A framework is presented that integrates laboratory calibrated geomechanical data with machine learning (ML) to predict the shear modulus (Gs) of caprock as a resilience metric for subsurface CO2 and H2 storage. Data driven approaches have been applied to related geomechanical properties in storage contexts; however, a specific gap is addressed through the use of high resolution 10 cm static Gs labels derived from dipole sonic log calibration in evaporitic caprocks, combined with systematic benchmarking and interpretability analysis across multiple models. A transformation and calibration workflow is employed, in which high resolution dynamic elastic moduli obtained from dipole sonic logs, including compressional wave velocity (Vp) and shear wave velocity (Vs), along with bulk density (RHOB), are converted into laboratory calibrated static moduli. Calibration is performed using triaxial and uniaxial compression tests on core samples, following international society for rock mechanics standards, from an anhydrite rich caprock interval at depths of about 1200 to 1900 m under representative confining pressures. This procedure generates 10 cm resolution ground truth static Gs labels for ML training and validation. A comprehensive benchmarking and interpretability analysis is then conducted across multiple models, including feedforward deep neural networks (DNN), one dimensional convolutional neural networks (CNN), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM). A sensitivity analysis is applied to evaluate the contribution of input features, including Vs, Vp, RHOB, gamma ray (GR), and neutron porosity (NPHI), to model performance and predictive robustness, which improves interpretability and confidence in the developed models. Among the evaluated models, the feedforward DNN achieves the highest predictive accuracy on the test set with R2 of 0.9774, MAE of 0.0352, and RMSE of 0.9734, and it outperforms CNN with R2 of 0.9745, XGBoost with R2 of 0.9621, and LightGBM with R2 of 0.9485. Complementary metrics, including MAE, RMSE, and MARE, confirm the superior balance between accuracy and generalization achieved by the DNN.
Hydrogel-based plant bioelectronics are emerging as promising platforms for real-time monitoring and modulation of plant physiology, stress responses, environmental interactions, and growth. Compared with rigid electrodes and conventional polymer films, hydrogels provide a soft, hydrated, conductive, and tunable interface that reduces mechanical mismatch with growing plant tissues while enabling electrochemical, electrophysiological, optical, and multimodal sensing. This review examines recent advances in hydrogel materials for plant bioelectronics, focusing on how network structure, design requirements, materials strategies including crosslinking chemistry, porosity, swelling, adhesion, conductivity, transparency, gas permeability, and biocompatibility affect plant-device performance. Applications in monitoring plant physiology, hormones, pH, moisture, glucose, and overall plant health are highlighted. Reported hydrogel systems exhibit Young’s moduli from ∼ 1 kPa to several MPa and ionic conductivities of 10−3-10−1 S cm−1. Several plant-interfacing devices sustain strains above 300 %, maintain stable electrical performance over 10,000 loading cycles, and support continuous growth monitoring for up to 14 days. Despite these advances, standardised evaluation under realistic agricultural conditions remains limited. Future research should prioritise standardised testing, biodegradable biomass-derived materials, multimodal sensing integration, and closed-loop bioelectronic systems to advance precision agriculture and bio-regenerative life-support applications.
Autonomous ground vehicles (AGVs) operating in mountainous and off-road environments need to perform complex local navigation while simultaneously handling severe terrain undulation, intricate obstacle distributions, and task-driven observation requirements. Traditional navigation architectures often rely on decoupled planning and control modules, which makes it difficult to rigorously coordinate nominal guidance with terrain related safety and kinematic constraints. To address these challenges, this paper proposes a unified terrain-aware local navigation framework based on dynamical system modulation and control barrier functions (DSM-CBF), where traversal safety, operational agility, and observation quality are jointly considered. A DSM-based nominal guider is developed to generate continuous and smooth motion commands with natural obstacle avoidance behavior by reshaping the local vector field through anisotropic modulation. The resulting nominal control is then refined by a CBF-based safety filter formulated as a slack-variable-augmented hard quadratic program, so that forward invariance of the safety set can be guaranteed under obstacle clearance, terrain traversability, and speed constraints. Task-related observation metrics, including bistatic geometry as well as range and azimuth resolutions, are further incorporated into the optimization objective to preserve favorable sensing quality during bounded-speed terrain-following motion. Comprehensive simulations and real-vehicle experiments verify the effectiveness and practical executability of the proposed framework. The simulation results show that it satisfies the radial-resolution requirement with an achieved value of approximately 1.01 m relative to the 1.13 m design threshold and improves mission efficiency by about 27% under the observation-quality-prioritized setting; compared with the SOTA methods, the proposed method achieves zero terminal error, maintains an admissible obstacle-clearance margin with minΓobs=1.0439, reduces the turn-RMS by 86.1% and 83.6% relative to A* and D* Lite, respectively, and reduces the computational runtime by 97.9% compared with MPC-CBF, while real-vehicle tests further demonstrate safe and smooth traversal on a physical rugged-terrain AGV platform.
Trauma-informed practice (TIP) and culturally responsive pedagogy (CRP) are frequently advanced as complementary equity approaches in schools, yet the practice-level interface between them remains under-specified—particularly in communities shaped by poverty, displacement, racism, and intergenerational trauma. This qualitative study examined how 26 primary school educators in a culturally diverse, socioeconomically disadvantaged Australian school described adapting trauma-informed practices to students' social and cultural contexts. Data were collected through semi-structured interviews and analysed using reflexive thematic analysis, with ecological systems theory providing an interpretive framework. Three themes captured educators' sense-making: (1) Prioritising safety and belonging, reflecting microsystem practices addressing basic needs shaped by socioeconomic adversity; (2) Navigating cultural complexity, illuminating mesosystem negotiations between institutional expectations and family cultural practices; and (3) Seeking deeper understanding, capturing educators' recognition of exosystem gaps between available professional learning and the intersecting realities of trauma, culture, and disadvantage. Findings highlight the interpretive labour educators undertake when adapting standardised frameworks to local contexts, while revealing tensions between individualised trauma responses and structural inequities. Implications for culturally responsive professional development, institutional policy reform, and educational practice are discussed.
Grounded in the entity-referent correspondence (ERC) framework, this study examines how three authenticity dimensions of AI-generated destination portrayals, true-to-ideal, true-to-fact, and true-to-self, relate to destination attractiveness via parallel and sequential pathways of place imagination and mental simulation. Analysis of 406 participants exposed to an AI-generated tourism video reveals that true-to-ideal and true-to-fact authenticity show significant direct effects on destination attractiveness, whereas true-to-self authenticity operates entirely indirectly, consistent with a meaning-making route. For parallel mediation, true-to-ideal and true-to-self authenticity exert significant indirect effects via both mediators, whereas true-to-fact authenticity does so only through place imagination. The sequential pathway from place imagination to mental simulation receives consistent support across all dimensions. Theoretically, this study advances the ERC framework from a static typology to a processual account by delineating the distinct cognitive routes of each referent and highlighting both the conceptual promise and measurement challenges of operationalizing true-to-self authenticity as perceived communicative sincerity in algorithmically generated portrayals.
