Search Results (1,005)

The tame symmetric algebras of period four, TSP4 algebras for short, form an important class of algebras, with interesting connections to various branches of modern algebra. The study of this class has been recently developed in two major directions. The first embraces new classes of examples of TSP4 algebras, such as virtual mutations and generalized weighted surface algebras, both extending the known class of weighted surface algebras. The second provides new classifications of TSP4 algebras (based on known results for 2-regular case), which handle algebras for which Gabriel quivers satisfy more general properties; the classification in biregular case is known (the biserial case is a current work in progress). An ongoing project sheds a new light on the combinatorics of such algebras, introducing a useful new tool for their classification called periodicity shadows. In this paper, we address the problem of classifying TSP4 algebras from another perspective: we provide a classification of all TSP4 algebras with not-too-big Gabriel quivers, i.e., having at most 5 vertices, but without restrictions on their structure, which differentiates it from previous classifications. The result is based on the application of the concept of the periodicity shadow, which allows all possible Gabriel quivers of such algebras to be computed (for small number of vertices) as well as on recent results concerning iterated mutations of algebras with periodic simple modules. The main result shows that TSP4 algebras with at most 5 vertices are generalized weighted surface algebras, confirming a general conjecture in this case. Full article

Airborne microplastics (AMPs) are increasingly recognized as an emerging air pollutant. However, observational data remain scarce in Southeast Asia. This study provides the first observations of AMPs in Phnom Penh, Cambodia, using µFTIR-ATR imaging. Number concentration, morphology, polymer composition, aerodynamic size distribution, Feret diameter, and surface aging characteristics were investigated together with meteorological parameters, gaseous pollutants, water-soluble ionic tracers, and HYSPLIT backward trajectories to examine possible source attribution. AMPs were dominated by polyethylene (PE), polypropylene (PP), and polyethylene terephthalate (PET), with 52% classified as fragments and 82% having Feret diameter smaller than 30 µm. Across four independent 72-h sampling periods (n = 4), AMP concentrations ranged from 0.55 to 1.27 MP m⁻³ in TSP, with a mean ± standard deviation of 0.97 ± 0.30 MP m⁻³, and from 0.23 to 0.49 MP m⁻³ in the PM_2.5 fraction, with a mean ± standard deviation of 0.33 ± 0.10 MP m⁻³. In total, 134 particles were identified in TSP, of which 46 were detected in the PM_2.5 fraction. Carbonyl and hydroxyl indices indicated that PE and PP were relatively fresh and in low-to-moderate surface aging states. Pearson correlations suggested that the abundances of individual polymers were varied differently in relation to local environmental and precipitation-related variables; however, the limited number of sampling periods precludes source or process attribution. In addition, HYSPLIT backward trajectories showed that some air masses arriving in Phnom Penh had passed over marine regions under southwest monsoon flow. These findings provide the first baseline dataset for AMP pollution in Phnom Penh, Cambodia, and highlight the combined importance of local emissions and regional atmospheric transport in Southeast Asia. Full article

To address the issue of unbalanced path allocation in multi-UUV cooperative operations under inhomogeneous ocean environments during full-coverage search missions, this paper proposes a heterogeneous UUV path planning method that integrates a hybrid waypoint deployment strategy with a dual-chromosome co-evolutionary genetic algorithm. First, heterogeneous UUVs are adaptively assigned to sub-regions according to the search value of the sea area, and a combination of Poisson sampling and Voronoi iterative refinement is adopted to complete the layout of detection points. Subsequently, connectivity-constrained K-means clustering is introduced to decompose the multi-traveling salesman problem (MTSP) into several independent TSP sub-problems. Finally, a dual-chromosome encoding scheme for task sequences and split points is designed, and a penalty matrix is incorporated into the fitness function to account for obstacle avoidance constraints, thereby establishing an integrated genetic-algorithm-based solution framework that incorporates both decomposition and obstacle avoidance. Simulation results demonstrate that the proposed method reduces the number of planned detection points by 12.4%, 12.8%, and 9.3% compared with baseline methods in circular, rectangular, and irregular sea areas, respectively, while the optimal path lengths are shortened by 5.8%, 4.5%, and 5.9%. Moreover, the cooperative mission time with four UUVs is reduced by 73.9%, 72.7%, and 71.2% relative to a single UUV, demonstrating an approximately linear speedup relative to the number of UUVs. Convergence analysis and extended experiments on 15 instances further confirm the algorithm’s solution stability and robustness under varying regional scales, shapes, and obstacle configurations. These results validate that the proposed approach not only reduces the number of deployment points and path cost, but also effectively balances obstacle avoidance and multi-robot load distribution. Full article

