Search Results (2,310)

In the context of cross-Strait integrated development, agricultural cooperation policies between the Chinese mainland and Taiwan are intended to serve as key instruments for integration. However, these policies frequently encounter an implementation dilemma in which higher-level authorities actively promote policy goals while grassroots governments respond primarily through symbolic actions. Existing studies have largely explained this phenomenon from static perspectives, such as resource constraints or individual motivation, but have paid insufficient attention to how defensive compliance and distorted feedback interact to sustain systemic implementation failure. To address this gap, this study adopts political systems theory and conceptualizes policy implementation as a dynamic process involving input, conversion, output, and feedback. Using a comparative case study of two counties, supported by semi-structured interviews, participant observation, and document analysis, the study examines how local governments process politically sensitive policy mandates under conditions of high political pressure and resource mismatch. The findings show that contradictory inputs create strong risk-avoidance incentives, leading local governments to adopt defensive compliance strategies during the conversion stage. Through symbolic implementation, resource diversion, and responsibility shifting, policies are translated into formally compliant but substantively hollow outputs. These symbolic outputs generate distorted feedback that conceals implementation failures and prevents higher-level authorities from making corrective adjustments, thereby trapping the policy system in a state of suspended implementation and apparent stability. Theoretically, this study extends political systems theory by revealing how defensive compliance and feedback distortion function as adaptive mechanisms that sustain system persistence while undermining substantive policy performance. Practically, it provides important insights for enhancing governance effectiveness and preventing systemic implementation failure in politically sensitive policy domains. Full article

Tracking highly maneuvering, non-cooperative UAVs poses significant challenges due to rapid and unpredictable changes in target dynamics. Under such conditions, traditional single-model filters often fail to maintain reliable state estimates, resulting in degraded tracking performance. Multiple-Model Kalman Filter ( (MMKF) approaches, including the Generalized Pseudo Bayesian (GPB1) and Interacting Multiple-Model (IMM) algorithms, improve robustness by simultaneously considering multiple candidate motion models and weighting them according to the observed target behavior. Adaptive strategies, such as ${\chi }^2$-test-based or t-test-based methods, further enhance performance by dynamically responding to changes in maneuvering patterns. This paper presents a multi-criteriacomparative assessment of four MMKF formulations–GPB1, IMM, ${\chi }^2$-test-based, and t-test-based filters– under a consistent modeling and simulation framework. Particular emphasis is placed on systematically analyzing the role of the transition probability matrix (TPM), investigating how fixed, adaptive, and TPM-free strategies affect estimation accuracy, robustness to noise, and mode-identification performance. Beyond conventional Root Mean Square Error (RMSE) metrics, the filters’ comparison is carried out through confusion matrices and dwell time analysis to highlight performance nuances and trade-offs. This allows to establish which filter formulation is preferable in different operational conditions. Full article

Material handling is an important process in open-pit mining, where trucks transport material extracted by shovels to different destinations within the mine. The decision regarding the next destination of a truck strongly influences operational efficiency. In current mining operations, this decision is typically handled by centralized dispatching systems based on predefined criteria. However, such approaches often struggle to adapt to dynamic operating conditions and rely on a central control unit, which may reduce flexibility and robustness. This paper proposes a decentralized multi-agent system for truck dispatching with reinforcement learning (MAS-TDRL). In the proposed approach, autonomous agents representing trucks, shovels, and unloading points cooperate through a negotiation mechanism based on an enhanced Contract Net Protocol to generate operational schedules. Reinforcement learning is integrated into the decision-making process of truck agents, allowing them to learn from previous negotiations and improve their participation over time. The proposed system is evaluated through simulation using scenarios based on real data from an open-pit copper mine in Chile. The results show that incorporating reinforcement learning increases the material transported per hour by approximately 18–29% compared to a multi-agent system without learning, while maintaining computation times below 10 min even in the largest scenario, which remains compatible with operational decision-making in open-pit mining contexts. Full article

Advance hydraulic supports are widely applied in deep coal mine roadways; however, inappropriate initial support force often leads to either insufficient roof control or over-support, weakening the effectiveness of bolt–cable systems. To clarify the interaction mechanism between advance passive support and active bolt–cable reinforcement, an advance roadway support model was developed using FLAC3D based on the geological conditions of the 1432 working face in the Dongtan Coal Mine. Numerical simulations were conducted by varying the initial support force from 0 to 14 MPa, and the corresponding roof displacement, bolt stress, and cable axial force responses were systematically analyzed. The results indicate that roof subsidence decreases nonlinearly with increasing support force, exhibiting a rapid suppression stage (0–10 MPa) and a stable coordination stage (10–12 MPa). Within this optimal range, load transfer from the roof to the passive support is significantly enhanced, leading to effective stress relief and homogenization in the bolt–cable system. When the support force exceeds 12 MPa, further deformation control becomes marginal, indicating a transition from cooperative load sharing to over-support. These findings reveal the staged interaction mechanism between advance passive support and active reinforcement systems, providing a quantitative basis for selecting appropriate initial support force in deep roadway engineering. Full article

