Search Results (55)

Heart disease remains one of the leading causes of mortality worldwide, creating a strong need for reliable population-scale risk prediction models for large-scale screening and preventive monitoring. However, existing machine learning and deep learning approaches often struggle under severe class imbalance, data leakage risks, and unstable precision–recall trade-offs, limiting reliability in population-scale health-monitoring settings. To address these challenges, this study proposes a precision-aware Dual-Branch FT-Transformer framework for cardiovascular risk prediction using the BRFSS-2024 dataset. The proposed architecture separates recall-oriented detection and precision-oriented verification through two specialized prediction heads and integrates them using a lightweight gating mechanism trained strictly within training folds to prevent information leakage and enable controlled error arbitration. Under a strict leakage-safe 5-fold cross-validation protocol, the proposed model achieves an F1-score of 0.43, recall of 0.59, and AUPRC of 0.38 at a fixed threshold of 0.50 while reducing false negatives by more than 50% compared to LightGBM without excessive false positives. Although some baseline models achieve higher AUROC values, the proposed framework demonstrates more balanced and clinically meaningful precision–recall behaviour at operational screening thresholds. Additional evaluation on an independent NHANES cohort under the same leakage-safe re-training protocol further suggests robustness across heterogeneous population-health settings. Overall, the proposed dual-objective learning framework provides a practical and robust approach for imbalanced tabular prediction in population-scale cardiovascular risk assessment. Full article

The rapid evolution of diffusion models has shifted visual synthesis from text-only inputs to precisely controlled generation driven by multi-source heterogeneous sensor signals (e.g., audio, 3D, and physiological data). This paper presents a systematic review of cross-modal mapping and controllable generation under multi-source collaboration. More precisely, we propose a unified “cross-modal mapping and injection” taxonomy by abstracting the intervention logic of heterogeneous signals. Fundamentally, we analyze these mechanisms in a backbone-agnostic manner, delineating the architectural transition from legacy U-Net dependencies to scalable architectures like Diffusion Transformers (DiTs) and tracing the technical evolution from single-source atomic driving to complex multi-source collaborative paradigms. Our mechanistic analysis reveals that seamless feature fusion heavily relies on gradient conflict resolution, rigorous arbitration, and dynamic disentanglement under multi-constraint scenarios. Furthermore, by systematizing current evaluation metrics, we identify intrinsic quality-controllability trade-offs through performance game analysis (e.g., Pareto optimization), yielding a scientifically grounded technical selection guide. The study concludes that overcoming current generation limitations necessitates integrating Hardware-in-the-Loop (HIL) deployment, PDE-driven physical constraints, and causal inference, laying the foundation for next-generation robust and real-time generative models. Full article

The rising market penetration of new energy vehicles (NEVs) is transforming urban traffic into a heterogeneous mix of battery electric (BEVs), hybrid electric (HEVs), and conventional fuel vehicles (FVs). For analytical brevity, traditional internal combustion engine vehicles (ICEVs) are hereafter referred to as ‘fuel vehicles (FVs)’ in the discussion of New Energy Vehicle (NEV) networks. This research investigates the efficacy of centralized coordination for NEVs within a localized region, as opposed to individualized speed control, in enhancing the mitigation of traffic congestion. Evaluating traffic efficiency and decarbonization strategies in such settings often requires extensive random sampling and Monte Carlo simulations over a large set of parameter combinations. However, conventional microscopic traffic simulators, which rely on fine-grained modeling of vehicle dynamics and signal control, incur prohibitive computational time when scaled to large networks and numerous experimental scenarios. In this study, battery electric vehicles and hybrid electric vehicles are designed as density-aware vehicles, whose movement speed is adaptively adjusted according to the regional traffic density in their vicinity and the control parameter β. In contrast, fuel vehicles adopt a stochastic movement speed and, together with other vehicle types, exhibit either movement or stoppage in the lattice environment. This density-driven speed-adaptive control and lattice arbitration mechanism is intended to reproduce, in a simplified yet extensible manner, changes in mobility and traffic-flow stability under high-density traffic conditions. The simulation results indicate that, under the same Manhattan road network and vehicle-density conditions, tuning the β parameter of new energy vehicles to reduce their movement speed in high-density areas and to mitigate abrupt position changes can suppress traffic-flow oscillations, delay the onset of the congestion phase transition, and promote spatial equilibrium of traffic flow. Meanwhile, this study develops simplified energy-consumption and carbon emission models for battery electric vehicles, hybrid electric vehicles, and fuel vehicles, demonstrating that incorporating a speed-adaptive density strategy into mixed traffic flow not only helps alleviate abnormal congestion but also reduces potential energy use and carbon emissions caused by congestion and stop-and-go behavior. From a sensing and practical perspective, the proposed framework assumes that future connected and autonomous vehicles (CAVs) can estimate vehicle states and local traffic density through GNSS–IMU multi-sensor fusion and V2X communications, indicating methodological consistency between the proposed model and real-world CAV sensing capabilities and making it a suitable and effective experimental platform for investigating the relationships among new energy vehicle penetration, density-control strategies, and carbon footprint. Full article