Background: Cancer-related pain involves complex peripheral and central mechanisms. Quantitative sensory testing (QST) assesses somatosensory function and provides insights into pain mechanisms. However, longitudinal evidence in oncological populations remains limited. This study investigated longitudinal changes in somatosensory function and their relationship with pain intensity and pain trajectory groups in a Portuguese cancer cohort. Methods: This longitudinal study was conducted within the international PAINLESS project and included participants from the Portuguese clinical unit. A total of 56 cancer patients were assessed at diagnosis (baseline, T0), before treatment, and at 6-month follow-up (T6). Pain intensity was measured using the Numeric Rating Scale (NRS), and somatosensory function was assessed using standardized thermal QST, including heat pain threshold (HPT), cold detection threshold (CDT), cold pain threshold (CPT), temporal summation of pain (TSP), and conditioned pain modulation (CPM). Non-parametric analyses evaluated longitudinal changes, correlations between pain and QST measures, differences according to clinically relevant pain (NRS < 4 vs. ≥ 4), and differences across clinically meaningful pain trajectory groups. Results: Thermal sensory thresholds changed significantly, with decreased HPT (p = 0.025) and increased CDT (p < 0.001) and CPT (p = 0.005). At T0, pain intensity was negatively correlated with CDT (r_s = − 0.371, p = 0.009), whereas at T6 it was positively correlated with CPT (r_s = 0.401, p = 0.006). Longitudinal changes in pain intensity were also associated with changes in CPT (r_s = 0.337, p = 0.029). Trajectory analysis demonstrated significant baseline CDT differences across pain trajectory groups (p = 0.028), with higher CDT values in the pain increase group than in the persistent pain group (adjusted p = 0.035). No significant longitudinal changes were observed in central pain modulatory measures (TSP and CPM). Conclusions: Altered thermal sensory processing, particularly cold-related measures, was associated with pain intensity and trajectories during the first six months after diagnosis. While central pain modulation remained stable, CDT and CPT showed consistent relationships with clinically relevant pain outcomes. These findings support the potential utility of thermal QST for characterizing sensory profiles and pain trajectory groups in cancer-related pain; being important, further longitudinal studies are needed to determine the predictive value of these measures. Full article

The Traveling Salesman Problem (TSP) and the Vehicle Routing Problem (VRP) are two classical combinatorial optimization problems. In recent years, their drone-assisted variants, the Traveling Salesman Problem with Drones (TSP-D) and the Vehicle Routing Problem with Drones (VRP-D) have attracted growing attention. Generally, these problems are solved using exact algorithms or metaheuristic algorithms. However, as the problem complexity increases and the scale of instances grows, these approaches often become less efficient. In this paper, we propose a reinforcement learning method with a shared attention encoder and a hierarchical dual-decoder architecture, where truck–drone coordination is achieved by first decoding the truck’s next node and then conditionally decoding the drone action. To further explore the solution space of large-scale instances, the proposed method adopts a multi-rollout learning strategy. We conducted experiments on large-scale TSP-D and VRP-D instances, and the results show that this model outperforms traditional metaheuristic algorithms in terms of both solution quality and computational efficiency. Full article

