Search Results (42)

Efficient data dissemination to mobile sink groups with heterogeneous spatial distributions that are globally sparse but locally dense remains a critical challenge in wireless sensor networks (WSNs). To address severe energy inefficiencies in conventional single-strategy approaches, we propose an energy-efficient, strictly lightweight hybrid mobile groupcasting protocol that dynamically integrates unicasting and partial flooding. The proposed protocol eliminates in-network computational overhead by shifting the entire subgrouping burden exclusively to the data source. The source formulates data dissemination as an analytical cost minimization problem and executes a highly scalable heuristic subgrouping algorithm that operates in linear time, $\mathcal{O}\left(\right|M\left|\right)$, relative to the number of member sinks. By embedding this optimal configuration directly into the data packet header, resource-constrained intermediate sensor nodes are completely relieved from heavy clustering calculations and only need to execute simple, predefined geographic forwarding or localized flooding rules. The simulation results using the QualNet 4.0 platform validate that our source-delegated architecture significantly reduces redundant transmissions and unnecessary flooding regions. The proposed protocol achieves up to 24% and 44.5% reductions in communication energy consumption compared to conventional unicasting-based and flooding-based protocols, respectively, while maintaining reliable data delivery under realistic network dynamics. Full article

Background/Objectives: Reliable uncertainty quantification (UQ) is a prerequisite for deploying automated systems in safety-critical medical imaging workflows, yet existing approaches either sacrifice computational efficiency or provide poorly calibrated confidence estimates. We present UQ-Mamba, a lightweight architecture that embeds uncertainty quantification natively into a Mamba state space model via linearized error propagation. Methods: UQ-Mamba yields per-prediction approximate epistemic and aleatoric uncertainty estimates in a single deterministic forward pass at only 9.5% additional inference overhead. We note that these components are heuristic approximations derived under three explicit assumptions (diagonal covariance, first-order linearization, and scalar mean-activation reduction) and have not been empirically validated as true Bayesian posteriors. By propagating learnable log-variance parameters through the SSM state transition matrix, UQ-Mamba bridges the gap between parameter efficiency and principled calibration without requiring stochastic sampling or multiple forward passes. Results: Evaluated across four medical imaging modalities—CT organ classification, colorectal histopathology, dermoscopy, and chest radiography—UQ-Mamba achieves 89.71% accuracy with ECE = 0.0217 on OrganMNIST using only 466K parameters ($3.3\times$ lower ECE than ResNet-50 at $50\times$ fewer parameters; note that UQ-Mamba optimizes NLL, whereas ResNet-50 uses standard cross-entropy, which is a confounding factor in the ECE comparison), improves the Mamba baseline by 2.42 percentage points on PathMNIST (ECE = 0.1188 after temperature scaling), achieves 68.88% test accuracy with ECE = 0.0597 on HAM10000 dermoscopy (matching EfficientNet-B0 at $9\times$ fewer parameters), and reaches mAUC = 0.8196 on CheXpert chest radiographs. Conclusions: Ablation studies confirm that the SSM propagation mechanism is necessary for meaningful uncertainty decomposition. These results establish uncertainty-aware SSMs as a promising proof-of-concept direction for calibrated, parameter-efficient medical image classification, with potential relevance to resource-constrained deployment settings pending further clinical validation. Full article

In response to the challenges associated with suboptimal route efficiency, insufficient environmental adaptability as well as unsmooth paths in global path planning for mobile robots using the conventional A* algorithm, this paper introduces an adaptive A* algorithm. Initially, an adaptive estimation function is put forward by utilizing the positional relationship between the robot’s current and target position. Through tuning the coefficients with the heuristic function dynamically, path generation time is curtailed. Subsequently, the distance function model is optimized. The arithmetic mean of Euclidean distance and Manhattan distance is utilized to enhance the algorithm’s adaptability to diverse environmental maps. Ultimately, the redundant point deletion strategy is implemented to remove unnecessary nodes along the route, thereby enhancing path smoothness. Experimental results show that across three varying maps, the proposed algorithm, relative to the conventional A* algorithm, on average achieves a 69% reduction in path generation time, a decrease in path length of 2.66 m, and a decline in the quantity of mean steering angles exceeding or equaling 45 degrees of 38.1%. Moreover, when compared with several classic A* algorithm variants and recent improved algorithms, the proposed approach is capable of generating the shortest and most smooth path, confirming its superior planning performance while fulfilling both efficiency and smoothness demands. Full article

