Search Results (372)

Unmanned aerial vehicles (UAVs) have made significant advances in autonomous sensing, particularly in the field of precision agriculture. Effective path planning is critical for autonomous navigation in large orchards to ensure that UAVs are able to recognize the optimal route between the start and end points. When UAVs perform tasks such as crop protection, monitoring, and data collection in orchard environments, they must be able to adapt to dynamic conditions. To address these challenges, this study proposes an enhanced Q-learning algorithm designed to optimize UAV path planning by combining static and dynamic obstacle avoidance features. A shortest distance priority (SDP) strategy is integrated into the learning process to minimize the distance the UAV must travel to reach the target. In addition, the root mean square propagation (RMSP) method is used to dynamically adjust the learning rate according to gradient changes, which accelerates the learning process and improves path planning efficiency. In this study, firstly, the proposed method was compared with state-of-the-art path planning techniques (including A-star, Dijkstra, and traditional Q-learning) in terms of learning time and path length through a grid-based 2D simulation environment. The results showed that the proposed method significantly improved performance compared to existing methods. In addition, 3D simulation experiments were conducted in the AirSim virtual environment. Due to the complexity of the 3D state, a deep neural network was used to calculate the Q-value based on the proposed algorithm. The results indicate that the proposed method can achieve the shortest path planning and obstacle avoidance operations in an orchard 3D simulation environment. Therefore, drones equipped with this algorithm are expected to make outstanding contributions to the development of precision agriculture through intelligent navigation and obstacle avoidance. Full article

In this work, we propose the general idea of using a path-finding algorithm to solve a variational problem. By interpreting a variational problem of finding the function that minimizes a functional integral as a shortest path finding, we can apply the shortest path-finding algorithm to numerically estimate the optimal function. This can be achieved by discretizing the continuous domain of the variational problem into a spatially weighted graph. The weight of each edge is defined according to the function of the original problem. We adopt the Moser lattice as the discretization scheme since it provides adjustable connections around a vertex. We find that this number of connections is crucial to the estimation of an accurate optimal path. Dijkstra’s shortest path-finding algorithm was chosen due to its simplicity and convenience in implementation. We validate our proposal by applying Dijkstra’s path-finding algorithm to numerically solve three famous variational problems, i.e., the optical ray tracing, the brachistochrone, and the catenary problems. The first two are examples of problems with no constraint. The standard Dijkstra’s algorithm can be directly applied. The third problem is an example of a problem with an isoperimetric constraint. We apply the Lagrangian relaxation technique to relax the optimization in the standard Dijkstra algorithm to incorporate the constraint. In all cases, when the number of sublattices is large enough, the results agree well with the analytic solutions. In all cases, the same path-finding code is used, regardless of the problem details. Our approaches provide more insight and promise to be more flexible than conventional numerical methods. We expect that our method can be useful in practice when an investigation of the optimal path in a complex problem is needed. Full article

Wireless sensor networks (WSNs) play a crucial role in the Internet of Things (IoT) for ubiquitous data acquisition and tracking. However, the limited battery life of sensor nodes poses significant challenges to the long-term scalability and sustainability of these networks. Wireless power transfer technology offers a promising solution by enabling the recharging of energy-depleted nodes through a wireless portable charging device (WPCD). While this approach can extend node lifespan, it also introduces the challenge of bottleneck nodes—nodes whose remaining energy falls below a critical value of the threshold. The paper addresses this issue by formulating an optimization problem that aims to identify the optimal traveling path for the WPCD based on ant colony optimization (WPCD-ACO), with a focus on minimizing energy consumption and enhancing network stability. To achieve it, we propose an objective function by incorporating a time-varying z phase that is managed through linear programming to efficiently address the bottleneck nodes. Additionally, a gateway node continually updates the remaining energy levels of all nodes and relays this information to the IoT cloud. Our findings indicate that the outage-optimal distance achieved by WPCD-ACO is 6092 m, compared to 7225 m for the shortest path and 6142 m for Dijkstra’s algorithm. Furthermore, the WPCD-ACO minimizes energy consumption to 1.543 KJ, significantly outperforming other methods: single-hop at 4.8643 KJ, GR-Protocol at 3.165 KJ, grid clustering at 2.4839 KJ, and C-SARSA at 2.5869 KJ, respectively. Monte Carlo simulations validate that WPCD-ACO is outshining the existing methods in terms of the network lifetime, stability, survival rate of sensor nodes, and energy consumption. Full article

