Search Results (1,749)

Within the payloads of JUICE and Europa Clipper, there are instruments suitable for the search of specific biosignatures that can diagnose life tracks in two ways. The payloads include mass spectrometers capable of measuring isotopic abundances for identifying life, and chromatography instruments testing whether ocean worlds harbor amphiphile mixtures, which would lead to a lipid-first origin of life. In this paper we describe how the two missions may begin to test whether there may be large detectable excursions of stable isotopes of chemical elements on the icy surfaces of the Jovian icy moons that are substantially shifted from their expected isotopic distributions. The detection of an unambiguous signal would suggest a biogenic origin, provided care is taken to exclude abiotic thermal isotopic fractionation. Our suggested tests should be confirmed independently with other techniques. Stable isotope geochemistry on the icy Jovian moons has not yet been thoroughly discussed in the literature. In addition, we enquire whether insights into life’s origin could be retrieved from Europa’s ocean and surface, including the question of the first steps in the evolution of life. Special emphasis has been put on an approach to seek on the surface of ocean worlds chemical phenomena that are rather primitive, such as reproducing lipid micelles as roots of protocells, but nevertheless can predict a path towards life with published models. Full article

Mobile robot path planning using Ant Colony Optimization (ACO) has the disadvantages of slow convergence, local optima, and unsmooth paths because of fixed heuristics and constant pheromone updating. In this paper, Self-Adaptive Risk-Aware Bidirectional updating ACO (SAR-BACO) is proposed with three improvements: (1) composite heuristic incorporating target attraction, obstacle repulsion and direction consistency to minimize early blind searching; (2) dynamic pheromone updating based on solution quality and number of iterations to balance exploration and exploitation; (3) triangular pruning to remove redundant turning points and become smoother. Theoretical analysis verifies convergence. Our experimental results on grids up to 50 × 50 demonstrate that SAR-BACO performs much better than classical and heuristic-improved algorithms with respect to the length of a path, convergence rate, smoothness and efficiency. Using SAR-BACO on a 50 × 50 map, the path lengths, convergence iterations and turning points decreased by 60.68%, 48.96%, and 96.00% respectively compared to Basic ACO (after triangular pruning, values averaged over 50 runs). The framework provides a generalizable solution to autonomous navigation with the need to consider both search efficiency and path executability. Full article

Intel Software Guard Extensions (SGX) have been widely studied and adopted in privacy-preserving information systems to enhance the security and privacy guarantees of sensitive data computation. By constructing a protected enclave within the processor, SGX provides hardware-enforced confidentiality and integrity for sensitive data and critical code. Nevertheless, due to inevitable interactions between trusted enclaves and untrusted host environments, SGX applications remain vulnerable to memory corruption attacks. Existing detection techniques exhibit fundamental limitations, including the lack of systematic induction of SGX-specific memory corruption behaviors, the absence of fine-grained parameter-level taint modeling during call-chain construction, and relatively inefficient call-chain exploration strategies over large search spaces. To address these issues, we propose ParaTaintGX, an analysis framework that integrates parameter-level taint states into vulnerability detection. ParaTaintGX constructs fine-grained call-chain nodes that capture both functions and the taint states of their parameters. It further introduces a Multi-node Heuristic Priority Search Algorithm to guide call-chain exploration. In addition, a backtracking-based pruning strategy is applied during path analysis to efficiently identify memory corruption vulnerabilities. Our evaluation demonstrates that ParaTaintGX discovers 12 vulnerabilities across 10 open-source SGX projects, outperforming the best baseline tool by two vulnerabilities. It achieves 19.35% precision, surpassing the most precise existing tool by 8.37 percentage points. These results highlight its superior detection capability and precision. Full article

In complex ground environments, conventional RRT* often suffers from low planning efficiency and poor path quality for robot path planning. This paper proposes BGSE-RRT* (Bi-tree Cooperative, Goal-guided, low-discrepancy Sampling, multi-sector Expansion). First, BGSE-RRT* constructs a nonlinear switching probability via bi-tree cooperative adaptive switching, together with KD-Tree nearest-neighbor acceleration and multi-condition triggering, to adaptively balance global exploration and local convergence. Meanwhile, a goal-guided expansion with dynamic target binding and adaptive step size, under a multi-constraint feasibility check, accelerates the convergence of the two trees. When the goal-guided expansion becomes blocked, BGSE-RRT* generates candidate points in local multi-sector regions using a 2D Halton low-discrepancy sequence and selects the best candidate for expansion; if the multi-sector expansion still fails, a sampling-point-guided expansion is activated to continue advancing and search for a feasible path. Second, B-spline smoothing is applied to improve trajectory continuity. Finally, in five simulation environments and ROS/real-robot joint validation, compared with GB-RRT*, BI-RRT*, BI-APF-RRT*, and BAI-RRT*, BGSE-RRT* reduces planning time by up to 84.71%, shortens path length by 2.94–6.88%, and improves safety distance by 20.68–48.33%. In ROS/real-robot validation, the trajectory-tracking success rate reaches 100%. Full article