Human T-cell lymphotropic virus type 1 (HTLV-1) is a neglected pathogen with a heterogeneous global distribution. In low-prevalence areas, diagnostic testing is challenged by limited clinical awareness and the low positive predictive value of screening tests. We retrospectively analyzed 10,891 HTLV-1/2 test results from 7719 patient samples submitted to a specialized laboratory in Germany between 2004 and 2024. Antibody screening had a positive predictive value (PPV) of 31.7% for the Diasorin Murex HTLV I + II assay (39 of 123 reactive samples confirmed) and 34.5% for the Abbott Architect rHTLV-1/2 assay (67 of 194 reactive samples confirmed). Raising the sample/cutoff (s/co) threshold to ≥5 would have increased the PPV to >80%, with only two missed diagnoses per assay. HTLV-1 infection was confirmed in 124 carriers and HTLV-2 infection in three carriers. The vast majority of carriers originated from countries with higher endemicity. A delayed antibody response was observed in HTLV-1 infections via organ transplantation. In four patients with neurological disease, elevated HTLV-1/2-specific antibody indices showed antibody production in cerebrospinal fluid (CSF). HTLV-1 proviral load was measured in 83 carriers, with a median of 20,000 HTLV-1 DNA copies per 10⁶ cells (interquartile range, 4000–119,600). Seventy-four percent (17/23) of high-dose intravenous immunoglobulin (IVIg) preparations contained HTLV-1/2 antibodies, which can lead to false-positive HTLV-1/2 antibody screening results in recipients without underlying infection. Therefore, HTLV-1/2 testing presents two major pitfalls: false-negative results in immunosuppressed patients and false-positive results following IVIg administration. Full article

Keratoconus (KC) is a degenerative corneal disease, characterized by stromal thinning and abnormal ECM remodeling, leading to fibrosis. Corneal fibrosis is a leading cause of visual impairment. Corneal stromal keratocytes differentiate into myofibroblasts, which alters extracellular matrix (ECM) protein deposition. Nintedanib (NIN) is an antifibrotic FDA-approved tyrosine kinase inhibitor, but its function in the cornea is largely unknown. This study examined the impact of NIN within the human corneal stromal microenvironment. Healthy corneal stromal fibroblasts (HCFs) and KC fibroblasts (HKCs) in 2D and 3D in vitro cultures were treated with 1 μM or 2.5 μM NIN. Cell types were evaluated in 2D cultures for metabolic activity, viability, and migration. Protein expression of alpha-smooth muscle actin (α-SMA), collagens (COLs) 1, 3, and 5, cellular fibronectin containing extra domain A (EDA-FN), and thrombospondin-1 (TSP-1) were evaluated in 3D cultures. NIN reduced metabolic activity in HKCs without affecting cell viability. NIN reduced cell migration, downregulated COL3, COL5, EDA-FN, and TSP-1 expression in HCFs and HKCs. COL1 was upregulated in HCFs, whereas α-SMA was upregulated in HKCs. Overall, these findings demonstrate that NIN modulates corneal stromal cell migration and fibrotic marker expression, highlighting its potential as a therapeutic strategy for reducing corneal fibrosis associated with keratoconus. Full article

In the single-depot multi-traveling salesman problem, traditional depot location methods often overlook task balance among traveling salesmen, leading to excessive load on certain units and compromising overall operational efficiency. To address this issue, this paper proposes an optimized depot location method based on clustering and multi-task balancing. The core contribution lies in the design of a multi-weight adaptive depot optimization method. This approach clusters city nodes into multiple groups through cluster analysis and dynamically synthesizes direction vectors using information such as the number of samples within each cluster and the convex perimeter. It iteratively optimizes depot locations, minimizing the total path length while enhancing workload balance across all traveling salesman routes. Additionally, a “divide-and-conquer” strategy decomposes the complex MTSP into multiple parallel TSP subproblems, which are then efficiently solved using Or-Tools. A comprehensive evaluation framework is introduced, incorporating Total-Sum distance, Min-Max distance, Workload Balance, Cluster separability, Robustness, and Running time. Experimental results on the TSPLIB standard dataset demonstrate that the proposed method exhibits significant advantages over various traditional clustering algorithms in both route optimization and route balancing, validating its effectiveness and practicality. The method’s robust performance provides a reliable solution for real-world applications such as logistics distribution, further highlighting its practical value. Experimental results show that the proposed method reduces the total travel distance and improves workload balance on multiple TSPLIB instances compared with conventional depot selection baselines. Full article