In recent years, reinforcement learning (RL) has been applied to frontier-based exploration to enhance a robot’s decision-making policy and improve exploration performance. In this work, we address this scenario with the aim of pushing forward the finding of the optimal frontier selection policy in unknown, unstructured environments, with RL deployed for a minimal sensing drone setup. We propose a novel policy architecture, featuring an attention module that uses the global map features captured by a convolutional neural network together with local frontier features in the form of scalar values, trained end-to-end with a scoring network using the Proximal Policy Optimization algorithm over a 2D randomized unstructured environment. Our approach demonstrates improved exploration efficiency in the evaluated scenarios, as it surpasses purely heuristic-based frontier selection strategies used as baselines for other RL methods, achieving shorter paths than the Nearest Frontier, the Hybrid Approach, and the TARE local horizon, as well as one-shot sim-to-real policy deployment. Full article

School physical education (PE) has often relied on linear progressions in which teachers demonstrate, pupils practise prescribed techniques, and achievement is judged through visible reproduction of preferred movement forms. Non-linear pedagogy (NLP) and the constraints-led approach (CLA) offer an alternative ecological-dynamics rationale for supporting pupils’ integral development, including motor competence, adaptable movement capability, and dispositions for lifelong physical activity and physical literacy. However, existing review work has not sufficiently explained why principled NLP/CLA designs remain unevenly enacted across ordinary school PE systems. We conducted a theory-informed interpretive synthesis drawing on critical interpretive synthesis and thematic synthesis. A structured English-language search of ERIC, SPORTDiscus, Scopus, and Google Scholar (2010–2025) was combined with title-and-abstract screening, full-text assessment, backward and forward citation chaining, and purposive retention of foundational or Singapore-context records, and reporting was strengthened through PRISMA-like transparency aids adapted to interpretive synthesis. The final coded corpus comprised 36 included sources: 9 empirical studies, 3 reviews, 9 conceptual or practitioner texts, 6 theoretical or critical sources, 4 review-method papers, and 5 Singapore policy, context, or professional-learning documents used as an illustrative policy lens. Through iterative coding, descriptive theme development, and analytical integration, we identified six coherence domains shaping enactment: teacher beliefs and knowledge; curriculum and lesson structure; assessment and accountability; systemic and resource constraints; professional development ecosystems; and stakeholder and cultural factors. These domains informed a Systemic Coherence Framework spanning micro, meso, and macro levels. The synthesis suggests that assessment coherence may be a high-leverage condition because it links curriculum legitimacy, reporting, and teacher defensibility, but its comparative influence across domains remains a hypothesis for future empirical testing. The framework is offered as an analytic heuristic rather than a prescriptive model and is intended to help researchers, teacher educators, school leaders, and policy actors diagnose where curriculum intent, assessment language, professional learning, and organisational routines support or inhibit ecologically informed practice. Full article

Dynamic multicast routing in software-defined networks is challenging due to continuously changing network states, multicast branch coupling, and the dependency between local forwarding decisions and global multicast tree construction. Existing multicast routing approaches mainly rely on static heuristics or snapshot-based optimization, which makes them difficult to maintain routing adaptability and decision stability under dynamic network conditions. To address these limitations, this paper proposes a topology-aware deep reinforcement learning multicast routing algorithm, named Graph-structured Hierarchical Actor–Critic for Multicast Routing (GHAC-MR). Specifically, the multicast routing process is formulated as a sequential tree construction problem, where each forwarding action incrementally affects the subsequent multicast tree evolution. A graph-structured state representation mechanism is designed to encode network topology information, link resource states, and multicast branch dependencies, enabling the routing agent to capture structural correlations among multicast forwarding nodes. Furthermore, a hierarchical actor–critic learning architecture is introduced to jointly optimize multicast forwarding policies and long-term routing rewards, thereby improving routing adaptability and convergence stability in dynamic network environments. Experimental results on multiple representative network topologies demonstrate that the proposed GHAC-MR algorithm achieves superior performance in multicast acceptance ratio, resource utilization efficiency, and routing adaptability compared with representative heuristic, evolutionary, and reinforcement learning-based multicast routing schemes. Full article