Efficient UAV logistics in complex urban airspaces requires a synergistic approach to task allocation and path planning. However, traditional methods often decouple these two phases, leading to physically infeasible or sub-optimal delivery schedules. This paper proposes a Dual-Layer Optimization Framework (D-LOF) to address the Multi-UAV delivery problem in 3D urban environments. The upper layer utilizes an improved Genetic Algorithm (GA) with a specialized constraint repair operator to optimize task sequences for a heterogeneous UAV fleet. The lower layer employs an altitude-aware A* algorithm that dynamically balances vertical energy costs and horizontal cruise efficiency across multiple altitude layers. Unlike conventional models, our framework iteratively feeds precise 3D flight costs from the lower layer back to the upper layer to guide evolutionary search. Simulation results demonstrate that the D-LOF consistently achieves global convergence within 20 generations. Compared to single-altitude planning and rule-based strategies, the proposed method can reduce total operational costs and maintains zero time-window violations in high-density obstacle scenarios. This study provides a robust decision-making tool for “last-mile” urban logistics by navigating the trade-offs between 3D spatial constraints and delivery punctuality. Full article

High-dimensional global optimization and microgrid economic scheduling problems are often dominated by nonlinear search landscapes, strong coupling among decision variables, and stringent operational constraints, which severely limit the effectiveness of conventional metaheuristic approaches. In response to these challenges, this study presents a multi-strategy cooperative optimization framework derived from stock exchange trading principles, referred to as MESETO. The proposed method departs from the single-path evolutionary process of the standard SETO algorithm by introducing a diversified strategy collaboration mechanism that enables the dynamic adjustment of search behaviors throughout the optimization process. Multiple complementary update strategies are jointly employed to balance global exploration and local exploitation, while an adaptive probability regulation scheme continuously reallocates computational effort toward strategies that demonstrate superior performance. In addition, a solution validation mechanism is incorporated to prevent population degradation by rejecting ineffective evolutionary moves, thereby enhancing convergence stability. Extensive numerical experiments conducted on the CEC2017 and CEC2022 benchmark suites across different dimensional configurations demonstrate that MESETO consistently achieves improved solution accuracy, faster convergence, and stronger robustness compared with several representative state-of-the-art metaheuristic algorithms. Furthermore, the applicability of the proposed optimizer is verified through a 24 h microgrid economic scheduling case that integrates renewable energy sources, energy storage systems, dispatchable generators, and grid interaction. Simulation results confirm that MESETO effectively reduces operational costs while maintaining stable and efficient scheduling performance. Overall, the results indicate that MESETO constitutes a reliable and efficient optimization framework for solving complex global optimization problems and practical energy management applications. Full article

Background: Dense cross-layer connectivity can shorten gradient paths and promote feature reuse, potentially improving optimization under fixed training budgets. Objective: We test whether concatenation-based dense historical connectivity improves decoder-only autoregressive language modeling under controlled comparison protocols. Methods: We compare a standard Transformer decoder and a dense decoder on Penn Treebank and WikiText-2 under two fairness regimes: (i) a same training recipe setting with a fixed baseline and a bounded dense architectural search, and (ii) a same parameter budget setting where the dense model is resized to not exceed the baseline parameter count. Results: Dense connectivity does not consistently reduce test perplexity; on WikiText-2, the baseline remains better in both regimes, while gains on Penn Treebank are small and regime-dependent. Ablations within the dense family show that depth and feed-forward capacity are the most reliable drivers of perplexity improvements. Conclusions: Probes and attention diagnostics do not reveal a clear advantage for dense connectivity in our limited probe set, while Zipf–RQA analysis of long-form generations reveals systematic structural differences between baseline and dense outputs. Specifically, Zipf–RQA is used here as a descriptive structural probe rather than a performance metric. Full article

In agricultural automation, trajectory planning for fruit-picking robot arms must satisfy dynamic obstacle avoidance and real-time control constraints in complex orchards, forming a high-dimensional, constrained optimization problem. Due to strong nonlinearity and steep gradients, traditional planners often yield high-cost trajectories with unstable quality. This paper introduces a Reinforced Arctic Puffin Optimization (RAPO) algorithm for trajectory planning in high-dimensional, complex, constrained scenarios. RAPO improves Arctic Puffin Optimization (APO), which uses a two-stage foraging strategy but may suffer premature convergence, insufficient population diversity, and weak boundary handling. Dynamic fitness–distance balance (DFDB) adaptively coordinates exploration and exploitation. An elite-pool dynamic search strategy (DEPSS) combines t-distribution perturbation and Lévy flight to maintain diversity and enhance exploitation. A convex-lens opposition-learning boundary control method (CLOBC) improves out-of-bounds handling and reduces invalid search. Stochastic centroid opposition learning (SOBL) further suppresses premature convergence and expands coverage. On the CEC2017 benchmark (30/50/100 dimensions), RAPO outperforms nine algorithms in convergence speed and solution quality, verified by Wilcoxon and Friedman tests. In dense, narrow, and dynamic obstacle scenarios, RAPO achieves the lowest path cost, converges within 30 iterations, reduces variance, and generates smoother trajectories. This case study demonstrates RAPO’s robust mathematical performance, providing a robust and efficient framework for agricultural picking robots. Full article

