Search Results (2,853)

Port operations involving ship unloading have traditionally posed significant complexity and have proven difficult to solve optimally using exact methods. This study investigates the long continuous unloading of ships carrying liquid products, where transportation is carried out using full truckload deliveries. For the first time, this work integrates the problem of liquid-based ship unloading with full truckload vehicle routing and truck driver scheduling. The primary objective is to minimize the total transportation costs during the continuous unloading process, while satisfying extra constraints such as driver rest–break–drive regulations, time windows, a heterogeneous fleet structure, and port-specific constraints such as maintaining a minimum number of backup vehicles at the port during unloading. To address this complex problem, a route-based insertion heuristic is employed as an initial step in a column generation framework designed for exact optimization. The approach incorporates a nested label setting algorithm for column generation, enhanced with acceleration techniques involving multi-search strategies, and refined selection methods. Performance analysis, based on artificial datasets closely resembling real-world scenarios and consisting of 112 instances, demonstrates that optimality gaps below 1% can be achieved within computational times considered reasonable in the context of the existing literature, while the total number of customer nodes and the minimum number of required vehicles at the port are at most 100 and 5, respectively. Full article

Virtual networks have emerged as a promising solution for enabling diverse users to efficiently share bandwidth resources over optical network infrastructures. Despite the invention of various schemes aimed at ensuring secure isolation among virtual networks, the security of data transfer in virtual networks remains a challenging problem. To address this challenge, the concept of evolving traditional optical networks into key programmable optical networks (KPONs) has been proposed. Inspired by this, this paper delves into the establishment of secure virtual networks over KPONs, in which the information-theoretically secure keys can be supplied for ensuring the information-theoretic security of data transfer within virtual networks. A layered architecture for secure virtual network provisioning over KPONs is proposed, which leverages software-defined networking to realize the programmable control of optical-layer resources. With this architecture, a heuristic algorithm, i.e., the key adaptation-based secure virtual network provisioning (KA-SVNP) algorithm, is designed to dynamically allocate key resources based on the adaption between the key supply and key demand. To evaluate the proposed solutions, an emulation testbed is established, achieving millisecond latencies for secure virtual network establishment and deletion. Moreover, numerical simulations indicate that the designed KA-SVNP algorithm performs superior to the benchmark algorithm in terms of the success probability of secure virtual network requests. Full article

The A* algorithm is a cornerstone in mobile robot navigation. However, the traditional A* suffers from key limitations such as poor path smoothness, lack of adaptability to dynamic environments, and high computational costs in large-scale maps. This review presents a comprehensive analysis of 20 recent studies (2020–2025) on improved A* variants and their hybrid integrations with complementary algorithms. The improvements are categorized into two core strategies: (i) geometric and structural optimization, heuristic weighting and adaptive search schemes in A* algorithm, and (ii) hybrid models combining A* with local planners such as Dynamic Window Approach (DWA), Artificial Potential Field (APF), and Particle Swarm Optimization (PSO). For each group, the mathematical formulations of evaluation functions, smoothing techniques, and constraint handling mechanisms are detailed. Notably, hybrid frameworks demonstrate improved robustness in dynamic or partially known environments by leveraging A* for global optimality and local planners for real-time adaptability. Case studies with simulated grid maps and benchmark scenarios show that even marginal improvements in path length can coincide with substantial gains in safety and directional stability. This review not only synthesizes the state of the art in A*-based planning but also outlines design principles for building intelligent, adaptive, and computationally efficient navigation systems. Full article

Content delivery networks (CDNs) face steadily rising, uneven demand, straining heuristic cache replacement. Reinforcement learning (RL) is promising, but most work assumes a fully observable Markov Decision Process (MDP), unrealistic under delayed, partial, and noisy signals. We model cache replacement as a Partially Observable MDP (POMDP) and present the Miss-Triggered Cache Transformer (MTCT), a Transformer-decoder Q-learning agent that encodes recent histories with self-attention. MTCT invokes its policy only on cache misses to align compute with informative events and uses a delayed-hit reward to propagate information from hits. A compact, rank-based action set (12 actions by default) captures popularity–recency trade-offs with complexity independent of cache capacity. We evaluate MTCT on a real trace (MovieLens) and two synthetic workloads (Mandelbrot–Zipf, Pareto) against Adaptive Replacement Cache (ARC), Windowed TinyLFU (W-TinyLFU), classical heuristics, and Double Deep Q-Network (DDQN). MTCT achieves the best or statistically comparable cache-hit rates on most cache sizes; e.g., on MovieLens at $M=600$, it reaches $0.4703$ (DDQN $0.4436$, ARC $0.4513$). Miss-triggered inference also lowers mean wall-clock time per episode; Transformer inference is well suited to modern hardware acceleration. Ablations support $CL=50$ and show that finer action grids improve stability and final accuracy. Full article