The classic design of the Practical Byzantine Fault Tolerance (PBFT) protocol relies on a centralized primary node, which not only creates a performance bottleneck but also introduces severe data censorship risks, threatening the data integrity and security of Edge Computing networks. To address this challenge, this paper proposes DC-PBFT (Decoupled PBFT), a censorship-resistant consensus protocol for Edge-Internet of Things (Edge-IoT) environments. The core innovation of DC-PBFT lies in the decoupling of the Proposer and Primary roles, supplemented by Verifiable Random Function (VRF)-based dynamic role rotation, which fundamentally eliminates the arbitrary power of a single node. Building on this, the protocol introduces a parallel group consensus mechanism: an elected Consensus Committee (CC) composed of Active Edge Nodes leads the consensus, while an independent Replica Network (RN) performs parallel validation. When a disagreement arises, the protocol triggers a global disagreement arbitration process involving all nodes to guarantee final consistency and attribute fault. To ensure long-term incentive compatibility, we also designed a hybrid election mechanism combining Proof-of-Stake and dynamic reputation, along with corresponding economic incentives and a tiered penalty system. Theoretical analysis proves that DC-PBFT satisfies Consistency and Liveness, and achieves strong censorship resistance guarantees. Simulation results demonstrate that DC-PBFT’s scalability significantly outperforms PBFT and RepChain; its reputation mechanism effectively improves long-term performance under sustained Byzantine attacks; and, compared to asynchronous censorship-resistant protocols like HoneyBadgerBFT, DC-PBFT achieves censorship resistance with over 45% lower transaction confirmation latency. Full article

Multi-omnidirectional mobile robot formations offer significant advantages for applications in unstructured environments. However, under constraints such as limited field of view and high operator cognitive load, existing teleoperation frameworks struggle to guarantee formation safety and stability. In this study, a bilateral shared control framework for multi-robot formation that integrates intent perception and vortex-field modulation is proposed. First, an Intent-Mediated Asymmetric Vortex Modulation (IM-AVM) strategy is developed, where the operator’s micro-intentions are mapped to determine the topological orientation of a vortex field. By constructing a dynamic asymmetric modulation matrix, saddle points in the potential field are geometrically eliminated, enabling deadlock-free obstacle avoidance while maintaining a rigid formation. Second, a multi-dimensional perception-based dynamic authority arbitration and topological deadlock escape mechanism is constructed, facilitating a seamless transition from assisted deadlock to autonomous escape. Finally, a formation coordination system based on anisotropic flow field modulation and adaptive sliding mode control is designed. Rigid formation constraints are transformed into a tangential safe flow field, and robust tracking is subsequently achieved through an Adaptive Nonsingular Fast Terminal Sliding Mode Controller (ANFTSMC). Theoretical analysis and experimental results demonstrate that the proposed framework achieves collision-free navigation for the formation in simulated environments. Full article

