Search Results (11,335)

In multi-mission air operations, area-patrol missions and long-range missions typically share a limited tanker fleet while imposing different constraint structures: patrol refueling is bounded by hard time windows, whereas long-range missions are governed by restricted-zone avoidance, detour tolerances, and multi-segment fuel verification. Conventional scenario-wise independent planning splits resources and wastes cross-region ferry mileage. This paper adapts the established paradigms of the location–routing problem (LRP) and the vehicle routing problem with time windows (VRPTW) to this joint refueling scenario: a joint planning model prioritizes the number of tanker sorties over total system flight distance, and a decoder-coupled adaptive large neighborhood search (ALNS) integrates airspace selection, task assignment, tanker routing, and dual-timeline rendezvous decoding, with all mission hard constraints embedded in a deterministic, reproducible evaluator that adjudicates feasibility at every search iteration. Experiments at three scales (17, 42, and 100 tasks) show 100% mission coverage and 100% patrol time-window satisfaction: relative to scenario-wise independent planning, tanker sorties decrease by 16.2–19.7% and tanker flight distance by 14.6–15.5% (significant after Bonferroni correction on 90 paired replicates per scale); against genetic algorithm (GA) and ant colony optimization (ACO) baselines—and against a route-encoding GA under an equal solution-space representation—the method is superior in solution quality and runtime ($p<0.001$), and the separation persists when the baselines receive a 25-fold evaluation budget. Monte Carlo simulations characterize how plan feasibility degrades under execution-time disturbances. Within the studied instance families, the framework yields executable joint refueling plans within operational runtimes. Full article

This paper presents the closed-loop integration of three neural estimators within a physically based guidance, navigation, and control (GNC) architecture for a canard-controlled rocket. The estimators support Global Navigation Satellite System (GNSS) carrier-phase ambiguity processing, reconstruct the gravity vector in body axes, and correct the terminal line-of-sight (LOS) estimate obtained by fusing GNSS, inertial measurement unit (IMU), and semi-active laser (SAL) quadrant-detector information. Building on earlier specialized or partially integrated studies, the contribution is the simultaneous embedding of the three estimators in a common nonlinear six-degree-of-freedom closed loop and their evaluation, against a matched model-based baseline and an eight-configuration ablation study, under nominal, wind, high-angular-rate, GNSS-degradation, and combined-disturbance scenarios. By combining complementary attitude, gravity, and target-relative information while retaining physical validation and model-based fallback, the architecture is designed to improve navigation resilience and functional coverage across multiple sensor-degradation modes. All 500 closed-loop evaluation trajectories completed without numerical divergence. The overall trajectory-averaged squared vector errors were $4.13\times {10}^{-3}{\mathrm{m}}^2/{\mathrm{s}}^4$ for gravity and $1.59\times {10}^{-3}$(dimensionless, corresponding to a $2.3^{\circ }$ RMS-equivalent angular error) for LOS; under combined disturbances, their mean errors were 3.08 and 2.99 times the corresponding nominal values. Relative to the matched model-based baseline, the integrated architecture improved the GNSS ambiguity fix-success rate from $96.4\%$ to $97.9\%$ and reduced terminal-guidance circular error probable (CEP50) from $0.49$ m to $0.35$ m (an absolute reduction of $0.140$ m; paired-bootstrap $95\%$ CI $[0.118,0.156]$ m), corresponding to a $28.6\%$ relative reduction, and the ablation study indicated a near-additive, monotonically improving contribution from each of the three modules. The results demonstrate simultaneous operation of the three modules as active components of the propagated GNC loop over the tested conditions, with a quantified improvement over the matched conventional configuration. Full article

