLE WANT TO BE IN THEIR OWN HOMES”: THEMATIC ANALYSIS- PATIENT AND CARER FEEDBACK AFTER PATHFINDER EMERGENCY CALL RESPONSE

Abstract Background Older adults are frequent attendees at the Emergency Department (ED) and experience high rates of adverse outcomes following ED presentation. There is a growing evidence base for alternative care pathway models at the time of a low acuity emergency medical services (EMS) call. Previous research has found a high level of patient satisfaction with the Pathfinder model, in which an Advanced Paramedic and a Physiotherapist or Occupational Therapist respond to EMS calls [1]. However, the reasons underpinning this from a patient perspective have not previously been reported. Methods This is a qualitative study employing thematic analysis of open-ended responses recorded in 429 telephone interviews with service users (patients or their next-of-kin), who had been attended by the Pathfinder service following an EMS call. Results Five primary themes were identified: (1) the professionalism of the interdisciplinary clinical team; (2) “the right service, in the right place at the right time”; (3) the role of Pathfinder in “getting the ball rolling” through following up and co-ordinating referrals and services; (4) the lasting impact of the experience on the patient and their next-of-kin; (5) the value of skilled communication with the older person, as well as smooth and “tuned-in” communication across the team. Conclusion Older people voiced a clear preference for hospital avoidance, and strongly valued the opportunity to be assessed and treated in their homes at the time of an EMS call rather than automatic conveyance to the ED. They recognised the value of a skilled interdisciplinary team, with a follow-up service, that effectively positions itself between the acute hospital and community services. Reference 1. Bernard P, Corcoran G, Kenna L, et al. Is Pathfinder a safe alternative to the emergency department for older patients? An observational analysis. Age Ageing 2021; 50(5):1854–1858.

Research on Automatic Control Model of Two-wheeled Differential Robot

Abstract Aiming at the problem that the speed control of the traditional two-wheeled differential robot is cumbersome and requires a lot of manpower, the intelligent two-wheeled speed matching and intelligent path finding model of the robot under the known surrounding environment are studied. The extended method is used to process the environment image to solve the volume problem of the robot itself, and the improved PRM algorithm is used to solve the shortest path, and the post-alignment of the shortest path is distinguished. Using the radius of curvature, the speed ratio of the two-wheeled differential robot at each position can be obtained, thus establishing the automatic control of the two-wheeled differential robot. The automatic control model of two-wheeled robot is synthesized. The model can effectively reduce the manpower consumed by the robot in path finding and control, and it has important case reference significance for saving the economic cost of two-wheeled differential robot control.

A Simulation Analyzing Approach to Estimating the Probability of Airborne Infection Risks in Railway Station Platform Coupling with the Wells-Riley Model and Pathfinder Model.

Railway station platforms present a particular challenge, especially during a train departure or arrival where some passengers may have potential conditions that make them vulnerable to airborne infections due to the high density and close proximity of passengers. This study presented a simulation analyzing approach to estimating the probability of airborne infection risks in station platform spaces coupling with the Wells-Riley model and Pathfinder model. We examine the impact of overcrowded area of the station platform on infection rates under various traces of evacuation. The result of the potential risk for three modes is discussed, and the results of the standard model under the same parameter setting are optimised. Next, the impact of the ventilated volume based on uneven distribution of individuals and the exposure time based on evacuation on the infection risk in platform spaces are studied. The relationship between platform spaces overcrowding and the infection risk provided further insights to observe the supporting information.

A Data-Based Bi-Objective Approach to Explore the Accessibility of Multimodal Public Transport Networks

One of the top concerns for travelers when choosing public transportation is whether they can reach their destination in limited time and monetary cost on the basis of ensured reliability. However, the existing literature shows no studies on how to evaluate bi-objective multimodal accessibility under travel time uncertainty. In order to fill this research gap, this paper creates a multimodal super network based on smart card data in which the transfers among taxi, bus, and subway modes are developed and applied. Next, we propose a two-stage opportunity accessibility model to calculate bi-objective multimodal accessibility under travel time uncertainty. Then we propose a multimodal reliability path finding model and a reliability boundary convergence algorithm to solve this problem. Finally, we conduct a large-scale real-world case study. It is found that the impedance heterogeneity between different modes is significant, and multimodal travel has better accessibility than a unimodal one. Although multimodal accessibility decreases as the reliability increases, the advantage of multimodal over unimodal accessibility increases with reliability, and it can be improved up to 14.61% by multimodal transfers. This model can effectively guide traffic management departments to improve traffic accessibility in terms of time and cost and advise commuters to choose living places.