Conventional colorimetric sensing methods typically rely on signal intensity at a single wavelength, often selected heuristically based on peak visual modulation. This approach overlooks the structured information embedded in full-spectrum transmission profiles, particularly in intensity-based systems where linear models may be highly effective. In this study, we experimentally demonstrate that applying a forward feature selection strategy to normalized transmission spectra, combined with linear regression and ten-fold cross-validation, yields significant improvements in predictive accuracy. Using food dye dilutions as a model system, the mean squared error was reduced from over 22,000 with a single wavelength to 3.87 using twelve selected features, corresponding to a more than 5700-fold enhancement. These results validate that full-spectrum modeling enables precise concentration prediction without requiring changes to the sensing hardware. The approach provides a proof-of-concept framework that may be extended to colorimetric assays used in medical diagnostics, environmental monitoring, and industrial analysis following broader validation with real analytes and heterogeneous sample matrices. Full article

Time-Sensitive Networking (TSN) with Cyclic Queuing and Forwarding (CQF) is a critical mechanism for ensuring deterministic forwarding. However, existing incremental schedulers typically rely on single-dimensional heuristics, which fail to address the coupled impact of traffic characteristics and spatiotemporal resource distribution. This limitation leads to suboptimal scheduling success, especially under complex topologies and high network loads. To address this, we propose TMCOA–MFS, a joint incremental scheduling framework that integrates the Triple-Mode Cooperative Optimization Algorithm (TMCOA) with a Multi-Feature Scheduling (MFS) strategy. The logic of our approach is twofold: First, to balance spatial resource distribution, we introduce the TMCOA—inspired by table-tennis offensive–defensive behaviors—to optimize path selection by minimizing port-load variance and escaping local optima through a three-mode population partition. Second, building upon the optimized spatial paths, the MFS strategy is employed to resolve temporal scheduling conflicts. By computing a composite priority score that accounts for path hops, offset configuration difficulty, and flow size, MFS enables a robust incremental offset search with integrated feasibility checking. Extensive simulations on benchmark functions and diverse TSN scenarios demonstrate that the TMCOA offers superior convergence and stability. More importantly, the integrated TMCOA–MFS framework significantly enhances scheduling success rates and load balancing, effectively overcoming the bottlenecks of high-load and topologically complex environments. Full article

The discrete element method (DEM) is widely used to simulate concrete failure, but calibrating its mesoscopic dynamic parameters is computationally expensive due to the high-dimensional parameter space. This study proposes a physics-informed active learning framework to autonomously calibrate these parameters under impact loads. An FDM-DEM coupled split Hopkinson pressure bar model is established to simulate macroscopic dynamic compressive responses. Subsequently, a Plackett–Burman experimental design reduces the parameter optimization space from 16 to 8 core dimensions. A multi-layer perceptron surrogate model is then constructed. By comparing two heuristic active sampling strategies, results indicate that a parameter priority-guided strategy incorporating physical priors significantly outperforms a mid-value exploration strategy. The proposed approach achieves coefficients of determination exceeding 0.9 for predicting multiple macroscopic dynamic indicators on an independent testing set. Building upon this forward mapping, a robust inverse parameter prediction mechanism is established, achieving a closed-loop reconstruction of 0.8662. This framework provides a reliable, data-efficient, and automated pathway for calibrating complex multiphase particulate systems. Full article

Geometric problem-solving (GPS) has been a long-standing challenge in the fields of formal mathematics and artificial intelligence. To address the limitations of unidirectional approaches, we developed a neuro-symbolic system that integrates forward and backward reasoning. The neural component employs a gating-enhanced attention network to select candidate theorems, guiding the heuristic search and pruning irrelevant branches. The symbolic component is a bidirectional solver built on FormalGeo, which performs rigorous geometric relational reasoning and algebraic computation. The neural component predicts the theorems based on the current problem state, while the symbolic component applies these theorems and updates the problem state. These two parts interact iteratively until the problem is solved. The solving process is organized as a graph structure where facts and goals serve as nodes and theorems as edges, thereby generating a human-readable solution. The proposed neuro-symbolic system achieved an 89.63% problem-solving success rate (PSSR) on the FormalGeo7K dataset, surpassing the previous best result. Full article

Instead of being a mechanism used by management in the name of productivity, it is suggested that social power theory can be combined with the extended marketing mix to empower groups of actors in organisations. These two foundational rubrics usually perpetuate the status quo, but when used with a fresh approach, they may be used to improve engagement. The question is how these two theories can be used to describe performance, connections and power relationships, and how might this change organisations for the better? This conceptual paper combines these two rubrics in a single heuristic that reflects hidden currents in organisations. Understanding how these intersections work can foster greater empowerment for actors and produce efficiencies for management. Learning how to use the points of leverage would be a significant step forward for marketing within and between organisations. Full article