Mineral prospectivity mapping (MPM) increasingly integrates geological, geochemical, geophysical, remote-sensing, and structural evidence through machine-learning workflows. Predictive accuracy alone, however, does not establish that a prospectivity map is geologically credible or useful for exploration decisions. This critical narrative review examines explainable artificial intelligence (XAI) for MPM through the connections among model behavior, mineral-system knowledge, sampling, spatial validation, uncertainty, and field evidence. We distinguish methods demonstrated in representative MPM studies from general explanation tools and proposed applications. Study-level comparisons show that SHAP and permutation-based attribution can support evidence-layer auditing and target interpretation, while their meaning depends on correlated predictors, label construction, and evaluation design. Spatially separated evaluation tests a different generalization problem from random splitting; neither replaces newly acquired field evidence. Geological plausibility, model faithfulness, explanation stability, and decision utility therefore require separate assessment. We synthesize practical pathways for geological knowledge embedding and three-dimensional modeling, identify limits in current graph explanations and uncertainty reporting, and propose a minimum reporting checklist. Future priorities include geospatial foundation models, source-traceable language tools, three-dimensional prospectivity and four-dimensional extensions incorporating geological time, knowledge-guided hypothesis generation, integrated exploration systems, and field-based evaluation of explanations. Full article

To address the urgent need for AI literacy in vocational education, we implemented a 17-week robotics-enhanced micro-major using scaffolded hardware-in-the-loop environments. A longitudinal quasi-experimental design revealed significant gains across three dimensions: architectural knowledge (+15.1%), system attitude (+7.2%), and ethical awareness (+5.8%). Behavioral telemetry confirmed a near-doubling of high-frequency AI tool adoption (34.8 to 61.1%), demonstrating sustained integration of coding assistants beyond classroom settings. These findings establish robotics-based curricula as a scalable framework for bridging the digital divide, cultivating human–AI collaboration, and preparing vocational learners for lifecycle deployment in complex cyber-physical ecosystems. Full article

Effective Internet of Things (IoT) education requires students to integrate sensing, actuation, embedded programming, networking, and application development into complete cyber-physical systems. Achieving this integration remains challenging, particularly because students often enter IoT courses with widely varying backgrounds in hardware and networking. This paper presents a decade of experience in the iterative development of an undergraduate project-based IoT course and reports course evaluation results from four recent offerings: Fall 2020, Fall 2021, Spring 2023, and Spring 2024. The course adopts a three-stage project sequence consisting of (1) a single-node sensing and actuation system, (2) a network-connected IoT device using standard communication protocols, and (3) an open-ended capstone prototype, progressively scaffolding students from guided implementation to independent system design. We describe instructional strategies for addressing common implementation challenges, including hardware debugging, software toolchain configuration, and project scoping, and present representative student projects. Course effectiveness draws on anonymous pre- and post-course surveys together with rubric-based project assessment. Across all four offerings, students consistently reported high perceived learning gains, while final project performance remained strong across cohorts with different levels of incoming hardware experience. The course design, instructional framework, and assessment methodology provide a practical and adaptable example for project-based IoT education that can be readily adapted by other institutions to accommodate diverse student backgrounds and local curricular needs. Full article

Precise indoor thermal environment forecasting is critical for the energy-efficient operation and model predictive control of building heating, ventilation, and air-conditioning (HVAC) systems. However, conventional predictive models often struggle to untangle the complex spatiotemporal dynamics captured by sparse sensing networks. This study proposes a Spatio-Temporal Joint-Embedding Predictive Architecture (ST-JEPA) for indoor multi-node temperature and humidity prediction. By reformulating predictive representation learning from visual domains to structured sensor-node sequences, the proposed model explicitly integrates historical multi-node observations, spatial positional encoding, and concurrent HVAC operating states. The model was evaluated at prediction horizons from 30 s to 5 min using 3568 frames recorded over approximately 29.8 h by a nine-node sensor array in a single laboratory. Experimental results indicate that ST-JEPA achieves high short-term forecasting accuracy, with temperature root mean square errors (RMSE) of 0.0490 °C at 30 s and 0.0647 °C at 1 min. In the original ablation experiment, removing HVAC operating-state inputs increased temperature RMSE by up to 68.3%. Aggregate errors remained relatively stable under the tested 30–50% node-masking conditions, although additional single-node tests revealed location-dependent sensitivity. Comparisons with simpler baselines showed no consistent superiority across targets and horizons. ST-JEPA characterizes the short-term evolution of indoor thermal fields, providing a potential forecasting basis for HVAC feedforward regulation; its effects on energy consumption and thermal comfort remain to be evaluated. Full article