Edge, fog, and cloud continuum architectures that interconnect resource-constrained devices, intermediate edge servers, and remote cloud data centers face persistent challenges in handling heterogeneous and latency-sensitive workloads while reducing energy consumption and improving resource utilization. Classical task offloading approaches either rely on static heuristics, which lack adaptability to dynamic conditions, or on metaheuristic optimizers, which often incur high computational overhead and centralized coordination. This paper proposes LITO, a lemur-inspired task offloading algorithm for edge, fog, and cloud continuum systems that models the infrastructure as a social system in which computing nodes assume distinct roles that mirror lemur social hierarchies. Building on an abstracted model of lemur group behavior, LITO incorporates two key lemur-inspired mechanisms: an energy-aware task assignment mechanism based on sun basking, a thermoregulation behavior in which lemurs seek favorable warm spots, mapped here to selecting energetically efficient execution nodes, and a cooperative scheduling policy based on huddling, group clustering under stress, mapped here to sharing load among overloaded nodes. These mechanisms are combined with a continual supervised policy-learning layer with contextual bandit feedback that refines offloading decisions from online feedback. The resulting multi-objective formulation jointly minimizes energy consumption and deadline violations while maximizing resource utilization and throughput under high-load conditions in the edge and fog segment of the continuum. Simulations under diverse workload regimes and task complexities show that LITO outperforms representative multi-objective offloading baselines in terms of energy consumption, resource utilization, latency, Service Level Agreement (SLA) violations, and throughput in congested scenarios. Full article

In complex maritime environments and scenarios with severe signal interference, unmanned surface vehicle (USV) swarms face dual challenges: unreliable GNSS signals due to interference and difficulties in accurately calibrating multi-sensor installation errors. These issues severely constrain the capability for high-precision cooperative formation operations. To address these problems, this paper proposes a cooperative localization and all-source online calibration algorithm based on a unified factor graph optimization framework. First, a tightly coupled all-source graph framework is established, integrating navigation radar, electro-optical systems (EOSs) with laser rangefinders, IMU, and GNSS into a sliding window. By leveraging high-precision mutual observations among the swarm, strong geometric constraints are constructed to mitigate the drift of individual inertial navigation systems. Second, an adaptive GNSS weighting mechanism based on signal quality and a degradation detection strategy based on eigenvalue analysis of the Fisher Information Matrix (FIM) are designed. These mechanisms enable online identification and robust estimation of extrinsic parameters, effectively resolving calibration divergence under weak excitation conditions such as straight-line sailing. Finally, the proposed algorithm is validated using field data from three USVs combined with simulated interference experiments. Results demonstrate that the algorithm can rapidly converge to high-precision calibration parameters without artificial targets (radar translation error < 0.2 m, EOS rotation error < 0.05°). During periods of simulated GNSS interference, the cooperative localization root mean square error (RMSE) is reduced to 2.85 m, representing an accuracy improvement of approximately 84.5% compared to traditional methods. This study achieves a “more accurate as it runs” cooperative navigation effect, providing reliable technical support for USV swarm applications in GNSS-denied environments. Full article

Artificial intelligence (AI) has been explored as a promising diagnostic aid for ocular surface diseases (OSDs). The spectrum of OSD ranges from highly prevalent benign conditions such as dry eye disease (DED) to rare but potentially dangerous disorders, including ocular surface squamous neoplasia (OSSN) and conjunctival melanoma. This review provides an overview of current applications of AI across the major categories of ocular surface pathology and specifically highlights anterior segment imaging modalities, including slit-lamp examination, optical coherence tomography (OCT), and in vivo confocal microscopy (IVCM). Meibography, tear film dynamics, biochemical profiling, and other DED-related measures are also examined. Across these domains, reported AI model performance matches or exceeds that of ophthalmologists, offering consistent, reproducible, and accurate approaches for guiding diagnosis. However, studies with limited external or prospective validation, variable labeling strategies, and small, device-specific datasets predominate in the current literature, thereby limiting generalizability. Large multicenter datasets, standardized diagnostic frameworks, multimodal integration, and prospective trials that assess human–AI cooperation in practical settings should be an emphasis in future research. By filling these gaps, AI systems could advance from experimental tools to clinically reliable applications that improve access and diagnostic accuracy in the care of ocular surface disease and tumors. Full article

