This article investigates the asynchronous mixed $H_{\infty } $ and passive control for discrete-time switched systems subject to denial-of-service (DoS) attacks and communication resource constraints. In order to alleviate the communication pressure, an event-triggered mechanism (ETM) is adopted. However, due to the introduction of ETM, the asynchronous phenomenon may occur between the subsystem and controller. In addition, DoS attacks can have adverse effects on system performance by interfering with communication channels. To address the above issues, a weighted mixed $H_{\infty } $ and passivity performance criterion is adopted to balance disturbance attenuation and energy dissipation. On this basis, the Lyapunov function method is used to design the average dwell time (ADT) switching signal, ensuring that the asynchronous switched system is globally uniformly asymptotically stable (GUAS). Meanwhile, the design strategy of the controller is presented in the form of linear matrix inequalities (LMIs). Finally, the effectiveness of the proposed method is demonstrated through a practical example.

Wildfire evacuation outcomes vary due to interactions among fire progression, human behavior, and traffic dynamics. Most models treat these factors separately, leaving a gap in integrated approaches. This study develops a tri-coupled framework linking fire spread, multichannel communications, and agent-based traffic modeling. The framework integrates a fire model, a communications model capturing varied cognition times, and a spatial-queue traffic model, applied to three Marin County communities. Using a multi-scalar strategic action fields perspective (macro, meso, and micro), the analysis shows how asynchronous communication, background traffic, and localized behaviors interact to shape outcomes. Evacuation efficiency depends on communication timing and coordination, with cognitive delays producing nonlinear congestion. Peak traffic reduces capacity, underscoring the need for governance mechanisms separating emergency from routine flows. At the community level, demographic, infrastructural, and temporal characteristics shape performance, requiring geographically targeted strategies. Micro-level analyses highlight the disproportionate impact of everyday responsibilities, such as child pickup, on congestion, emphasizing behavioral realism in planning. The study offers implications for embedding adaptive communication, dynamic traffic management, and behavioral insights into institutionalized crisis information governance. The findings reveal that evacuation capacity functions as a form of access to safety, unevenly distributed by communications access, mobility constraints, and network characteristics. Linking these to continuous, data-driven evaluation can enhance wildfire preparedness while creating scalable models for other hazard-prone regions, advancing resilience and equity in evacuation systems.

This paper presents a resilient control framework for networked cascade systems (NCCSs) subject to transmission delays and hybrid cyber-attacks involving false data injection (FDI) and Denial-of-Service (DoS) attacks. The hybrid attack model is defined by the asynchronous operation of these two threats: DoS attacks disrupt data availability by blocking the communication channel, while FDI attacks compromise data integrity by injecting false signals during the DoS dormant periods. Accordingly, an event-triggered mechanism and a neural network-based estimator are co-designed to reduce the network transmission burden and identify and compensate for unstructured FDI attack signals. The closed-loop system is modeled as a switched system, and sufficient stability conditions under hybrid attacks are derived through a Lyapunov-based neural network approach to facilitate the design of an H∞ controller with guaranteed robust performance. Simulations on a steam temperature cascade control system validate the effectiveness of the proposed method, demonstrating 20% faster convergence and a 67% reduction in oscillations compared with a conventional method while maintaining stability and security under hybrid attacks.

The rapid expansion of artificial intelligence computing infrastructure and the semiconductor industry has made the dynamic sensing of policy planning, technological innovation, capital investment, and physical construction essential for industrial monitoring and resource allocation. However, policy documents, remote-sensing imagery, patent relations, and economic indicators exhibit substantial heterogeneity, temporal misalignment, and variations in data quality, making it difficult for conventional unimodal or synchronously fused methods to accurately characterize the continuous evolution of construction activities. To address these challenges, a multimodal industrial sensing dataset for artificial intelligence computing infrastructure was constructed, and an artificial intelligence-driven asynchronous cross-modal dynamic graph prediction framework was developed. Differential temporal lags among policy announcements, capital investment, patent growth, and physical construction are learned through an asynchronous cross-modal alignment module. Interference caused by cloud contamination, missing text, patent disclosure delays, and incomplete economic observations is dynamically suppressed through a reliability-aware fusion mechanism. Time-varying propagation relationships among countries, enterprises, patents, and industrial parks are further modeled using an economically modulated dynamic heterogeneous graph. Experimental results demonstrate that, in next-window construction event prediction, the proposed model achieved an Accuracy of 91.72%, a Precision of 91.08%, a Recall of 90.64%, an F1-score of 90.86%, and a ROC-AUC of 95.67%, significantly outperforming baseline methods including XLM-R, Swin Transformer, TimesNet, HGT, and TGN. In construction intensity forecasting, the model achieved an MAE of 0.104, an RMSE of 0.153, a MAPE of 10.91%, an R2 of 0.854, and a Pearson correlation coefficient of 0.928. Ablation experiments further confirmed the effectiveness of asynchronous alignment, reliability calibration, missing-modality compensation, economic modulation, and long-term graph memory. The proposed approach provides a temporally interpretable intelligent sensing method for monitoring computing center construction, evaluating industrial park expansion, allocating energy and communication infrastructure, and planning equipment supply.