This study focuses on the mathematical method developed by integrating the surrogate model as constraints for wall stability into the heuristic optimization algorithm to gain the optimum cost and ${\mathrm{CO}}_2$ emission value of the gabion retaining wall (GRW). This study also includes the comparison of optimum GRW results with optimum cantilever retaining wall (CRW) designs for different design cases. The Harmony Search Algorithm (HSA), which efficiently explores the design space and robustly reaches the optimum result in solving optimization problems, was used as the heuristic optimization algorithm. The primary construction scenario was considered as an optimization problem, which involved excavating the slope, constructing the wall, and compacting the backfill soil to minimize the cost and CO${}_2$ emissions for separate objective functions of GRW and CRW designs. Comparative results show that GRWs outperform CRWs in terms of sustainability and cost-efficiency, achieving 55% lower cost and 78% lower CO${}_2$ emissions on average, while the HSA–surrogate model provides a fast and accurate solution for geotechnical design problems. The surrogate models for sliding, overturning, and slope stability safety factors of GRW exhibited exceptional accuracy, characterized by minimal error values (MSE, RMSE, MAE, MAPE) and robust determination coefficients ($R^2\approx 0.99$), hence affirming their dependability in safety factor assessment. By integrating the surrogate model based on the statistical method into the optimization algorithm, a quick examination of the wall’s stability was performed, reducing the required computational power. Full article

Aiming at the problems of low efficiency, high energy consumption, and poor path quality during the multi-mechanism operation of bridge cranes in spatial tasks, an improved Rapidly exploring Random Tree (RRT) algorithm based on multi-strategy fusion is proposed for energy-efficient path planning. First, the improved algorithm introduces heuristic path information to guide the sampling process, enhancing the quality of sampled nodes. By defining a heuristic boundary, the search space is constrained to goal-relevant regions, thereby improving path planning efficiency. Secondly, focused sampling and reconnection strategies are adopted to significantly enhance path quality while ensuring the global convergence of the algorithm. Combined with line segment sampling and probability control strategies, the algorithm balances global exploration and local refinement, further optimizing path selection. Finally, Bezier curves are applied to smooth the generated path, markedly improving path smoothness and feasibility. Comparative experiments conducted on a constructed three-dimensional simulation platform demonstrate that, compared to other algorithms, the proposed algorithm achieves significant optimization in planning time, path cost, number of path nodes, and number of random tree nodes, while generating smoother paths. Notably, under different operational modes, this study provides a quantitative evaluation of operational efficiency and energy consumption based on energy efficiency trade-offs, offering an effective technical solution for the intelligent operation of bridge cranes. Full article

Unmanned surface vehicles (USVs) rely on multi-sensor perception, such as radar, LiDAR, GPS, and vision, to ensure safe and efficient navigation in complex maritime environments. Traditional ant colony optimization (ACO) for path planning, however, suffers from premature convergence, slow adaptation, and poor smoothness in cluttered waters, while the dynamic window approach (DWA) without global guidance can become trapped in local obstacle configurations. This paper presents a sensor-oriented hybrid method that couples an improved ACO for global route planning with an enhanced DWA for local, real-time obstacle avoidance. In the global stage, the ACO state–transition rule integrates path length, obstacle clearance, and trajectory smoothness heuristics, while a cosine-annealed schedule adaptively balances exploration and exploitation. Pheromone updating combines local and global mechanisms under bounded limits, with a stagnation detector to restore diversity. In the local stage, the DWA cost function is redesigned under USV kinematics to integrate velocity adaptability, trajectory smoothness, and goal-deviation, using obstacle data that would typically originate from onboard sensors. Simulation studies, where obstacle maps emulate sensor-detected environments, show that the proposed method achieves shorter paths, faster convergence, smoother trajectories, larger safety margins, and higher success rates against dynamic obstacles compared with standalone ACO or DWA. These results demonstrate the method’s potential for sensor-based, real-time USV navigation and collision avoidance in complex maritime scenarios. Full article