To address the difficulty of simultaneously achieving fast response, high stability, and strong disturbance rejection in gimbal systems operating under complex conditions, an adaptive control strategy based on offline–online cooperative optimization is proposed. The method is built upon the conventional proportional–integral–derivative (PID) control framework. First, a particle swarm optimization (PSO) algorithm is employed offline to obtain an optimized set of initial control parameters, thereby improving the transient response during the startup phase. Subsequently, a single-neuron adaptive (SNA) mechanism is introduced to adjust the control parameters online according to real-time error information, enhancing the system’s adaptability to environmental variations and external disturbances. Stability analysis demonstrates the convergence of the proposed control scheme. Finally, an experimental gimbal platform is constructed to validate the effectiveness of the method. Experimental results show that, under various disturbance conditions, the proposed strategy effectively reduces angular fluctuation amplitude, shortens the settling time, and maintains smooth control performance. These results indicate that the proposed control strategy exhibits strong robustness and significant engineering applicability. Full article

Lightweight, efficient, and reversible hydrogen storage materials are critical for the advancement of hydrogen-based energy technologies. In this work, we present a comprehensive density functional theory (DFT) investigation of hydrogen storage in pristine and metal-decorated C₈ carbon quantum dots (CQDs), representing ultrasmall, highly curved nanomaterials at the molecular–nanoscale interface. Lithium, magnesium, and titanium were investigated as representative decorating metals to tailor hydrogen adsorption strength and reversibility. The pristine C₈ quantum dot is structurally stable but exhibits negligible hydrogen affinity (−0.062 eV per H₂), rendering it unsuitable for practical storage applications. In contrast, metal decoration significantly enhances hydrogen adsorption while preserving molecular H₂ physisorption, yielding optimal single-molecule adsorption energies of −0.172, −0.304, and −0.451 eV for Li-, Mg-, and Ti-CQDs, respectively. Sequential adsorption analysis indicates exceptionally high hydrogen uptakes of up to 18 H₂ molecules for Li-CQD and 20 H₂ molecules for both Mg- and Ti-CQDs, corresponding to very high theoretical gravimetric capacities. Energy decomposition and interaction region analyses demonstrate that hydrogen uptake proceeds via a cooperative physisorption mechanism driven by dispersion, electrostatic, and polarization interactions, strongly enhanced by quantum confinement and extreme curvature effects inherent to the CQD. Grand canonical thermodynamic modeling confirms fully reversible hydrogen storage under practical temperature and pressure conditions. Among the systems studied, Mg-CQD exhibits the most favorable balance between adsorption strength and desorption accessibility, delivering a remarkable reversible gravimetric hydrogen storage capacity of 21.7 wt%, significantly surpassing most metal-decorated graphene-, fullerene-, and carbon nanotube-based materials reported to date. These results establish metal-decorated C₈ quantum dots as a new class of high-performance nanomaterials for reversible hydrogen storage and demonstrate the potential of ultrasmall carbon quantum dots to overcome the long-standing trade-off between hydrogen uptake and reversibility in nanostructured storage media. Full article

This paper develops a region-specific technological transformation strategy for the automotive and mobility sector in Southeast Lower Saxony (SON) under conditions of high uncertainty driven by electrification, digitalization, and automation. The study integrates three analytical components: (i) a SWOT-based baseline assessment of SON’s current strengths, weaknesses, opportunities, and threats; (ii) a scenario-technique framework describing alternative German mobility futures toward 2035; and (iii) a two-round Delphi survey with experts from the Institutes of Automotive Engineering and Engineering Design to evaluate actionable transformation measures. SWOT factors are mapped to scenario key-factor projections and assessed using a trinary impact scale (−1/0/+1), followed by aggregation and normalization to derive scenario-specific change factors. Delphi-rated measures are then prioritized using scenario-overarching performance and SWOT relevance, yielding a tiered strategy concept. The resulting strategy is organized around five interdependent pillars: strengthening industry–research cooperation, advancing research in modern mobility, developing key mobility-support technologies (battery technology, AI, circular economy), expanding digital infrastructure, and upgrading R&D infrastructure and talent capacity, supported by enabling regulatory and workforce measures. The paper provides focus points from regional diagnosis to prioritized action, supporting robust strategic decision-making and adaptive capability building in SON. Full article

Botulinum neurotoxins (BoNTs), among the most potent biological toxins, rely on co-produced nontoxic proteins to survive harsh gastrointestinal conditions and achieve efficient systemic dissemination after oral exposure. Recent structural and functional studies have revealed how BoNTs bind to the nontoxic non-hemagglutinin (NTNH) factors to engage in interactions with either OrfXs/P47 or hemagglutinins (HAs) components for systemic dissemination. This review synthesizes recent findings that elucidate the molecular basis of NTNH-specific anchoring to the HA70 triskelion-like element or to the host protease-activated form of OrfX2, thereby highlighting divergent pathways that enhance oral toxicity. We also discuss current perspectives on the molecular mechanisms through which BoNTs, in cooperation with associated nontoxic proteins, are absorbed from the intestine. Full article

