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Aim An assessment method that can precisely represent human‐vectored long‐distance dispersals (HVLDD) is currently in need for the effective management of invasive species. Here, we focussed on HVLDD happening along roads and proposed a path‐finding algorithm as a more precise dispersal assessment tool than the most widely used Euclidean distance method by using pine wilt disease (PWD) as a case study. Location Busan Metropolitan City, Republic of Korea. Methods A path‐finding algorithm, which calculates distances by considering the spatial distribution of road networks, was tested for its effectiveness in estimating dispersal distances of HVLDD events. To this end, annual HVLDD cases were classified from entire PWD occurrence data from 2016 to 2019, and their dispersal distances were calculated using the path‐finding algorithm and the Euclidean distance method. We constructed potential dispersal ranges based on the occurrence points in 2016, 2017 and 2018 using the respective year's mean dispersal distance for both methods, and their performances in accounting for each subsequent year's HVLDD cases were compared to determine which method calculated more precise distances. The information on which road class contributed more to dispersal occurrences and distances was analysed as well using the proposed algorithm. Results The potential dispersal ranges of the path‐finding algorithm accounted for more future anthropogenic infection cases than the ones that used the Euclidean distance method, validating its higher functionality. It also revealed that most HVLDDs started and ended on small roads, and large roads constituted the majority of the total dispersal length. Main conclusions The path‐finding algorithm has proven to be a more effective dispersal assessment method for HVLDD events. It can help design effective control strategies. Thus, we encourage using the path‐finding algorithm for the dispersal assessment of invasive species that move along road networks, and for the development of more powerful HVLDD prediction models.

Perturbation in the Dystroglycan (Dg)–Dystrophin (Dys) complex results in muscular dystrophies and brain abnormalities in human. Here we report that Drosophila is an excellent genetically tractable model to study muscular dystrophies and neuronal abnormalities caused by defects in this complex. Using a fluorescence polarization assay, we show a high conservation in Dg–Dys interaction between human and Drosophila . Genetic and RNAi‐induced perturbations of Dg and Dys in Drosophila cause cell polarity and muscular dystrophy phenotypes: decreased mobility, age‐dependent muscle degeneration and defective photoreceptor path‐finding. Dg and Dys are required in targeting glial cells and neurons for correct neuronal migration. Importantly, we now report that Dg interacts with insulin receptor and Nck/Dock SH2/SH3‐adaptor molecule in photoreceptor path‐finding. This is the first demonstration of a genetic interaction between Dg and InR.

Prioritised Planning (PP) is a popular approach for multi-agent and multi-robot navigation. In PP, collision-free paths are computed for one agent at a time, following a total order over the agents, called a priority ordering. Many MAPF algorithms follow this approach or use it in some way, including several state-of-the-art MAPF algorithms, although it is known that PP is neither complete nor optimal. In this work, we characterise the space of problems a PP algorithm can solve, and define the search problem of identifying whether a given MAPF problem is in that space. We call this search problem Prioritised MAPF (P-MAPF) and investigate its computational complexity, showing that it is generally NP-hard. Then, we develop a novel efficient search algorithm called Path and Priority Search (PaPS), which solves P-MAPF, providing guarantees of completeness and optimality. We next observe that PP algorithms operate with two primary degrees of freedom – the choice of priority ordering, and the choice of individual paths for agents. Accordingly, we further divide P-MAPF into two planning problems corresponding to the two degrees of freedom. We call them Priority-Function Constrained MAPF (PFC-MAPF), where the path choice is fixed while the priority ordering is not, and Priority Constrained MAPF (PC-MAPF), where the priority ordering is fixed while the path choice is not. We analyse these problems as well, and show how PaPS can be easily adapted to create algorithms that solve these problems optimally. We experiment with our algorithms in a range of settings, including comparisons with existing PP baselines. Our results show how the different degrees of freedom of PP-based algorithms affect their behaviour, and provide the first-known results for solution-quality optimality for PP-based algorithms on a popular MAPF benchmark set. The latter can be used as a lower bound for any PP algorithm.

This research work uses a simplified approach to combine location information from a beacon’s propagation signal interaction with a mobile device sensor (accelerometer and gyroscope) with local building information to give real-time location and guidance to a user inside a building. This is an interactive process with visualisation information that can help user’s orientation inside unknown buildings and the data stored from different users can provide useful information about users’ movements inside a public building. Beacons installed on the building at specific pre-defined positions emit signals that give a geographic position with an associated imprecision, related with Bluetooth’s range. This uncertainty is handled by building layout and users’ movement in a developed system that maps users’ position, gives guidance, and stores user movements. This system is based on an App (Find Me!) for Android OS (Operating System) which captures the Bluetooth Low Energy (BLE) signal coming from the beacon(s) and shows, through a map, the location of the user’s smartphone and guide him to the desired destination. Also, the beacons can deliver relevant context information. The application was tested by a panel of new and habitual campus users against traditional wayfinding alternatives yielding navigation times about 30% smaller, respectively.

This article introduces a novel Multi-agent path planning scheme based on Conflict Based Search (CBS) for heterogeneous holonomic and non-holonomic agents, designated as Heterogeneous CBS (HCBS). The proposed methodology employs a hybrid A ∗ algorithm for non-holonomic car-like robots and a conventional A ∗ algorithm for holonomic robots. Following this, a body conflict detection strategy is utilized to construct the conflict tree, bridging the initial path planning with the resolution of conflicts among agents. Moreover, we present two variants of HCBS: the Enhanced Conflict-Based Search (EHCBS) and the Depth-First Conflict-Based Search (DFHCBS). We evaluate the efficacy of our proposed algorithms—HCBS, EHCBS, and DFHCBS—against a standard prioritized planning algorithm, focusing on success rates and computational efficiency in environments with varying numbers of agents and obstacles. The empirical results demonstrate that EHCBS exhibits superior computational efficiency in small, dense environments, while DFHCBS performs well in larger-scale environments. This highlights the adaptability of our proposed approaches in various settings, proving the computational advantage of EHCBS and DFHCBS over traditional methods.