Ultra-wideband (UWB) and inertial measurement unit (IMU) fusion is an effective scheme for train and rail-guided target localization because UWB provides absolute ranging results and the IMU provides high-rate motion prediction. In practical rail transportation systems, UWB anchors are usually deployed along the track with large longitudinal intervals and limited lateral separation to reduce installation and maintenance costs. This anchor deployment leads to a large condition number of the observation matrix, so slight ranging errors caused by non-line-of-sight (NLoS) propagation may be amplified into large localization errors. To mitigate LoS/NLoS interference, this article proposes a propagation reliability estimation method for track-constrained UWB/IMU fusion localization. First, rail transportation localization along a narrow path is formulated as a one-dimensional track-constrained problem, and each UWB ranging result is converted into a candidate longitudinal coordinate on the known track centerline. Second, the reliability of each anchor-target propagation is estimated in a sliding window by comparing the motion increments solved by UWB observations with the motion prediction by the IMU. Third, the estimated reliability is incorporated into a reliability-weighted track-domain update before a closed-loop position-velocity Kalman correction. The simulation results show that, under the mixed LoS/NLoS scenario, the proposed method achieves an NLoS-interval RMSE of 0.0090 m. Compared with Track-EKF, Track-Gauss-AUKF, Track-Adaptive KF, and Track-SW-FGO, the proposed method reduces the NLoS-interval RMSE by 92.9%, 62.1%, 92.8%, and 92.6%. A supplementary real-data stress test on the public STAR-loc dataset demonstrates an average longitudinal RMSE of 0.0555 m under a strict online calibrated-range protocol, supporting the algorithm's practical applicability against real-world lateral sway and asynchronous sensor noise.

Experts recommend incorporating health literacy into training to enhance providers' self-awareness and ability to implement these techniques, ultimately improving health outcomes. Interprofessional faculty developed training on plain language and health literacy as a social determinant of health. Clinical students participated in an asynchronous orientation, live educational sessions, and patient visits. Pre- and post-surveys assessed students' knowledge and attitudes toward health literacy and plain language. A faculty member collected unclarified jargon terms during each patient visit. Results were analyzed using descriptive statistics. Paired t-tests were performed for jargon use frequency and McNemar's test for knowledge scores. The purpose was to develop and pilot an interprofessional training program to improve students' knowledge and use of plain language communication in a pediatric ambulatory clinic. Fifty-five pharmacy, medicine, nursing, social work, physical therapy, and dentistry students participated. Composite knowledge scores about health literacy increased, p < .001. 93.3% of students understood how this course contributed to their professional development, and 81.6% rated live content as effective. The average number of unclarified jargon terms per visit decreased from 2.8 in week 1 to 2.4 in week 2. This study is a pilot of a real-world application that prioritizes patient-centered plain language and can be modeled in other clinical settings to support early exposure to health literacy concepts and enhance learners' knowledge.

Interprofessional care involves two or more professionals from different disciplines working in partnership to achieve a common goal and deliver high-quality care. In primary care, this can be implemented through Interprofessional Primary Care (IPPC) teams, which are a recommended approach to support chronic disease management. However, there is a scarcity of literature describing the organizational and interpersonal features that facilitate successful IPPC team outcomes, particularly in the field of chronic disease management. This review examined guiding features, and clinical, humanistic, and economic outcomes related to IPPC teams and chronic disease management. MEDLINE, Scopus, CINAHL, Embase, and The Cochrane Database of Systematic Reviews were searched for full-text English articles from 2000-2023. Primary research studies evaluating outcomes related to IPPC teams providing chronic disease management interventions were included. 24 studies on IPPC ignterventions were included. 15 reported humanistic outcomes, 12 studies reported clinical outcomes, and 3 studies reported economic outcomes. IPPC teams were impactful in improving outcomes for patients and providers. Studies also reported specific interactional components (such as communication, knowledge sharing, and shared decision-making) and practice features (such as team meetings, patient education, asynchronous care organization) that facilitated successful interventions. Implementation of IPPC teams is complex owing to the heterogeneity of primary care contexts. IPPC teams can demonstrate benefits clinically and for patient/provider satisfaction. Organizational features such as structured team and patient interactions, clear role division, and a culture of mutual respect are valuable to improve communication and care coordination in chronic disease management.