Background: A child with Attention-Deficit Hyperactivity Disorder (ADHD) faces two issues: inattention and hyperactivity/impulsivity. These two symptoms make the child’s life more challenging compared to non-ADHD individuals. Therefore, one of the steps toward better quality of life involves cooperation with and contact with the environment to better address this condition. Thanks to Artificial Intelligence (AI), doctors, caregivers, and parents are increasingly better able to understand the hardships these children face. One AI technique is Reinforcement Learning (RL). Methods: We propose an RL model simulation with 44 child agents with or without ADHD, using the Independent Deep Q Network (IDQN), Value Decomposition Network (VDN), and QMIX algorithms. Results: Comparing the results obtained with these three algorithms, children with ADHD find it more challenging to choose the maximum rewards than neurotypical children (395 at episode 300 for non-ADHD compared to 340 at episode 120 for ADHD using IDQN, 69 from episode 90 for ADHD compared to 82 for non-ADHD via VDN, and 31 at episode 110 for ADHD versus 28 at episode 110 for non-ADHD with QMIX). Conclusions: The simulated ADHD agents struggle to aim for the maximum rewards as much as neurotypical children. Full article

In human–robot collaboration (HRC) scenarios with direct physical contact, accurately estimating human intentions and adjusting robot behaviors based on multimodal information is the core factors that restrict the efficiency and precision of current HRC tasks. To enhance the performance of human–robot collaboration under physical contact conditions, we propose a joint network model named ACT_force_cooperative (AFC). This model leverages force sensing information as a representation of human intent to achieve human intent prediction during physical interaction, while simultaneously capturing visual information and robot state data, thereby enabling more efficient execution of human–robot collaborative tasks with multimodal information processing. Existing HRC methods often ignore humans’ collaborative experience in the environment and fail to recognize the uniqueness of interactive force in expressing human intentions. Focusing on the special role of interactive force among various types of data in physical interaction environments, the proposed model predicts humans’ future behavioral intentions and adopts a Transformer model to realize the fusion and representation of multimodal information, thus accomplishing more accurate and effective HRC tasks. Experimental results demonstrate that, through the processing of force sensing information and fusion of multimodal data, the proposed model reduces the motion error by 44.9% and increases the effective collaborative time ratio by 20.2% compared with the baseline Action Chunk Transformer (ACT) model. This not only improves the motion accuracy of the robot in collaborative tasks but also enhances the collaborative experience of human operators. Full article

Naturally Occurring Retirement Communities Supportive Service Programs (NORC-SSPs) are one of the most popular models of aging in place. While the existing NORC literature focuses on the social and service environments of these programs, their built environments remain underexplored, particularly across housing tenures. This study is the first to explore the built environment, social integration, and socio-demographic factors among older people living in NORCs in New York, and their associations with health and well-being. The mixed-methods research included qualitative (interviews with NORC directors) and quantitative (151 resident surveys and an architectural assessment) data on 26 housing developments in New York, collected simultaneously using a convergent parallel design. The findings show that socialization and exercise improve the health and quality of life of NORC residents. The study also revealed that older people living in public housing have different needs than those in cooperative housing, namely a worse perception of their health and dwellings of a poorer physical condition. Therefore, the services offered by NORC programs should vary according to housing type, while management and NORC staff should improve coordination to address maintenance in public housing. Future research should examine interventions to improve the physical environments of NORC residents. Full article

Rural transportation corridors remain weakly instrumented for continuous low-altitude airspace monitoring. At the same time, Vehicle-to-Everything (V2X) roadside units (RSUs) are increasingly deployed for transportation safety services. This work investigates whether existing RSUs can be extended with passive, cooperative RF sensing to detect small UAVs without modifying standards-compliant ITS communications in the protected 5.9 GHz band. A calibrated simulation study evaluates corridor-scale operation under realistic propagation conditions, including terrain masking and narrowband interference. All results reported in this paper are derived from simulation and do not include field measurements or hardware prototyping. False alarm performance under diverse ISM emitters is not quantified. The results show that cooperative processing across neighboring RSUs improves epoch-level verified detection coverage compared with single-RSU sensing. Bearing variability is reduced for weak or partially masked signals. These gains result from feature-level validation across spatially separated receivers rather than deterministic signal combining. RF calibration constrains detections to physically plausible kilometer-scale ranges. The resulting angular accuracy is sufficient for early warning and track initiation, but not for precise localization. Overall, the findings indicate that existing V2X infrastructure can support supplementary early warning capability for corridor-scale airspace monitoring while preserving primary V2X safety functions. Full article