As the number of automated guided vehicles (AGVs) within automated container terminals (ACT) continues to rise, conflicts have become more frequent. Addressing point and edge conflicts of AGVs, a multi-AGV conflict-free path planning model has been formulated to minimize the total path length of AGVs between shore bridges and yards. For larger terminal maps and complex environments, the grid method is employed to model AGVs’ road networks. An improved bounded conflict-based search (IBCBS) algorithm tailored to ACT is proposed, leveraging the binary tree principle to resolve conflicts and employing focal search to expand the search range. Comparative experiments involving 60 AGVs indicate a reduction in computing time by 37.397% to 64.06% while maintaining the over cost within 1.019%. Numerical experiments validate the proposed algorithm’s efficacy in enhancing efficiency and ensuring solution quality.

Wireless Body Area Networks (WBANs) are an integral component of contemporary IoT-driven healthcare and can enable wearable sensors to continuously monitor a patient’s health. Despite their usefulness, routing data in WBANs is challenging because of factors such as tight energy restrictions, frequent node movement, congestion and unreliable trust between nodes. These problems often lead to lower network performance and reduced system life. Clustering techniques may be useful in enhancing energy consumption and scalability, however there are numerous pre-existing approaches, yet they still face issues such as premature convergence, unbalanced workloads and poor selection of cluster heads (CHs). To overcome those limitations, this work proposes a new QoS-aware, energy-efficient clustering-based routing scheme (QEEC-Routing) which combines three new algorithms. The Modified Raccoon Optimization (MRO) algorithm forms well-balanced clusters to distribute the energy usage more evenly. A Two-level Quaternion-Valued Recurrent Neural Network (TQV-RNN) is used to calculate adaptive trust levels to obtain more accurate CH selection. The Improved Hypercube Natural Aggregation (IHNA) algorithm then finds the most reliable routing paths, even with nodes in motion or congestion in the network. Tests conducted in NS3 simulator indicate that QEEC-Routing reduces energy consumption by 51.5%, improves packet delivery by 6.5% and increases the overall network lifetime by 14.9% as compared to current approaches. Altogether, the proposed design proposes a more reliable, energy-aware, and trust-conscious communication strategy that can be used for real-time IoT healthcare applications.

Autonomous unmanned aerial vehicles (UAVs) offer cost-effective and flexible solutions for a wide range of real-world applications, particularly in hazardous and time-critical environments. Their ability to navigate autonomously, communicate rapidly, and avoid collisions makes UAVs well suited for emergency response scenarios. However, real-time path planning in dynamic and unpredictable environments remains a major challenge, especially in confined tunnel infrastructures where accidents may trigger fires, smoke propagation, debris, and rapid environmental changes. In such conditions, conventional preplanned or model-based navigation approaches often fail due to limited visibility, narrow passages, and the absence of reliable localization signals. To address these challenges, this work proposes an end-to-end emergency response framework for tunnel accidents based on Multi-Agent Reinforcement Learning (MARL). Each UAV operates as an independent learning agent using an Independent Q-Learning paradigm, enabling real-time decision-making under limited computational resources. To mitigate premature convergence and local optima during exploration, Grey Wolf Optimization (GWO) is integrated as a policy-guidance mechanism within the reinforcement learning (RL) framework. A customized reward function is designed to prioritize victim discovery, penalize unsafe behavior, and explicitly discourage redundant exploration among agents. The proposed approach is evaluated using a frontier-based exploration simulator under both single-agent and multi-agent settings with multiple goals. Extensive simulation results demonstrate that the proposed framework achieves faster goal discovery, improved map coverage, and reduced rescue time compared to state-of-the-art GWO-based exploration and random search algorithms. These results highlight the effectiveness of lightweight MARL-based coordination for autonomous UAV-assisted tunnel emergency response.

This paper presents a framework in which satellite images are classified and augmented with additional semantic information to enable queries about what can be found on the map at a particular location, but also about paths that can be taken. This is achieved by a reasoning framework based on qualitative spatial reasoning that is able to find answers to high level queries that may vary on the current situation. This framework called SemCityMap, provides the full pipeline from enriching the raw image data with rudimentary labels to the integration of a knowledge representation and reasoning methods to user interfaces for high level querying. To illustrate the utility of SemCityMap in a disaster scenario, we use an urban environment—central Stockholm—in combination with a flood simulation. We show that the system provides useful answers to high-level queries also with respect to the current flood status. Examples of such queries concern path planning for vehicles or retrieval of safe regions such as “find all regions close to schools and far from the flooded area”. The particular advantage of our approach lies in the fact that ontological information and reasoning is explicitly integrated so that queries can be formulated in a natural way using concepts on appropriate level of abstraction, including additional constraints.

The pipe routing of ships has been manually performed by experts, and the design quality depends on the competence of the experts. Therefore, studies on pipe-routing automation and optimization are required. In addition, the pipe-routing task in a ship that requires frequent pipe-routing modifications requires a long time to be optimized. In this study, we developed a methodology that enables a rapid response in situations where frequent pipe-routing modifications are required by applying curriculum learning that can be stably learned by gradually solving easy-to-complex problems. In addition, this study aimed to minimize the length of the pipe and number of bends as an objective function. Finally, the proposed methodology was verified by comparing it with existing studies that used the A*, jump point search, and reinforcement-learning algorithms to determine the search speed, number of bends, and length of the path. Graphical Abstract Graphical Abstract