Autonomous aerial refueling is a key technology for enhancing the endurance of unmanned aerial vehicles; however, the wingtip vortices generated by the tanker create a strong three-dimensional wake-vortex flow field, whose downwash and lateral airflow can impose significant rolling moments on the follower Unmanned Aerial Vehicle (UAV), posing a serious threat to flight safety. To address this issue, this study proposes an integrated framework that combines wake-vortex risk-field modeling with optimal path planning. The classical Hallock–Burnham (HB) model is first employed to predict vortex descent and lateral transport, while a two-phase model is used to characterize the temporal decay of vortex circulation. The predicted vortex parameters are then coupled with the UAV’s aerodynamic characteristics, and the rolling-moment coefficient (RMC) is introduced as a risk metric to compute its spatiotemporal distribution in three dimensions, thereby transforming the invisible wake-vortex disturbance into a visualizable and quantifiable dynamic three-dimensional risk map. On this basis, a wake-vortex-aware path-planning algorithm based on particle swarm optimization (PSO) is developed, incorporating adaptive weighting and elitist mutation strategies. A multi-objective cost function considering path length, safety, and smoothness is further constructed to search for an optimal safe path under wake-vortex influence. Simulation results indicate that, compared with the classical A* and Rapidly-Exploring Random Tree (RRT) algorithms, the proposed method reduces cumulative risk exposure by approximately 90% and 75%, respectively, while limiting the increase in path length to about 8% (significantly lower than the increases of 40% for A* and 44% for RRT). In addition, the maximum turning angle is constrained within 10°, and the computation time remains around 0.052 s, satisfying real-time requirements. These results demonstrate that the proposed method can generate safe, efficient, and dynamically feasible paths for UAV aerial refueling and provide a valuable reference for wake-vortex avoidance in similar aerospace missions. Full article

To address the tendency of the traditional Children’s Drawing Development Optimization (CDDO) algorithm to fall into local optima and converge slowly in global optimization and fire-field robot path planning, this study proposes a Multi-Strategy Enhanced Children’s Drawing Development Optimization (MECDDO) algorithm. The algorithm achieves performance improvements through three core strategies: (1) an adaptive cooperative search strategy that integrates information from the global best, worst, and random individuals and guides updates via dynamic weighting, expanding the exploration of the solution space; (2) a multi-strategy adaptive selection mechanism that constructs a pool of four differentiated strategies and dynamically adjusts selection probabilities based on strategy success rates, balancing exploration and exploitation; and (3) a global-optimum guided boundary repair strategy that reduces the loss of high-quality information from out-of-bounds solutions, enhancing local exploitation efficiency. Experiments on the CEC2017 benchmark suite demonstrate that MECDDO achieves outstanding performance across 30-, 50-, and 100-dimensional spaces. Statistical significance was evaluated using the Friedman test and Wilcoxon signed-rank test at a 0.05 significance level. The Friedman test mean rankings (M.R.) are 1.63, 2.20, and 2.70, respectively, consistently outperforming traditional CDDO (M.R. = 9.83, 9.93, 9.73, ranked 10th). Applied to mobile robot path planning, MECDDO achieves an average path length of 27.95483 in 20 × 20 grid environments (rank 1), shortening paths by 8.83% compared with CDDO (30.66212, rank 10), and 61.15516 in 40 × 40 grids (rank 1), reducing paths by 37.19% versus CDDO (97.20336, rank 9), providing trajectories free of redundant turns and convergence speeds 2–3 times faster than competing algorithms. These results validate MECDDO’s significant advantages in numerical optimization accuracy and practical robot path planning. Full article

This review systematically examines major research advances in AUV path planning over recent years, covering several mainstream methodologies: sample-based path planning (e.g., PRM and RRT along with their asymptotically optimal variants, suitable for high-dimensional space exploration), graph-search-based path planning (e.g., A-series and D-series algorithms, achieving global optimization and dynamic replanning through environmental modeling), optimization-based approaches (including artificial potential field (APF), nonlinear programming (NLP), and model predictive control (MPC), designed to satisfy stringent dynamic constraints on AUV motion), swarm intelligence-based planning methods (such as genetic algorithms and ant colony optimization), and learning-based intelligent methods (such as deep reinforcement learning (DRL) for real-time decision-making in unknown and dynamic environments). Through in-depth analysis of these methods’ principles, improvement strategies, and AUV path planning contexts, this review highlights current research trends toward hybrid cooperative planning, dynamic environmental adaptability, and high-precision trajectory optimization. Finally, the paper outlines future directions for AUV path planning, emphasizing multi-AUV collaboration and higher-level intelligent decision-making as key research priorities. Full article