In this paper, we present a unique method to determine the level of bot contamination of web-based user agents. It is common practice for bots and robotic agents to masquerade as human-like to avoid content and performance limitations. This paper continues our previous work, using over 600 million web log entries collected from over 4000 domains to derive and generalize how the prominence of specific web browser versions progresses over time, assuming genuine human agency. Here, we introduce a parametric model capable of reproducing this progression in a tunable way. This simulation allows us to tag human-generated traffic in our data accurately. Along with the highest confidence self-tagged bot traffic, we train a Transformer-based classifier that can determine the bot contamination—a botness metric of user-agents without prior labels. Unlike traditional syntactic or rule-based filters, our model learns temporal patterns of raw and heuristic-derived features, capturing nuanced shifts in request volume, response ratios, content targeting, and entropy-based indicators over time. This rolling window-based pre-classification of traffic allows content providers to bin streams according to their bot infusion levels and direct them to several specifically tuned filtering pipelines, given the current load levels and available free resources. We also show that aggregated traffic data from multiple sources can enhance our model’s accuracy and can be further tailored to regional characteristics using localized metadata from standard web server logs. Our ability to adjust the heuristics to geographical or use case specifics makes our method robust and flexible. Our evaluation highlights that 65% of unclassified traffic is bot-based, underscoring the urgency of robust detection systems. We also propose practical methods for independent or third-party verification and further classification by abusiveness. Full article

The rapid development of generative artificial intelligence (GenAI) has revolutionized how individuals and organizations interact with technology. These systems, ranging from conversational agents to creative tools, are increasingly embedded in daily life. However, their effectiveness relies heavily on the usability of their graphical user interfaces (GUIs), which serve as the primary medium for user interaction. Moreover, the design of these interfaces must align with ethical principles such as transparency, fairness, and user autonomy to ensure responsible usage. This study evaluates the usability of GUIs for three widely-used GenAI applications, including ChatGPT (GPT-4), Gemini (1.5), and Claude (3.5 Sonnet) , using a heuristics-based and user-based testing approach (experimental-qualitative investigation). A total of 12 participants from a research organization in Australia, participated in structured usability evaluations, applying 14 usability heuristics to identify key issues and ethical concerns. The results indicate that Claude’s GUI is the most usable among the three, particularly due to its clean and minimalistic design. However, all applications demonstrated specific usability issues, such as insufficient error prevention, lack of shortcuts, and limited customization options, affecting the efficiency and effectiveness of user interactions. Despite these challenges, each application exhibited unique strengths, suggesting that while functional, significant enhancements are needed to fully support user satisfaction and ethical usage. The insights of this study can guide organizations in designing GenAI systems that are not only user-friendly but also ethically sound. Full article

Metaheuristics have proven to be effective in solving large-scale combinatorial problems by combining global exploration with local exploitation, all within a reasonably short time. The balance between these phases is crucial to avoid slow or premature convergence. We propose binary variants of the Arithmetic Optimization Algorithm for the set cover problem, integrating a two-step binarization scheme based on transfer functions with binarization rules and a greedy repair operator to ensure feasibility. We evaluate the proposed solution using forty-five instances from OR-Beasley and compare it with representative approaches, including genetic algorithms, path-relinking strategies, and Lagrangian-based heuristics. The quality of the solution is evaluated using relative percentage deviation and stability with the coefficient of variation. The results show competitive deviations and consistently low variation, confirming that our approach is a robust alternative with a solid balance between exploration and exploitation. Full article

The increasing demand for low-latency, computationally intensive vehicular applications, such as autonomous navigation and real-time perception, has led to the adoption of cloud–edge–vehicle infrastructures. These applications are often modeled as Directed Acyclic Graphs (DAGs) with interdependent subtasks, where precedence constraints enforce causal ordering while allowing concurrency. We propose a task offloading framework that decomposes applications into precedence-constrained subtasks and formulates the joint scheduling and offloading problem as a Markov Decision Process (MDP) to capture the latency–energy trade-off. The system state incorporates vehicle positions, wireless link quality, server load, and task-buffer status. To address the high dimensionality and sequential nature of scheduling, we introduce DepSchedPPO, a dependency-aware sequence-to-sequence policy that processes subtasks in topological order and generates placement decisions using action masking to ensure partial-order feasibility. This policy is trained using Proximal Policy Optimization (PPO) with clipped surrogates, ensuring stable and sample-efficient learning under dynamic task dependencies. Extensive simulations show that our approach consistently reduces task latency, energy consumption and QOS compared to conventional heuristic and DRL-based methods. The proposed solution demonstrates strong applicability to real-time vehicular scenarios such as autonomous navigation, cooperative sensing, and edge-based perception. Full article