This paper studies the security issues for cyber–physical systems, aimed at countering potential malicious cyber-attacks. The main focus is on solving the problem of extracting the most vulnerable attack path in a known attack graph, where an attack path is a sequence of steps that an attacker can take to compromise the underlying network. Determining an attacker’s possible attack path is critical to cyber defenders as it helps identify threats, harden the network, and thwart attacker’s intentions. We formulate this problem as a path-finding optimization problem with logical constraints represented by AND and OR nodes. We propose a new Dijkstra-type algorithm that combines elements from Dijkstra’s shortest path algorithm and the critical path method. Although the path extraction problem is generally NP-hard, for the studied special case, the proposed algorithm determines the optimal attack path in polynomial time, $O\left(nm\right)$, where $n$ is the number of nodes and $m$ is the number of edges in the attack graph. To our knowledge this is the first exact polynomial algorithm that can solve the path extraction problem for different attack graphs, both cycle-containing and cycle-free. Computational experiments with real and synthetic data have shown that the proposed algorithm consistently and quickly finds optimal solutions to the problem. Full article

Background/Objectives: With breast cancer (BC) survival improving due to optimized therapy, enhancing quality of life has become increasingly important. Both diagnosis and treatment, with their potential side effects, pose risks to mental well-being. Our study aimed to analyze the incidence and potential risk factors for mental disorders in BC patients. Methods: This retrospective analysis used claims data from AOK Baden-Wuerttemberg, including 11,553 BC patients diagnosed via ICD code C50 between 2010 and 2020 and 31,944 age-matched controls. Patients with mental disorders in the 12 months prior to diagnosis were excluded. Mental disorders were categorized into eight groups based on ICD codes: anxiety, obsessive compulsive disorder, adjustment disorder, dissociative disorder, hypochondriac disorder, affective disorder, mania, and other neuroses. Results: Mental disorders were significantly more common in BC patients than in controls (64.2% vs. 38.1%, p < 0.01, OR 2.91, 95%CI [2.79, 3.04]). In particular, hypochondriac, anxiety, affective, and adjustment disorders occurred significantly more often in BC patients. No differences were found for mania, bipolar disease, other neuroses, obsessive compulsive-, or dissociative disorders. Furthermore, endocrine therapy was associated with psychological comorbidities (OR 1.69, p < 0.001, 95%CI [1.53, 1.86]), while primarily metastasized patients (stage C) had a lower risk than adjuvant patients in stage A (OR 0.55, p < 0.0001, 95%CI [0.49, 0.61]). Regarding surgical treatment, mastectomy patients showed lower rates of mental illnesses (61.2%) than those with breast-conserving treatment (71.6%), or especially breast reconstruction (78.4%, p < 0.01). Breast reconstruction was also associated with more hypochondriac (p < 0.01) and adjustment disorders (p < 0.01). Conclusions: So, BC patients experience significantly more mental disorders than controls, particularly when treated with endocrine therapy and breast reconstructive surgery. Full article

In this paper, we explore the emerging role of graph neural networks (GNNs) in optimizing routing for next-generation communication networks. Traditional routing protocols, such as OSPF or the Dijkstra algorithm, often fall short in handling the complexity, scalability, and dynamic nature of modern network environments, including unmanned aerial vehicle (UAV), satellite, and 5G networks. By leveraging their ability to model network topologies and learn from complex interdependencies between nodes and links, GNNs offer a promising solution for distributed and scalable routing optimization. This paper provides a comprehensive review of the latest research on GNN-based routing methods, categorizing them into supervised learning for network modeling, supervised learning for routing optimization, and reinforcement learning for dynamic routing tasks. We also present a detailed analysis of existing datasets, tools, and benchmarking practices. Key challenges related to scalability, real-world deployment, explainability, and security are discussed, alongside future research directions that involve federated learning, self-supervised learning, and online learning techniques to further enhance GNN applicability. This study serves as the first comprehensive survey of GNNs for routing optimization, aiming to inspire further research and practical applications in future communication networks. Full article