Evacuation trials from a double-deck electric train unit: Experimental data and sensitivity analysis

Passenger trains represent a challenging environment in emergencies, with specific evacuation conditions resulting from the typical layout and interior design inherent to public transportation vehicles. This paper describes a dataset obtained in a full-scale controlled experiment emulating the emergency evacuation of a double-deck electric unit railcar carried out in Prague in 2018. 15 evacuation trials involving 91 participants were conducted under various evacuation scenarios considering different compositions of passenger crowd, exit widths, and exit types (egress to a high platform, to an open rail line using stairs, and a 750 mm jump without any supporting equipment). The study’s main goals were to collect experimental data on the movement conditions in the railcar and to study the impact of various boundary conditions on evacuation process and total evacuation time. Movement characteristics (exit flows, speeds) and human behaviour (pre-movement activities, exiting behaviours) were also analysed. The data obtained was used to validate and adjust a Pathfinder model to capture important aspects of evacuation from the railcar. Furthermore, a series of simulations using this model was performed to provide sensitivity analysis of the influence of crowd composition, exit width, and exit type on total evacuation time. As a key finding, we can conclude that for the case of a standard exit path (platform or stairs) the width of the main exit had the greatest impact on total evacuation time, however, crowd composition played the prevailing role in evacuation scenarios involving a jump.

Is Pathfinder a safe alternative to the emergency department for older patients? An observational analysis.

many patients brought to emergency departments (EDs) following an emergency medical services (EMS) call have non-urgent needs that could be treated elsewhere. Older people are particularly vulnerable to adverse events while attending the ED. Alternative care pathway models can reduce ED crowding and improve outcomes. Internationally, there is no consensus on which model is recommended. the aim of this study is to investigate the impact of the Pathfinder model on ED conveyance rates and patient safety. the Pathfinder service is a collaboration between the National Ambulance Service and Beaumont Hospital Occupational Therapy and Physiotherapy Departments. It is supported by the Government of Ireland's Sláintecare Integration fund. This is a retrospective cohort study of the Pathfinder service over a 5-month period. one-hundred and seventy-eight patients were responded to by the Pathfinder 'Rapid Response Team'. Average age was 79.6years (standard deviation 7.6), median clinical frailty score was 6 (interquartile range: 5-6). Sixty-four percent remained at home following initial review. None re-presented to the ED within 24hours, and 10% re-presented within 7days. The majority (67%) of patients required follow-up by the Pathfinder 'Follow-Up Team' and/or another community-based service. Feedback demonstrates 99% patient satisfaction with the service. the Pathfinder service is a safe alternative to ED conveyance for older people following an EMS call. It is the first model of this kind to be evaluated in Ireland. The overwhelmingly positive feedback confirms that older people want this service. This model could expand, with local adaptation, nationally and internationally.

Toward a Holistic Approach to Verification and Validation of Autonomous Cognitive Systems

When applying formal verification to a system that interacts with the real world, we must use a model of the environment. This model represents an abstraction of the actual environment, so it is necessarily incomplete and hence presents an issue for system verification. If the actual environment matches the model, then the verification is correct; however, if the environment falls outside the abstraction captured by the model, then we cannot guarantee that the system is well behaved. A solution to this problem consists in exploiting the model of the environment used for statically verifying the system’s behaviour and, if the verification succeeds, using it also for validating the model against the real environment via runtime verification. The article discusses this approach and demonstrates its feasibility by presenting its implementation on top of a framework integrating the Agent Java PathFinder model checker. A high-level Domain Specific Language is used to model the environment in a user-friendly way; the latter is then compiled to trace expressions for both static formal verification and runtime verification. To evaluate our approach, we apply it to two different case studies: an autonomous cruise control system and a simulation of the Mars Curiosity rover.