The Capacitated Vehicle Routing Problem (CVRP) is a central challenge in combinatorial optimization, with critical applications in logistics and transportation. Traditional methods struggle with large-scale instances, due to the computational demands, while learned construction models often suffer from degraded solution quality and constraint violations. This work proposes SPORL, a Scalable Partitioning and Optimization via Reinforcement Learning framework for large-scale CVRPs. SPORL decomposes the problem using a learned partitioning strategy, followed by parallel subproblem solving, and employs a greedy decoding scheme at inference to ensure scalability for instances with up to 1000 customers. A key innovation is a context-based attention mechanism that incorporates sub-route embeddings, enabling more informed and constraint-aware partitioning decisions. Extensive experiments on benchmark datasets with up to 1000 customers demonstrated that SPORL consistently outperformed state-of-the-art learning-based baselines (e.g., AM, POMO) and achieved competitive performance relative to strong heuristics such as LKH3, while reducing inference time from hours to seconds. Ablation studies confirmed the critical role of the proposed context embedding and decoding strategy in achieving high solution quality. Full article

Cultural heritage is increasingly mobilized as a tool of international engagement, yet the diplomatic uses of heritage remain conceptually underdeveloped and analytically fragmented. This paper introduces the Heritage Diplomacy Spectrum, a multidimensional framework that maps how states and affiliated actors use heritage—both tangible and intangible—to pursue strategic, symbolic, and normative goals in cross-border contexts. Drawing on critical heritage studies, international relations, and memory politics, this study identifies six analytical dimensions (e.g., proactive vs. reactive, cultural vs. historical, strategic vs. moral) and develops seven ideal types of heritage diplomacy, ranging from soft power projection to post-dependency and corrective diplomacy. These ideal types, constructed in the Weberian tradition, serve as heuristic tools to illuminate the varied motivations and diplomatic postures underlying heritage-based engagement. A central matrix is presented to illustrate how each type aligns with different strategic logics and affective registers. This study argues that heritage diplomacy constitutes a distinct modality of heritage governance—one that transcends soft power narratives and encompasses conflict, reconciliation, symbolic redress, and identity assertion. The framework contributes both to theory-building and policy analysis, offering a diagnostic lens through which the ethical, political, and communicative dimensions of heritage diplomacy can be more systematically understood. Full article

A significant problem in cybersecurity is to accurately detect malicious network activities in real-time by analyzing patterns in socket-level packet transmissions. This challenge involves distinguishing between legitimate and adversarial behaviors while optimizing detection strategies to minimize false alarms and resource costs under intelligent, adaptive attacks. This paper presents a comprehensive framework for network security by modeling socket-level packet transmissions and extracting key features for temporal analysis. A long short-term memory (LSTM)-based anomaly detection system predicts normal traffic behavior and identifies significant deviations as potential cyber threats. Integrating this with a zero trust signaling game, the model updates beliefs about agent legitimacy based on observed signals and anomaly scores. The interaction between defender and attacker is formulated as a Stackelberg game, where the defender optimizes detection strategies anticipating attacker responses. This unified approach combines machine learning and game theory to enable robust, adaptive cybersecurity policies that effectively balance detection performance and resource costs in adversarial environments. Two baselines are considered for comparison. The static baseline applies fixed transmission and defense policies, ignoring anomalies and environmental feedback, and thus serves as a control case of non-reactive behavior. In contrast, the adaptive non-strategic baseline introduces simple threshold-based heuristics that adjust to anomaly scores, allowing limited adaptability without strategic reasoning. The proposed fully adaptive Stackelberg strategy outperforms both partial and discrete adaptive baselines, achieving higher robustness across trust thresholds, superior attacker–defender utility trade-offs, and more effective anomaly mitigation under varying strategic conditions. Full article

The study of dramatic plays has long relied on qualitative methods to analyze character interactions, making little assumption about the structural patterns of communication involved. Our approach bridges NLP and literary studies, enabling scalable, data-driven analysis of interaction patterns and power structures in drama. We propose a novel method to supplement addressee identification in tragedies using Large Language Models (LLMs). Unlike conventional Social Network Analysis (SNA) approaches, which often diminish dialogue dynamics by relying on co-occurrence or adjacency heuristics, our LLM-based method accurately records directed speech acts, joint addresses, and listener interactions. In a preliminary evaluation of an annotated multilingual dataset of 14 scenes from nine plays in four languages, our top-performing LLM (i.e., Llama3.3-70B) achieved an F1-score of $88.75\%$ (P = $94.81\%$, R = $84.72\%$), an exact match of 77.31%, and an 86.97% partial match with human annotations, where partial match indicates any overlap between predicted and annotated receiver lists. Through automatic extraction of speaker–addressee relations, our method provides preliminary evidence for the potential scalability of SNA for literary analyses, as well as insights into power relations, influence, and isolation of characters in tragedies, which we further visualize by rendering social network graphs. Full article