This paper presents a dual-core RISC-V architecture designed for energy-efficient AI acceleration at the edge, featuring dynamic MAC unit sharing, frequency scaling (DFS), and FIFO-based resource arbitration. The system comprises two RISC-V cores that compete for shared computational resources—a single Multiply–Accumulate (MAC) unit and a shared external memory subsystem—governed by a channel-based arbitration mechanism with CPU-priority semantics, while each core maintains private instruction and data caches. The architecture implements a tightly coupled Neural Processing Unit (NPU) with CONV, GEMM, and POOL operations that execute opportunistically in the background when the MAC unit is available. Dynamic frequency scaling (DFS) with three levels (100/200/400 MHz) is applied to the shared MAC unit, allowing the dynamic acceleration of CNN workloads. The arbitration mechanism uses SystemC sc_fifo channels with CPU-priority polling, ensuring that CPU execution is minimally impacted by background AI processing while the NPU makes progress during idle MAC slots. The NPU supports 3 × 3 convolutions, matrix multiplication (GEMM) with 10 × 10 tiles, and pooling operations. The implementation is cycle-accurate in SystemC, targeting FPGA deployment. Experimental evaluation demonstrates that the dual-core architecture achieves 1.87× speedup with 93.5% efficiency for parallel workloads, while DFS enables 70% power reduction at low frequency. The system successfully executes simultaneous CPU and AI workloads, with CPU-priority arbitration ensuring no CPU starvation under contention. The proposed design offers a practical solution for embedded AI applications requiring both general-purpose computation and neural network acceleration, validated through comprehensive SystemC simulation on modern FPGA platforms. Full article

Drone-based RGBT person detection facilitates critical applications such as search and rescue, owing to its high maneuverability and inherent capability to mitigate visual occlusion. However, despite the complementary nature of RGBT systems, existing detectors often overlook the specific impact of occlusion during the fusion process, leading to feature contamination and subsequent detection failures. In this work, we address this limitation by formally defining two categories of occlusion: “soft occlusion,” where targets remain partially visible in at least one modality, and “hard occlusion,” which involves complete obstruction. To tackle these challenges, we propose Unveiling Occluded Targets (UOT), a novel multi-modal fusion framework that implements a Quality–Occlusion Arbitration (QOA) mechanism. By leveraging both quality-related and occlusion-related cues, UOT dynamically arbitrates the fusion process to maximize information recovery from the clearer modality. Extensive experiments on the RGBTDronePerson and VTUAV-det datasets demonstrate significant improvements, achieving an $mA{P}_{50}^{all}$ of 53.42% and an $mA{P}_{50}^{tiny}$ of 54.70% in densely occluded scenes. Qualitative analysis further confirms UOT’s robustness in reliably identifying targets obstructed by sparse foliage. Full article

In medical endoscopic imaging, specular reflection (SR) frequently leads to local overexposure, obscuring essential tissue information and complicating computer-aided diagnosis (CAD). Traditional convolutional neural networks (CNNs) face difficulties in modeling global illumination phenomena due to their biased local receptive fields and the inherent “object assumption.” Conversely, pure transformer models often lose high-frequency boundary details and incur substantial computational costs. To tackle these challenges, this paper introduces VDTAR-Net, a specialized framework adapted to address the unique optical characteristics of specular reflections. Building upon hybrid architectures, our contribution focuses on two core mechanisms: (1) a Cross-architecture Fusion Module (CFM) that enables deep, bidirectional information flow, allowing the Transformer’s global illumination modeling to continuously correct the CNN’s local texture biases; and (2) a Reflective-Aware Module (RAM), which explicitly integrates the physical prior of high-intensity saturation into the attention mechanism. This task-specific design significantly enhances sensitivity to boundary details in overexposed regions. We also created the first large-scale, expert-labeled cervical white light segmentation dataset, Cervix-WL-900. High-quality ground truth labels were generated through rigorous double-blind annotation and arbitration by senior experts. Experimental results show that VDTAR-Net achieves a Dice score of 92.56% and a mean Intersection over Union (mIoU) score of 87.31% on Cervix-WL-900, demonstrating superior performance compared to methods like U-Net, DeepLabv3+, SegFormer, and PSPNet. Ablation studies further confirm the substantial contributions of dual-path collaboration, CFM deep fusion, and RAM task-specific priors. VDTAR-Net provides a robust baseline for precise highlight segmentation, laying a foundation for subsequent image quality assessment, restoration, and feature decoupling in diagnostic models. Full article