Reversibility of computations in graph-walking automata

Graph-walking automata (GWA) are finite-state devices that traverse graphs given as an input by following their edges; they have been studied both as a theoretical notion and as a model of pathfinding in robotics. If a graph is regarded as the set of memory configurations of a certain abstract machine, then various families of devices can be described as GWA: such are two-way finite automata, their multi-head and multi-tape variants, tree-walking automata and their extension with pebbles, picture-walking automata, space-bounded Turing machines, etc. This paper defines a transformation of an arbitrary deterministic GWA to a reversible GWA. This is done with a linear blow-up in the number of states, where the constant factor depends on the degree of the graphs being traversed. The construction directly applies to all basic models representable as GWA, and, in particular, subsumes numerous existing results for making individual models halt on every input.

An optimization study based on Dijkstra algorithm for a network with trapezoidal picture fuzzy numbers

Path finding models attempt to provide efficient approaches for finding shortest paths in networks. A well-known shortest path algorithm is the Dijkstra algorithm. This paper redesigns it in order to tackle situations in which the parameters of the networks may be uncertain. To be precise, we allow that the parameters take the form of special picture fuzzy numbers. We use this concept so that it can flexibly fit the vague character of subjective decisions. The main contributions of this article are fourfold: $$\mathrm{(i)}$$ The trapezoidal picture fuzzy number along with its graphical representation and operational laws is defined. $$\mathrm{(ii)}$$ The comparison of trapezoidal picture fuzzy numbers on the basis of their expected values is proposed in terms of their score and accuracy functions. $$\mathrm{(iii)}$$ Based on these elements, we put forward an adapted form of the Dijkstra algorithm that works out a picture fuzzy shortest path problem, where the costs associated with the arcs are captured by trapezoidal picture fuzzy numbers. Also, a pseudocode for the application of our solution is provided. $$\mathrm{(iv)}$$ The proposed algorithm is numerically evaluated on a transmission network to prove its practicality and efficiency. Finally, a comparative analysis of our proposed method with the fuzzy Dijkstra algorithm is presented to support its cogency.

EdSketch: execution-driven sketching for Java

Sketching is a synthesis approach that allows users to provide high-level insights into a synthesis problem and let synthesis tools complete low-level details. Users write sketches—partial programs that have “holes” and provide test assertions as the correctness criteria. The sketching techniques fill the holes with code fragments such that the complete program satisfies all test assertions. Traditional techniques translate the sketching problem to propositional satisfiability formulas and leverage SAT solvers to generate programs with the desired functionality. While effective for a range of small well-defined domains, such translation-based approaches have a key limitation when applying to real applications: They require either translating all relevant libraries that are invoked directly or indirectly by the given sketch or creating models of those libraries, which requires much manual effort. This paper introduces execution-driven sketching, a novel approach for synthesizing Java programs with on-demand candidate generation. The key novelty of our work is to leverage runtime behavior to prune a large amount of search space. EdSketch explores the actual program behaviors in the presence of libraries and sketches small parts of real-world applications, which may use complex constructs of modern languages, such as reflection, native calls and File I/O. We further leverage a set of pruning strategies based on Java syntax to expedite the synthesis process. EdSketch embodies our approach in two forms: a stateful search based on the Java PathFinder model checker; and a stateless search based on re-execution inspired by the VeriSoft model checker. Experimental results show that EdSketch can complete some sketches that contain complex constructs in the presence of libraries, recursive procedures and advanced features like reflection. Without translating to SAT, EdSketch ’s performance compares well with the SAT-based Sketch system for a range of small but complex data structure subjects.