To address the problems of high energy consumption and excessive navigation time in autonomous tugboat operations during cross-regional missions, an Ant Colony Optimization algorithm integrated with Potential Field Maps (PFM-ACO) is proposed. The proposed method is capable of planning routes that satisfy navigation time constraints, thereby improving navigation efficiency while minimizing voyage energy consumption. Specifically, time-based and energy-consumption-based potential field maps are constructed using ocean current data. The initial pheromone matrix and heuristic function are further redesigned to enhance target-oriented guidance. In addition, an adaptive heuristic factor based on a goal-biased strategy is introduced to strengthen the global search capability of the algorithm. Finally, the proposed PFM-ACO algorithm is compared with the A*, A*-DCE and NDACA algorithms. Experimental results demonstrate that, under navigation time constraints, the paths generated by PFM-ACO achieve both the lowest energy consumption and the highest path smoothness. Overall, the proposed algorithm outperforms the comparative methods, indicating its effectiveness and superiority in energy-efficient path planning for tugboat navigation. Full article

Efficient cross-district police dispatching is vital for timely emergency response, yet it faces complex constraints involving coupled inter-district routing, task sequencing, escort capacities, and mandatory transfers at makeshift police posts. This study formulates the Multi-district Police Dispatching and Path Planning Problem (MDPDPP) with makespan minimization. To address the problem’s hierarchical structure, we propose a Cooperative Iterated Greedy (CIG) algorithm. The problem is decomposed into district-level routing and capacity-constrained intra-district task scheduling, which are jointly optimized through a cooperative search mechanism. A capacity-aware decoding and local search strategy is further developed to capture the non-linear effects of escort capacity dynamics and mandatory detours. Computational experiments on a wide range of instances show that the proposed CIG algorithm consistently outperforms several state-of-the-art metaheuristics in terms of solution quality and robustness. Friedman statistical tests further confirm the statistical significance of the observed performance improvements, demonstrating the effectiveness of the proposed approach for complex multi-district police dispatching systems. Full article

This paper presents Regret-Guided Heuristic Decentralized Prioritized Planning with Velocity Decomposition (RG-HDP-VD), a physics-aware cooperative trajectory planning framework for heterogeneous Unmanned Aerial Vehicles (UAVs) relief delivery in post-earthquake, non-convex canyon environments. RG-HDP-VD addresses two prevalent failure modes: energy-inefficient congestion caused by ignoring time-varying payload dynamics, and the collapse of feasible sets due to strict arrival windows in fixed-speed planning. We construct a mass-augmented energy topology and use a mass-augmented energy-aware A* search to extract baseline physical metrics—path length, total energy, and unit-distance energy—for each UAV. Regret-Guided (RG) arbitration then quantifies the relative energy cost of waiting versus detouring at conflicts and grants right-of-way to heavy-load, high-cost platforms. These priorities are embedded into Heuristic Decentralized Prioritized Planning (HDP), which maintains a global spatiotemporal occupancy map and serializes planning to eliminate deadlocks. To satisfy tight time windows, Velocity Decomposition (VD) maps 4D temporal constraints into a 3D path-length feasible interval and is realized via an improved VD-TSRRT* sampling-based planner. In high-fidelity simulations, RG-HDP-VD demonstrates superior scalability over conventional methods, maintaining high success rates (up to 100%) in saturated scenarios, while reducing average planning time by ~45% and total system energy by 6.7%. Finally, real-world flight demonstrations using a heterogeneous quadrotor team validate the framework’s practical feasibility and robust hardware execution. Full article

Addressing the challenges of cooperative navigation for unmanned aerial vehicles (UAVs) in dynamic unknown environments, this paper proposes a collaborative method based on Joint Cognition and Risk Perception (JCRP). The method employs a sequential cooperative framework, where a pioneer UAV constructs a transferable environmental map, while successor UAVs integrate this prior knowledge with real-time perceptions to form a joint cognitive representation. A dynamic trust mechanism quantitatively evaluates cognitive reliability, enabling risk-aware path planning that balances safety and efficiency. Simulations and physical experiments demonstrate that JCRP reduces the path length of follower UAVs by approximately 41.39% and improves the safe decision ratio by 10.9 percentage points over baseline methods. These results validate the method’s robustness in complex scenarios, such as maze-like environments, highlighting its potential for applications in search-and-rescue. Full article