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.
Off grid renewable systems on remote coasts lean on storage to bridge long spells of weak solar and wind, yet mechanical and electrochemical storage are rarely tested side by side under the same conditions. This work assesses seawater pumped hydro storage (PHS) and lithium iron phosphate (LFP) batteries inside a standalone system for Rangabali, Bangladesh, and routes surplus renewable energy that would otherwise be dumped into a reverse osmosis plant treated as a priority-dispatched, shiftable load. Both options were sized for the same community and water loads under cycle charging and load following dispatch using NSGA-II, NSGA-III, MOGWO, and MOSSOA, then ranked by a two stage Fuzzy TOPSIS framework on cost, lifecycle emissions, reliability, excess energy utilization, and job creation. The PV/WT/DG/PHS configuration under load following dispatch ranked first with a cost of energy of 0.2452 $/kWh and a loss of power supply probability of 4.928% while supplying 2821.6 MWh of electricity per year. Holding the diesel generator to 894 h/year kept lifecycle emissions at 0.1632 × 106 kg of CO₂ equivalent per year, a 97% cut against a conventional baseline, and 272.24 MWh/year of surplus produced 62,154.82 m3 of freshwater; the RO plant and storage together used 57.77% of all surplus generation (excess energy utilization factor). The LFP bank recovered energy at 95% round trip efficiency against 70% for PHS, but its 15-year life and heavy embodied carbon left its own storage-related emissions nearly 50 times above PHS's, even though whole-system lifecycle emissions differed by less than a factor of two between the top-ranked configuration of each technology. A Monte Carlo analysis of 10,000 runs returned a mean cost of energy of 0.2031 $/kWh with narrow dispersion when demand was sampled 10% to 50% above the design load with capacity held fixed.
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 min Gamma obs = 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.
Mixed methods research has a prominent place in tourism and hospitality scholarship, yet adoption has advanced faster than methodological selectivity, which leaves many studies combining qualitative and quantitative strands without explaining why both strands were necessary, how the strands were related, or what integrated inference their combination alone made possible. Drawing on a critical review of 222 mixed methods studies published in leading tourism and hospitality journals, this article identifies dominant patterns, strengths and weaknesses, and underused possibilities. In response, the article offers a stage-based guide that links problem formulation, purpose selection, design fit, sampling bridges, strand roles, integration, divergence handling, reporting architecture, and contribution development. In doing so, the article advances a more demanding standard for mixed methods scholarship and argues that the next phase of tourism and hospitality research should shift from routine method combination toward integrated inferences that are conceptually sharper, methodologically clearer, and practically more useful.