Route discovery in ad hoc on-demand distance vector (AODV)-based mobile ad hoc sensor networks relies on route request (RREQ) dissemination, which improves reachability, but generates redundant rebroadcasts, channel contention, delay, and energy waste. Fixed probabilistic rebroadcasting mitigates broadcast storms, but its performance depends on a manually selected forwarding probability applied uniformly across different local redundancy conditions. This work proposes the Thompson-sampling probabilistic AODV (TSP-AODV), a lightweight adaptive extension, in which each intermediate node selects among a small set of forwarding probability arms using a local duplicate-pressure context and delayed route reply (RREP) feedback. The reward function uses the local observation of a corresponding RREP as delayed feedback while penalising high forwarding probability in locally redundant contexts. TSP-AODV requires no additional control packets, no topology exchange, and only a small number of local Beta belief distribution parameters per node. Evaluated against AODV, fixed probabilistic rebroadcasting, counter-based suppression, and dynamic probabilistic-counter suppression over 1600 simulation runs spanning four node densities and four mobility levels, TSP-AODV achieves the lowest normalised routing overhead and the highest RREQ suppression ratio, while no statistically significant PDR difference relative to the fixed probabilistic baseline was observed under the tested conditions. End-to-end delay is also reduced significantly relative to fixed probabilistic rebroadcasting. The learned belief behaviour confirms context-dependent adaptation, with the dominant forwarding-probability arm decreasing as duplicate pressure increases. An additional 800-run sensitivity analysis characterises the PDR–NRO–delay trade-off across the tested penalty coefficients and feedback-window durations in two representative scenarios. These results are limited to the evaluated parameter grid and do not establish scenario-independent parameter robustness. Full article

Combinatorial Route Optimization (CRO) problems, such as the Vehicle Routing Problem (VRP) or the Travelling Salesman Problem (TSP), are commonly seen in scheduling, logistics, and transportation. While current machine learning (ML) methods can overcome certain limitations of traditional approaches, including exact and heuristic algorithms, they typically require substantial computational resources, large training datasets, and carefully designed models, thereby limiting their scalability and practical deployment. In this paper, we develop a method to address such concerns in a data-efficient and near-training-free manner using foundation models. We select TSP, one of the most well-known combinatorial optimization problems, to solve in our experiments and employ the Tabular Prior-Data Fitted Network (TabPFN), one of the newly designed foundation models. Specifically, we develop a node-based formulation that converts TSP into a sequence of localized prediction tasks and constructs a complete route through in-context learning provided by TabPFN. The proposed method enables TabPFN, a model developed for regression and classification, to be applied to CRO problems with only one TSP sample for fine-tuning. We evaluate the proposed method across varying TSP instance sizes and demonstrate that our approach generalizes effectively without retraining, maintains competitive solution quality, and exhibits promising scalability. These findings suggest that CRO problems can be approached through foundation models, enabling scalability as well as generating rapidly deployable solutions with near-training-free adaptation. Full article