Reliable mobility management is critical for high-mobility 5G/6G applications such as smart cities, intelligent transportation systems (ITS), and connected vehicles, where reactive 3GPP handover mechanisms become unreliable as user equipment (UE) velocity increases. This paper proposes a hybrid Bidirectional LSTM (Bi-LSTM) and Deep Reinforcement Learning (DRL) framework for AI-native predictive handover management: a Bi-LSTM encoder extracts a knowledge embedding and forecasts future network observations, forming a predictive state that a Dueling Double DQN (Dueling DDQN) with prioritized experience replay uses to select handover actions. A closed-loop, dual-frequency refresh mechanism periodically updates both modules from accumulated network experience, without manual retuning. The framework is evaluated via Monte Carlo simulation across Smart City, ITS, and Connected Vehicle scenarios and Urban, Suburban, and Mixed eployments. The proposed Bi-LSTM predictor reduces RMSE by 51.9–68.6% relative to a persistence baseline and by 8.4–25.6% relative to a Transformer predictor, with the largest gains under high mobility. End-to-end evaluation shows the framework achieves a throughput of 67.0 Mbps, a latency of 48.1 ms, a handover failure rate of 3.47%, and an average utility of 0.678, outperforming all ablation variants; disabling closed-loop refresh causes the largest degradation (throughput loss: 7.0%, higher failure rate: 33.1%). Generalization experiments show bounded degradation under unseen conditions, with closed-loop adaptation recovering utility from approximately 0.588 to 0.678 within 100 refresh epochs after a domain shift. These results show that integrating predictive knowledge extraction, adaptive decision-making, and continual refresh provides a robust, closed-loop architecture for AI-native mobility management for proactive 5G/6G mobility management. Full article

Background: Physically-based, coupled hydrological–low-impact-development (LID) models, such as the U.S. EPA Storm Water Management Model (SWMM), estimate green infrastructure (GI) performance in detail but are computationally expensive to run across many catchment, storm, and typology combinations. Methods: This methodological proof-of-concept develops an open-source R workflow (randomForest, xgboost, caret) on a synthetic dataset of 2000 catchment–storm–typology scenarios generated from prescribed non-linear equations of imperviousness, storm return period, and the coverage of four GI typologies. None of the scenarios were generated by SWMM-LID simulation or field monitoring. Random forest, XGBoost, and a linear baseline were trained to predict synthetic peak-flow attenuation and suspended-solid (TSS) removal. Results: On the held-out synthetic test set, XGBoost and random forest reached R² values of 0.96 and 0.92 for peak-flow attenuation (linear baseline: 0.90) and 0.94 and 0.89 for TSS reduction (linear baseline: 0.79). These values show that the models learned the synthetic response surface; they do not measure predictive skill for real GI systems. Feature importance reproduced the typology weighting embedded in the data-generating equations. Surrogate inference took about 2–4 ms (measured), compared with tens of minutes typically reported in the literature for SWMM-LID runs (not measured here); this is an indicative comparison, not a controlled benchmark. Conclusions: This study is a methodological demonstration only and is not a validated hydrological surrogate. Real application requires retraining and validation using SWMM-LID simulation ensembles or field-monitored data. Full article

Energy-burdened rural communities require hybrid renewable energy storage solutions that are not only economically viable but also resilient to extended generation shortfalls. This study develops a spatially informed techno-economic and resilience assessment framework for hydrogen–biogas renewable microgrids and applies it to Robeson County, North Carolina. A Hydrogen Priority Index (HPI) is used to identify locations where high household energy burden coincides with favorable renewable energy suitability. Four community-scale microgrid configurations are then evaluated in HOMER Pro under standard economic criteria and an embedded seven-day winter solar shortfall stress scenario. Results show that biogas integration reduces net present cost by 28.4% by restructuring the optimal system architecture and reducing PV and battery oversizing. PV–battery configurations cannot achieve near-complete resilience under the imposed stress scenario regardless of component scaling, while hydrogen-inclusive configurations reduce stress-period unmet load by 97.2%. The full hydrogen–biogas hybrid delivers this resilience-constrained performance at 26% lower net present cost than the hydrogen-only configuration. These findings demonstrate that combining spatial prioritization with resilience-constrained techno-economic assessment supports more equitable and deployment-ready planning of renewable microgrids for underserved rural communities. Full article