This article examines the conceptual convergence between papal teaching and early Christian democratic political thought on the question of war and peace, focusing on Pope Benedict XV and Luigi Sturzo. While Christian democracy is commonly analyzed through the lens of Catholic social doctrine in areas such as social policy and political organization, its underlying assumptions concerning war, peace and international order remain underexplored. The study reconstructs Benedict XV’s wartime and postwar peace teaching, highlighting his moral critique of war, his emphasis on prevention, and his advocacy of juridical and institutional mechanisms such as arbitration, disarmament, and international cooperation. These positions are then compared with Sturzo’s political and theoretical reflections, which stress the subordination of politics to moral norms, skepticism toward nationalism and statism, and support for supranational institutions as safeguards of peace. The article situates this convergence within the broader historical transformation of the papacy’s relationship to democratic politics, particularly the dismantling of the non expedit principle and the emergence of Italian Christian democracy. It argues that both figures integrate ethical normativity with realism, offering an alternative to power-centered approaches in international relations and anticipating later “just peace” paradigms in Catholic social thought. Full article

This paper extends Rodney Brooks’ subsumption architecture into the era of Agentic AI by replacing its priority arbiter with a Generative Orchestrator that performs semantic mediation—interpreting heterogeneous agent outputs and integrating them into a coherent action rather than merely arbitrating among them. Brooks’ original model (1986) demonstrated that autonomous behavior can emerge from parallel reactive layers without symbolic representation, establishing principles later recognized as foundational to agentic systems: environmental responsiveness, autonomy, and goal-directed action. Contemporary Agentic AI, however, requires capabilities beyond mechanical response—decision-making, adaptive strategy, and goal pursuit. We therefore reinterpret subsumption layers as four interacting agent types: reflex, model-based, goal-based, and utility-based, coordinated through semantic mediation. The Generative Orchestrator employs large language models not for content generation but for decision synthesis, enabling integrative agentic behavior. This approach merges real-time responsiveness with interpretive capacity for learning, reasoning, and explanation. An autonomous driving case study demonstrates how the architecture sustains behavioral autonomy while generating human-interpretable rationales for its actions. Validation was conducted through a Python-based proof-of-concept on an NVIDIA platform, reproducing the scenario to evaluate and confirm the architecture. This framework delineates a practical pathway toward advancing autonomous agents from reactive control to fully Agentic AI systems capable of operating in open, uncertain environments. Full article

In traditional power networks, security protection models primarily rely on perimeter-based defenses, utilizing firewalls, virtual private networks (VPNs), and identity authentication to block external threats. However, once a node within the power system is compromised, attackers can exploit it as a pivot to launch lateral movement attacks from within the system, posing serious threats to the core operations of the power grid. To address the increasingly complex cybersecurity landscape, this paper proposes a security defense approach that integrates an improved trust evaluation model based on the Practical Byzantine Fault Tolerance (PBFT) algorithm with a zero-trust architecture, leveraging the structural and functional symmetry among network nodes. The PBFT algorithm’s fault tolerance and consensus mechanisms are leveraged to ensure dynamic trust scoring across multiple nodes. This approach guarantees that each node has an equal role in the system’s operations, maintaining fairness and security across the network. Furthermore, the primary node in the PBFT consensus process is redefined as the arbitration node in the zero-trust framework, and faulty nodes can be automatically replaced through the view change protocol, thereby mitigating the centralization risk inherent in traditional zero-trust models. Experimental results demonstrate that the proposed approach achieves high accuracy and robustness in defending against both internal and external attacks in power network scenarios, highlighting the role of symmetry in enhancing secure and balanced system operations. Full article