Navigation mark inspections are critical to ensuring maritime navigation safety, and the efficiency of unmanned aerial vehicle (UAV) inspection path planning directly affects inspection costs and operations. This problem is formulated as a Traveling Salesman Problem (TSP), and the traditional Grey Wolf Optimizer (GWO) has limitations in discrete optimization, including weak search capabilities, simple neighborhood structures, and poor local optimization. To address these issues, this paper proposes an improved Grey Wolf Optimizer (IGWO). First, this paper introduces three neighborhood search operators: reverse, insertion, and swap. Second, an adaptive step size mechanism based on Euclidean distance is designed. Third, the 3-opt local optimization algorithm is integrated. Finally, experiments are conducted using real navigation mark data from Pingtan and Tianjin, and IGWO is compared with traditional algorithms. Results show that IGWO effectively adapts GWO to discrete spaces and achieves optimal paths across datasets of varying scales. Its path length reduction rates improve by 0.51% to 58.01% over the other seven algorithms. These findings provide efficient UAV path planning solutions for navigation mark inspection and offer technical support for smart maritime supervision systems. Full article

This study addresses the safety and efficiency challenges of manual inspection in complex open-pit mining environments, where terrain steepness, limited coverage, and personnel exposure to hazards render conventional methods inadequate. We propose an integrated UAV inspection framework that combines 3D environmental modeling with a hierarchical optimization paradigm. The framework operates in three sequential stages. First, a high-fidelity 3D terrain model is constructed from point cloud data via skeletal feature extraction, which reduces computational complexity while preserving topographic structure. Second, an upper-layer Traveling Salesman Problem (TSP) solver determines the optimal inspection sequence across mandatory points (loading sites, dump sites, and crushing stations). Third, a lower-layer Chaotic Adaptive Population-based Grey Wolf Optimizer (CAP-GWO) refines the 3D path between consecutive TSP-ordered points, augmented by B-spline smoothing to ensure kinematic feasibility. Key inputs include: (i) raw LiDAR point cloud data of the mining site, (ii) facility coordinates and operational constraints (safety margins, maximum pitch angle, minimum turn radius), and (iii) UAV kinematic parameters. Outputs comprise a smooth, collision-free 3D trajectory with verified constraint satisfaction. Comparative experiments against eight metaheuristic algorithms (PSO, GA, ACO, BA, COA, GWO, SRA, SFOA) demonstrate that the proposed method reduces total path length by 15–20% on synthetic benchmark scenarios while maintaining zero constraint violations. Statistical validation via the Sign Test confirms the significance of these improvements (p < 0.05) across repeated independent trials. The framework is further validated on measured airborne LiDAR data of the Bingham Canyon open-pit copper mine (Utah, USA; USGS 3D Elevation Program), one of the largest operating open-pit mines in the world: on this real terrain, CAP-GWO achieves the best performance among the GWO-family algorithms, with a statistically significant 12.5% improvement over SRA (Wilcoxon p < 0.001) and 24% lower variance than the standard GWO, and all 210 experimental runs produce collision-free trajectories. Notably, the proposed hierarchical optimization framework achieves structural symmetry between the upper-layer sequencing task and the lower-layer path refinement task. This symmetric decomposition significantly reduces computational complexity while preserving solution quality, aligning with the principles of symmetry in engineering optimization. The framework offers a practical solution for autonomous, adaptive inspection scheduling in dynamic mining environments. Full article