Background: Healthcare information systems are effective at documenting encounters but remain less capable of representing evolving patient states, coordinating cross-disciplinary decisions, and reconnecting decisions with longitudinal outcomes and value. This Perspective derives a reference architecture for a clinical intelligence layer positioned between source systems and accountable care delivery. Methdology: Using a design-science approach, we combined requirements from learning health systems, semantic interoperability, clinical workflow modelling, value-based healthcare, and the sequence-sensitive characteristics of sarcoma care. We abstracted two complementary cross-domain design patterns—risk-aware common representation and ontology-driven workflow—and translated them into healthcare-specific requirements. Results: The resulting architecture contains seven layers: (1) source integration and provenance; (2) a canonical semantic model; (3) longitudinal patient-state representation; (4) analytics and scenario reasoning; (5) workflow-embedded decision support; (6) outcome and value feedback; and (7) network learning and governance. We additionally specify a minimal formal information model, a bitemporal knowledge-state model distinguishing clinical/effective time from information-availability time, provenance and contradiction semantics, analytic validation gates, and a synthetic architectural verification using four pre-specified patient trajectories comprising 18 synthetic records. All nine pre-specified architectural invariants were satisfied, including correct historical-state reconstruction, preservation of superseded versions, recommendation–patient decision–treatment separation, mixed temporal granularity, and zero retrospective information leakage. Conclusions: SHAPEHub is presented as an implementation-informed sarcoma exemplar rather than as a validated product. The proposed layer is intended to complement—not replace—electronic health records, interoperability standards, common data models, registries, and disease-specific applications. Further technical validation in production-like environments, together with workflow, safety, and clinical validation, remains necessary before claims of utility or transferability can be made. Full article

The essay develops a theoretical framework that conceptualizes affective online media rituals as repetitive, platform-mediated communication practices through which collective emotional synchronization, community formation, and social meaning-making occur. The paper employs theoretical triangulation, drawing on three complementary theoretical traditions—ritual communication theory (Carey; Dayan and Katz; Couldry), affect theory and affective publics (Papacharissi), and platformization and deep mediatization theories (Gillespie; Hepp; van Dijck)—to provide a conceptual definition of affective online media rituals and develop a theoretically informed typology of their main forms and functions. The article further argues that contemporary media rituals are different from past broadcast rituals in that they are embedded in algorithmically curated platform environments. To explain this transformation, it introduces the concept of the algorithmic feedback loop, describing how emotional expressions are continuously transformed into data, amplified by recommendation algorithms, and recirculated as increasingly visible ritual performances. This mechanism transforms the production of collective emotional experiences in platform societies. The study also argues that the mechanisms presented are platform-specific; algorithmic amplification is a non-deterministic component of the system and does not necessarily result in enduring engagement or heightened political efficacy in a universal sense. The article contributes to media and communication theory in three ways: first, by introducing the concept of affective online media rituals; second, by proposing a theoretical model of algorithmically amplified ritual communication; and third, by developing a typology that provides an analytical framework for future empirical research on digital publics, platform communication, online participation, and emotionally driven collective action. Full article

To address the challenges of complex programming, high physicaltesting costs, and lengthy development cycles in conventional diesel engine controller development, this paper describes the design and prototype implementation of an embedded simulation control system for diesel engines, intended for early-stage controller algorithm pre-validation. The system is built around an STM32F407VE microcontroller and follows the model-driven development (MDD) paradigm. First, in accordance with the real-time and accuracy requirements of the simulation control system, core software modules—including real-time task scheduling, signal acquisition and processing, Ethernet communication, and host–target interaction—are designed to construct an embedded software framework that integrates simulation computation, signal sampling, command execution, and data exchange. Second, a modular diesel engine simulation model is developed in the MATLAB R2022b/Simulink environment and the graphical model is transformed and ported into embedded real-time C code via automatic code generation tools. Finally, an embedded real-time simulation verification platform is built. Distinct from our previous work on parallel power units, this study focuses on a single-engine diesel power system. Test results demonstrate that the proposed single-engine simulation platform can run the diesel engine model in real time on the target hardware and achieve closed-loop speed tracking under starting and multi-step command scenarios. The platform provides a low-cost, preliminary verification aid for early-stage diesel controller algorithm logic debugging and pre-parameter tuning. It should be highlighted that this platform is not intended for high-fidelity physical reproduction of real diesel engines. Quantitative model accuracy against real-engine dynamometer data remains to be established in future bench calibration campaigns. Full article