Multi-Target Strike Path Planning Based on Improved Decomposition Evolutionary Algorithm

This study proposes a path-finding model for multi-target strike planning. The model evaluates three elements, i.e., the target value, the aircraft’s threat tolerance, and the battlefield threat, and optimizes the striking path by constraining the balance between mission execution and the combat survival. In order to improve the speed of the Multi-Objective Evolutionary Algorithm Based on Decomposition (MOEA/D), we use the conjugate gradient method for optimization. A Gaussian perturbation is added to the search points to make their distribution closer to the population distribution. The simulation shows that the proposed method effectively chooses its target according to the target value and the aircraft’s acceptable threat value, completes the strike on high value targets, evades threats, and verifies the feasibility and effectiveness of the multi-objective optimization model.

The Midline Axon Crossing Decision Is Regulated through an Activity-Dependent Mechanism by the NMDA Receptor.

Axon guidance in vertebrates is controlled by genetic cascades as well as by intrinsic activity-dependent refinement of connections. Midline axon crossing is one of the best studied pathfinding models and is fundamental to the establishment of bilaterally symmetric nervous systems. However, it is not known whether crossing requires intrinsic activity in axons, and what controls that activity. Further, a mechanism linking neuronal activity and gene expression has not been identified for axon pathfinding. Using embryonic zebrafish, we found that the NMDA receptor (NMDAR) NR1.1 subunit (grin1a) is expressed in commissural axons. Pharmacological inhibition of grin1a, hypoxia exposure reduction of grin1a expression, or CRISPR knock-down of grin1a leads to defects in midline crossing. Inhibition of neuronal activity phenocopies the effects of grin1a loss on midline crossing. By combining pharmacological inhibition of the NMDAR with optogenetic stimulation to precisely restore neuronal activity, we observed rescue of midline crossing. This suggests that the NMDAR controls pathfinding by an activity-dependent mechanism. We further show that the NMDAR may act, via modulating activity, on the transcription factor arxa (mammalian Arx), a known regulator of midline pathfinding. These findings uncover a novel role for the NMDAR in controlling activity to regulate commissural pathfinding and identify arxa as a key link between the genetic and activity-dependent regulation of midline axon guidance.

Scalable space-time trajectory cube for path-finding: A study using big taxi trajectory data

Route planning is an important daily activity and has been intensively studied owing to their broad applications. Extracting the driving experience of taxi drivers to learn about the best routes and to support dynamic route planning can greatly help both end users and governments to ease traffic problems. Travel frequency representing the popularity of different road segments plays an important role in experience-based path-finding models and route computation. However, global frequency used in previous studies does not take into account the dynamic space-time characteristics of origins and destinations and the detailed travel frequency in different directions on the same road segment. This paper presents the space-time trajectory cube as a framework for dividing and organizing the trajectory space in terms of three dimensions (origin, destination, and time). After that, space-time trajectory cube computation and origin-destination constrained experience extraction methods are proposed to extract the fine-grained experience of taxi drivers based on a dataset of real taxi trajectories. Finally, space-time constrained graph was generated by merging drivers’ experience with the road network to compute optimal routes. The framework and methods were implemented using a taxi trajectory dataset from Shenzhen, China. The results show that the proposed methods effectively extracted the driving experience of the taxi drivers and the entailed trade-off between route length and travel time for routes with high trajectory coverage. They also indicate that road segment global frequency is not appropriate for representing driving experience in route planning models. These results are important for future research on route planning or path finding methods and their applications in navigation systems.

Pathfinder and Role Model: Ada Bloxham, Australian Vocalist and Tonic Sol-fa Teacher

The Australian mezzo-soprano Ada Beatrice Bloxham (1865–1956) was the inaugural winner (in 1883) of the Clarke Scholarship for a promising musician resident in the Colony of Victoria to study at the Royal College of Music in London. She was the first Australian to enrol at the Royal College of Music and to graduate as an Associate of the College in 1888, and she was the first woman to be awarded a Fellowship of the Tonic Sol-fa College, London, also in 1888. After a period teaching and performing in Japan (1893–1899), she married and lived variously in South Africa, England, and France, returning to Australia in 1927. Due most probably to her marriage and family responsibilities, she appears not to have achieved her full potential as a performer and teacher. Nevertheless, Bloxham is worthy of recognition as having gained success as a musician and educator both in her native Australia and abroad during her early and middle years, and as a pathfinder and role model for other women during the early years of their musical careers.