Multi-UAV missions for remote sensing and environmental monitoring under extreme conditions require task allocation and path optimisation to efficiently distribute goals across vehicles. These methods must also be executed reliably inside an autonomous robotics framework. Several methods for the multiple travelling salesman problem (mTSP) show robust offline routeing efficiency. However, system-level validation under realistic operational conditions including waypoint management and inter-UAV separation remains limited. This research transforms the previously proposed Distance Efficient Clustering Kmeans Genetic Algorithm (DECK_GA) from an offline model into a deployment-focused multi-UAV remote sensing framework implemented in ROS2, Aerostack2, and Gazebo. A uniform waypoint management interface integrates planning, Rviz visualisation, and autonomous execution. The system combines Dynamic Centroid Kmeans (DCKmeans) for spatially coherent waypoint allocation with a Distance Efficient Genetic Algorithm (DEGA) for individual UAV route optimisation. The evaluation is conducted in a high-fidelity Antarctic environment where waypoints represent survey desired objectives in moss regions, and altitude is managed using terrain-referenced control involving two to five UAVs and 30 to 120 waypoints. The framework was evaluated against two baselines under identical mission configurations, with 10 trial runs for each: a Traditional GA Divide & Conquer planner and a Classical Kmeans DEGA planner, which utilises the same route optimisation method and differentiates the outcomes of the allocation stage. DECK_GA showed reduced mean planned and executed distances compared to the Traditional GA Divide & Conquer baseline across all configurations, achieving planned distance reductions ranging from 15.99% to 75.36%. Additionally, it produced shorter path than Classical Kmeans DEGA in 14 out of 16 configurations. The average minimum inter-UAV separation was greater than the Traditional GA Divide & Conquer baseline in 15 of the 16 configurations and higher than Classical Kmeans DEGA in 14 of the 16, which demonstrates that the DCKmeans allocation improves spatial separation. This research focuses on the framework for planning to execution instead of the introduction of a new optimisation method, as DECK_GA was proposed in previous research and is now incorporated and tested within an autonomy framework. This evaluation is simulation only. Real world flying, hardware in the loop testing, wind, communication latency, and location error prediction tend to be future developments. Full article

The wide deployment of phasor measurement units has enabled data-driven transient stability prediction (TSP) of power systems. However, ensuring the reliability of TSP results is still a significant challenge that limits the practical application of data-driven methods. To this end, a convolutional neural network (CNN)-based deep ensemble model with optimized class-specific thresholds is proposed to achieve reliable TSP. Specifically, a CNN is utilized as the backbone predictor, where the time-series variables from multiple generators are transformed into image-like inputs, and a CNN-based deep ensemble model is developed to provide accurate confidence estimation for TSP. Subsequently, considering the asymmetric importance of different classes in TSP, a confidence-based class-specific thresholds rule is adopted, and a multi-objective optimization model for determining the class-specific thresholds is formulated. In this optimization model, the reliability requirement of TSP is imposed as a constraint, requiring that true unstable rate (TUR) equal to 100%, with the objectives of minimizing the rejection rate and maximizing the true stable rate (TSR). The Pareto front of the class-specific thresholds can be obtained by solving the optimization model. Test results on two benchmark power systems show that the proposed method achieves a TUR of 100% and a TSR of at least 99% with approximately 10% of the samples rejected, demonstrating its effectiveness and scalability. Full article

Biomimetic optimization transfers biological information-processing mechanisms into computational systems. Ant colony optimization (ACO) is a canonical example: artificial ants functionally abstract pheromone-mediated stigmergy, decentralized exploration, trail decay through algorithmic evaporation, and adaptive path reinforcement. Building on this functional biological analogue, we present a controlled cross-paradigm evaluation of routing-heuristic generation. A standardized interface embeds human-designed rules, the genetic programming hyper-heuristic GHPP, a resource-constrained DeepACO-MLP proxy, and an offline ReEvo-style proxy into the same ACO solver. The methods are evaluated on held-out TSP and CVRP instances in terms of solution quality, reported generation or training cost, interpretability, and cross-scale behavior under a matched distribution. GHPP yields the shortest routes at all tested scales; the ReEvo-offline proxy and strong human-designed rules generally form a second tier, whereas the resource-constrained neural proxy degrades markedly as problem size increases. These results do not establish an intrinsic ranking of full-capability paradigms. Instead, they show that method selection depends on the operating constraint and on evidence provenance: longer locally measured offline search favors GHPP, while auditable explicit rules characterize the human and ReEvo-offline proxies. By holding the ant-inspired execution mechanism fixed and varying the source of heuristic information, the benchmark clarifies how evolutionary, neural, and LLM-style design strategies interact with a common biomimetic substrate. Full article