The poor timeliness of routing has always been an urgent problem in practical operator networks, especially in situations with large-scale networks and multiple network domains. In this article, a pruning idea of routing integrated with Dijkstra’s shortest path searching is utilized to accelerate the process of routing in large-scale multi-domain elastic optical networks (EONs). The layered-graph approach is adopted in the spectrum allocation stage. To this end, an efficient heuristic algorithm is proposed, called “Branch-and-Bound based Routing and Layered Graph based Spectrum Allocation algorithm (BBR-LGSA)”, which is an integrated RSA algorithm. Notably, the significant reduction in algorithm time complexity is not only reflected in the pruning method used in the routing stage but also in the construction of auxiliary graphs during the spectrum allocation stage utilizing the Branch-and-Bound method. Simulation results show that the proposed BBR-LGSA significantly reduces the average running time by nearly 78% with higher spectrum utilization in large-scale multi-domain EONs, compared with benchmark algorithms. In addition, the impact of key parameters on performance comparisons of different algorithms is evaluated. Full article

Parvalbumins are the main source of food allergies in fish meat, with each fish possessing multiple different parvalbumins. The naming convention of these allergens in terms of allergen codes (numbers) is species-specific. Allergen codes for parvalbumin isoallergens and allergen variants are based on sequence identities relative to the first parvalbumin allergen discovered in that particular species. This means that parvalbumins with similar allergen codes, such as catfish Pan h 1.0201 and redfish Seb m 1.0201, are not necessarily the most similar proteins, or encoded by the same gene. Here, we aim to elucidate the molecular basis of parvalbumins. We explain the complicated genetics of fish parvalbumins in an accessible manner for fish allergen researchers. Teleost or modern bony fish, which include most commercial fish species, have varying numbers of up to 22 parvalbumin genes. All have derived from ten parvalbumin genes in their common ancestor. We have named these ten genes “parvalbumin 1-to-10” (PVALB1-to-PVALB10), building on earlier nomenclature established for zebrafish. For duplicated genes, we use variant names such as, for example, “PVALB2A and PVALB2B”. As illustrative examples of our gene identification system, we systematically analyze all parvalbumin genes in two common allergy-inducing species in Japan: red seabream (Pagrus major) and chum salmon (Oncorhynchus keta). We also provide gene identifications for known parvalbumin allergens in various fish species. Full article

The Single-Source Shortest Paths (SSSP) graph problem is a fundamental computation. This study attempted to characterize concretely the energy behaviors of the two primary methods to solve it, the Bellman–Ford and Dijkstra algorithms. The very different interactions of the algorithms with the hardware may have significant implications for energy. The study was motivated by the multidisciplinary nature of the problem. Gaining better insights should help vital applications in many domains. The work used reliable embedded sensors in an HPC-class CPU to collect empirical data for a wide range of sizes for two graph cases: complete as an upper-bound case and moderately dense. The findings confirmed that Dijkstra’s algorithm is drastically more energy efficient, as expected from its decisive time complexity advantage. In terms of power draw, however, Bellman–Ford had an advantage for sizes that fit in the upper parts of the memory hierarchy (up to 2.36 W on average), with a region of near parity in both power draw and total energy budgets. This result correlated with the interaction of lighter logic and graph footprint in memory with the Level 2 cache. It should be significant for applications that rely on solving a lot of small instances since Bellman–Ford is more general and is easier to implement. It also suggests implications for the design and parallelization of the algorithms when efficiency in power draw is in mind. Full article