Automatic sign language recognition has become an important research area for improving communication between deaf individuals and the hearing community. While vision-based approaches achieve high recognition performance, their dependence on cameras and environmental conditions limits their suitability for portable real-time applications. This paper presents a fully embedded Moroccan Sign Language (MSL) recognition system based on a dual smart glove equipped with ten MPU6050 inertial measurement units (IMUs). A dedicated dataset of approximately 8000 gesture sequences representing 20 common MSL gesture classes was collected from 80 participants. The acquired multivariate inertial signals were preprocessed and used to train a lightweight Long Short-Term Memory (LSTM) network, which was quantized and deployed on a Raspberry Pi Pico microcontroller using TensorFlow Lite Micro. Experimental results achieved an overall recognition accuracy of approximately 98%, with high precision, recall, and F1-score, with an average inference latency of 27.4 ± 1.0 ms on the embedded platform. The proposed platform demonstrates the feasibility of accurate and low-latency MSL recognition on resource-constrained embedded hardware under controlled acquisition conditions, representing an initial proof of concept toward future wearable assistive communication systems. Full article

Light-emitting diode (LED) sources for high-end display and professional lighting must maintain color-point accuracy and color-rendering stability over a wide correlated color temperature (CCT) range. Three-color mixing places the color point on the Planckian locus, but non-synchronous thermal drift of the channels induces CCT shift, ${\mathrm{D}}_{\mathrm{u}\mathrm{v}}$ instability, and color-rendering degradation during long-term operation. This paper proposes a green–azure–warm-white (GAW) mixing and temperature-compensation method based on the hybrid genetic–particle swarm optimization (HGPSO) algorithm. For each channel, three-Gaussian chromaticity–temperature and spectral-power-distribution–temperature models are established, and the Duv tolerance is converted into per-channel duty-cycle bounds that shrink the HGPSO search space, enabling lookup-table-based real-time compensation on an embedded MCU. Within the 30–90 °C solder-pad range, compensation reduced the average CCT deviation at 65 °C and 85 °C from 115 K and 199 K to 14.3 K and 15.8 K and the average |${\mathrm{D}}_{\mathrm{u}\mathrm{v}}$| from 0.00167 and 0.0023 to within 0.001. The average ${\mathrm{R}}_{\mathrm{f}}$ deviation fell from 0.58% and 1.28% to 0.44% and 0.51% and the average ${\mathrm{R}}_{\mathrm{g}}$ deviation from 1.83% and 2.69% to 0.27% and 0.32%. The luminous-efficacy deviation remains within ±5% below 4500 K (maximum −8.05% at 6500 K). Cross-vendor validation confirms the portability of the method for long-term color consistency in display and lighting systems. Full article

To address the strong load rigidity and high energy consumption of large-scale broiler breeding, as well as the lack of precise load-characteristic support and efficient scheduling methods for breeding-oriented energy supply and consumption optimization, this paper conducts load-characteristic analysis for large-scale broiler houses and proposes an energy management optimization method based on the improved dream optimization algorithm (IDOA). First, year-round field monitoring was performed on a large-scale broiler farm in Laiyuan, Hebei, to analyze the load characteristics across seasons and breeding cycles and reveal the load evolution rules. Second, a comprehensive operational cost objective is established, considering the renewable operation and maintenance cost, the time-of-use power trading cost, and the carbon emission cost. Third, adaptive weight, dynamic mutation, and opposition-based learning strategies are embedded into the IDOA to better balance global exploration and local exploitation, and the improved algorithm outperforms other meta-heuristic algorithms on the CEC2017 benchmark functions. Simulation tests covering the four-season typical days and key breeding stages demonstrate that, compared with the rule-based dispatch strategy, the proposed method lowers the daily operating cost and effectively smooths the grid power profile, while the ESS state of charge is always maintained within the preset limits. Sensitivity analyses on the ESS capacity and the load and renewable forecasting errors further verify the robustness of the dispatch results, and the single-dispatch runtime of about 23 ms amply satisfies the real-time requirement of field implementation. This study offers theoretical support and practical reference for energy saving and carbon reduction in large-scale livestock breeding and breeding-park energy system scheduling. Full article