In modern shipbuilding, the structural complexity of ship components and the constrained workspace make robotic grinding prone to collisions. To improve safety and stability, this paper proposes a collision-free posture optimization method for ship-component operations. First, forward and inverse kinematic models are established, and postures along the path are organized into a directed graph. Feasible postures are then identified under joint-limit and singularity constraints. Directed bounding boxes and the GJK collision detection algorithm are applied to construct a collision-free posture set. An improved A* algorithm is then introduced. It incorporates a multi-source heuristic based on joint-space geometric distance and a safety-distance penalty to compute an optimal posture sequence with minimal joint deviation. This design promotes smooth transitions between consecutive postures. Simulation results show that the proposed method avoids robot–workpiece interference in constrained environments and improves obstacle avoidance and motion smoothness. Compared with the standard A* algorithm, the proposed approach reduces search time by 15.8% and increases the minimum safety distance by nearly fivefold. Compared with a non-optimized posture sequence, cumulative joint variation is reduced by up to 92.5%. The joint amplitude range decreases by an average of 41.2%, and the standard deviation of joint fluctuations decreases by 37.8%. The proposed method provides a generalizable solution for robotic measurement, assembly, and machining in complex and confined environments. Full article

Finding the maximum common induced subgraph is a fundamental problem in computer science. Proven to be NP-hard in the 1970s, it has, nowadays, countless applications that still motivate the search for efficient algorithms and practical heuristics. In this work, we extend a state-of-the-art branch-and-bound exact algorithm with new techniques developed in the deep-learning domain, namely graph neural networks and node embeddings, effectively transforming an efficient yet uninformed depth-first search into an effective best-first search. The change enables the algorithm to find suitable solutions within a limited budget, pushing forward the method’s time efficiency and applicability on larger graphs. We evaluate the usage of the L2 norm of the node embeddings and the Cumulative Cosine Similarity to classify the nodes of the graphs. Our experimental analysis on standard graphs compares our heuristic against the original algorithm and a recently tweaked version that exploits reinforcement learning. The results demonstrate the effectiveness and scalability of the proposed approach, compared with the state-of-the-art algorithms. In particular, this approach results in improved results on over 90% of the larger graphs; this would be more challenging in a constrained industrial scenario. Full article

In the evolving field of quantum computing, optimizing Quantum Error Correction (QEC) parameters is crucial due to the varying types and amounts of physical noise across quantum computers. Traditional simulators use a forward paradigm to derive logical error rates from inputs like code distance and rounds, but this can lead to resource wastage. Adjusting QEC parameters manually with tools like STIM is often inefficient, especially given the daily fluctuations in quantum error rates. To address this, we introduce MITS, a reverse engineering tool for STIM that automatically determines optimal QEC settings based on a given quantum computer’s noise model and a target logical error rate. This approach minimizes qubit and gate usage by precisely matching the necessary logical error rate with the constraints of qubit numbers and gate fidelity. Our investigations into various heuristics and machine learning models for MITS show that XGBoost and Random Forest regressions, with Pearson correlation coefficients of $0.98$ and $0.96$, respectively, are highly effective in this context. Full article

This study addresses the two-dimensional weakly homogeneous Bin Packing Problem (2D-BPP) in the context of robotic packing, where items must be arranged in a manner feasible for robotic manipulation. Traditional heuristics for this NP-hard problem often lack adaptability across diverse datasets, while metaheuristics typically suffer from slow convergence. To overcome these limitations, we propose a novel neural-driven constructive heuristic. The method employs a population of simple feed-forward neural networks, which are trained using black-box optimization via the Covariance Matrix Adaptation Evolution Strategy (CMA-ES). The resulting neural network dynamically scores candidate placements within the constructive heuristic. Unlike conventional heuristics, the approach adapts to instance-specific characteristics without relying on predefined rules. Evaluated on datasets generated by 2DCPackGen and real-world logistic scenarios, the proposed method consistently outperforms benchmark heuristics such as MaxRects and Skyline, reducing the average number of bins required across various item types and demand ranges. The most significant improvements occur in complex instances, with up to 86% of 2DCPackGen cases yielding superior results. This heuristic offers a flexible and extremely fast, data-driven solution to the algorithm selection problem, demonstrating robustness and potential for broader application in combinatorial optimization while avoiding the scalability issues of reinforcement learning-based methods. Full article