Herein, the Dijkstra algorithm was used to develop a model that considers evacuee congestion and derives an optimal evacuation route in underground structures in the event of an earthquake. The ground conditions and seismic intensities were varied, and the evacuation route was analyzed for four cases. The damage index for each underground structure due to an earthquake was determined considering the ground conditions and structure depth, and the evacuation speed reduction was evaluated as a function of the damage index. A congestion coefficient was applied when the evacuation capacity exceeded the threshold to reflect the evacuation speed reduction due to increased congestion in the same evacuation route. The evacuation route in some sections changed when congestion was considered, and the final evacuation time increased significantly when the congestion coefficient was applied. When the evacuation capacity at each node exceeded the threshold, the 1/3 value was applied as the congestion coefficient to evacuation velocity. When the original evacuation route was used after applying the congestion coefficient, the evacuation time increased by up to 220%. However, the evacuation time can be reduced by applying an alternative route that considers congestion. When an alternative route derived from considering congestion was used, the evacuation time decreased by up to 45% compared to that when the original route was used, and the time required decreased by up to 840 s. Hence, the reduction in evacuation speed due to evacuee congestion must be considered to derive alternative, optimal evacuation routes in the event of a disaster. In addition, evacuation routes should account for the location of evacuees using technologies such as real-time indoor positioning to consider the congestion level of evacuees. Full article

Pipe routing in ship design presents significant challenges because of its time-consuming nature and the need for considerable attention to detail. In recent years, the decreasing number of pipe designers has impacted the quality of pipe-routing designs. However, optimizing pipe routing is crucial for reducing construction costs and ship production time. This study focused on optimizing the procedure and routes, considering over 500 pipes throughout the engine room of an 82,000-ton Panamax bulk carrier with three decks. Procedure optimization was based on a genetic algorithm and a system that considered individual pipe characteristics, such as type, diameter, and length. For pipe-route optimization, we used the Dijkstra algorithm, which aims to provide the shortest pipe routing by minimizing branching, bending, crossing, and obstacle avoidance. Pipe-routing optimization was divided into “basic” and “detailed” designs to derive rough and detailed routes, respectively. The basic design allowed intersections and horizontal bending, while the detailed design provided a route without intersections and minimized all bending. The optimized design was compared with pipe routing designed by diameter and shorter-path orders. In ship construction, pipe-routing optimization reduced the overall cost of pipe routing by 7% compared to other systems that typically use diameter and shortest-route orders. The study’s findings highlight the practical cost benefits and potential applications of the pipe-routing process in ship construction. Full article

Self-organizing networks of small satellites have gradually gained attention in recent years. However, self-organizing networks of small satellites have high topological change frequency, large transmission delay, and complex communication environments, which require appropriate networking and routing methods. Therefore, this paper, considering the characteristics of satellite networks, proposes the shortest queue length-cluster-based routing protocol (SQL-CBRP) and has built a satellite self-organizing network simulation platform based on OMNeT++. In this platform, functions such as the initial establishment of satellite self-organizing networks and cluster maintenance have been implemented. The platform was used to verify the latency and packet loss rate of SQL-CBRP and to compare it with Dijkstra and Greedy Perimeter Stateless Routing (GPSR). The results show that under high load conditions, the delay of SQL-CBRP is reduced by up to 4.1%, and the packet loss rate is reduced by up to 7.1% compared to GPSR. When the communication load is imbalanced among clusters, the delay of SQL-CBRP is reduced by up to 12.7%, and the packet loss rate is reduced by up to 16.7% compared to GPSR. Therefore, SQL-CBRP performs better in networks with high loads and imbalance loads. Full article

This paper explores the challenges of finding robust shortest paths in multimodal transportation networks. With the increasing complexity and uncertainties in modern transportation systems, developing efficient and reliable routing strategies that can adapt to various disruptions and modal changes is essential. By incorporating practical constraints in parameter uncertainty, this paper establishes a robust shortest path mixed-integer programming model based on a multimodal transportation network under transportation time uncertainty. To solve robust shortest path problems with multimodal transportation, we propose a modified Dijkstra algorithm that integrates parameter uncertainty with multimodal transportation. The effectiveness of the proposed multimodal transportation shortest path algorithm is verified using empirical experiments on test sets of different scales and a comparison of the runtime using a commercial solver. The experimental results on the multimodal transportation networks demonstrate the effectiveness of our approach in providing robust and efficient routing solutions. The results demonstrate that the proposed method can generate optimal solutions to the robust shortest path problem in multimodal transportation under time uncertainty and has practical significance. Full article