The phylum Nematoda is host to a vast and diverse virosphere, yet severe viral diseases are rarely observed. This paradox between pervasive infection and limited pathology suggests the existence of a highly effective host–virus “truce”. In this review, we argue that this truce is not a result of viral attenuation but is actively arbitrated by a multi-tiered host immune network, whose primary characteristic is not destructive power but exquisite cost–benefit management. We deconstruct this network into two functional tiers. The first, the “effector layer”, comprises a diverse arsenal of antiviral pathways, including RNA interference (RNAi), the Intracellular Pathogen Response (IPR), and other direct-acting mechanisms. The second, the “regulatory layer”, acts as a command hub, integrating internal physiological states—such as metabolism and aging—with external threat signals to orchestrate a proportional defense, thereby mitigating the high fitness costs of immunity. Understanding this intricate network is critical, as it not only explains the dynamics of infection within nematodes but also has profound implications for a broader medical landscape, particularly through the “Trojan Horse” effect, where nematode-borne viruses might elicit immune responses in their final vertebrate hosts. Together, these insights provide a unified framework for studying nematode–virus interactions and for comparing antiviral strategies across metazoans. Full article

Human behavior is shaped by a pervasive motive to align with others, manifesting across a wide range of tendencies—from motor synchrony and emotional contagion to convergence in beliefs and choices. Existing accounts explain how alignment arises through predictive coding and observation–execution mechanisms, but they do not address how it is regulated in a manner that considers when alignment is adaptive and with whom it should occur. We propose a goal-directed model of social alignment that integrates computational and neural levels of analysis, to enhance our understanding of alignment as a context-sensitive decision process rather than a reflexive social tendency. Computationally, alignment is formalized as a prediction-error minimization process over the gap between self and other, augmented by a meta-learning layer in which the learning rate is adaptively tuned according to the inferred value of aligning versus maintaining independence. Assessments of the traits and mental states of self and other serve as key inputs to this regulatory function. Neurally, higher-order representations of these inputs are carried by the mentalizing network (dmPFC, TPJ), which exerts top-down control through the executive control network (dlPFC, rIFG) to enhance or inhibit alignment tendencies generated by observation–execution (mirror) circuitry. By reframing alignment as a form of social decision-making under uncertainty, the model specifies both the computations and neural circuits that integrate contextual cues to arbitrate when and with whom to align. It yields testable predictions across developmental, comparative, cognitive, and neurophysiological domains, and provides a unified framework for understanding the adaptive functions of social alignment, such as strategic social learning, as well as its maladaptive outcomes, including groupthink and false information cascades. Full article

The novel design and modular implementation of a distributed control system for a fuel cell generator, aimed at monitoring and actuation, are presented. Two ESP32 NodeMCU microcontrollers and MCP2515 modules are used for the controller area network (CAN) bus communication protocol. To compare this setup with a traditional battery management system (BMS), small rated-power fuel cell generators were connected individually via the CAN bus to form a larger stacked output. An RFID interface was introduced into the CAN bus system to enhance its applicability in stacked fuel cells, without interfering with original message frames, arbitration mechanisms, or CRC efficiency across various sectors. Additionally, to provide a clearer understanding of the system’s features and functions, a PC-based logic analyzer was employed as an analytical tool to monitor and analyze data transmitted over the CAN bus. Comprehensive insights into the system’s performance are supported by logic analysis of its complex applications in series-connected fuel cells. The advantages of the RFID-based CAN bus are further enhanced by modern communication protocols, offering greater scalability and flexibility, with potential applications in industrial automation, autonomous vehicles, and smart green power grids. Full article

High-speed switching chips are key components of network core devices in the high-performance computing paradigm, and their scheduling algorithm performance directly influences the throughput, latency, and fairness within the system. However, traditional scheduling algorithms often encounter issues such as high implementation complexity and high communication overhead when dealing with bursty traffic. To addressing the issue of bottlenecks in high-speed switching chip scheduling, we propose a low-complexity and high-performance scheduling algorithm called m-RGA, where m represents a priority mechanism. First, by monitoring the historical service time and load level of the VOQs at the port, the priority of the VOQs is dynamically adjusted to enhance the efficient matching and fair allocation of port resources. Additionally, we prove that an algorithm achieving a 2× speedup under a constant traffic model can simultaneously guarantee throughput and latency, making this algorithm theoretically as excellent as any maximum matching algorithm. Through simulation, we demonstrate that m-RGA outperforms Highest Rank First (HRF) arbitration in terms of latency under non-uniform and bursty traffic patterns. Full article