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Patient characteristics associated with caregiver satisfaction in emergency department-based hospice transitions
3D pore pressure prediction in the offshore Nile Delta, prestack inversion based workflow for drilling optimization and risk mitigation
Abstract The offshore Nile delta basin, which is considered as one of the world’s high-pressure basins, is where the study develops the geopressured cube in 3D for the sapphire field. The key phase in the pressure prediction process is thought to be the interval velocity. In this study, we aim to use high-quality seismic data to estimate the shear and acoustic impedance volumes from the seismic pre-stack inversion technique. These cubes are then transformed into a high-resolution 3D interval velocity cube, which act as the initial and crucial input for the pressure estimation. Using four wells for model building and one for model evaluation, the workflow generated complete 3D cubes for overburden, effective stress, and formation pressure using the pre-stack inversion velocity. Optimizing the Eaton and Bowers equations parameter to be valid in the offshore Nile delta rather than the standard parameter from the Gulf of Mexico. it is considered One of the paper’s novelties. There is a high correlation between the pressure calculated from the logging while drilling (LWD) at the rig site and the formation pressure derived from the workflow. The technique shows a novel method for use in the well-planning and exploratory stages.
Assessment of the water-energy-food system and its coordination level in China’s major grain production areas
Correction: Epimedin C attenuates airway inflammation and remodeling in Asthma by intervening M2 macrophage polarization via modulating the PI3K/Akt/mTOR signaling pathway
Essential resources for curated immune markers and antibody panels in cytometry analysis
Synergy and reciprocity between Brillouin and Mie scattering by the presence of silver nanoparticles in the stimulated Brillouin scattering cell
Time-course evaluation of irreversible electroporation, bleomycin electrochemotherapy, and IRE–bleomycin therapy in a rabbit VX2 liver model using a high-voltage generator
Cross-GNN: a cross-network embedding framework for link prediction based graph neural networks
Gut microbiota structure in stingless bees is shaped by ontogeny, caste, and sex
Abstract The gut microbiota of eusocial bees functions as a dynamic system; however, despite extensive study in honey bees and bumble bees, it remains poorly understood in stingless bees. We used 16 S rRNA gene sequencing to characterize the gut microbiota of Melipona capixaba and Melipona mondury across developmental stages (larvae, nurses, and foragers), and across castes (workers and queens) and sexes (males and females) in M. mondury . We found that workers and males harbour richer communities. Workers are dominated by core bee-associated taxa, including Lactobacillus , Bifidobacterium , and Commensalibacter ; whereas males are enriched in transient and environmental genera such as Erwinia and Morganella . Nurse microbiota profile overlaps those of larvae, queens, and foragers. Larvae and queens harbour less diverse communities dominated by fermentative taxa. Both virgin and physogastric queens are dominated by Erysipelatoclostridium sp., indicating notable similarity regardless of physiological status. Predictive functional profiling is consistent with a model where worker and male microbiota are specialized for environmental resilience and oxidative metabolism, while larvae and queens are biased towards fermentation and detoxification. Our findings reveal that the microbiotas of M. capixaba and M. mondury constitute a highly plastic system, providing insights into how social structure interacts to calibrate the microbiota profile of individual colony members.
Weibull-based reliability integrated degradation framework in proton exchange membrane fuel cell system
Decentralized, centralized, and hierarchical coordination of residential DERs in three-phase unbalanced distribution networks: a comparative analysis
Correction: Domain arrangement–driven immunogenicity of a computationally designed mRNA vaccine targeting PPE68, IrtA, and PE9 of Mycobacterium tuberculosis
Phytochemicals from Tetraclinis articulata wood tar: HPLC-DAD-ESI-MS analysis, biological activities and in silico studies
DPCT: dynamic part-center-based point cloud transformer
AI usage patterns and self-rated critical thinking among Chinese college students: the moderating role of academic motivation
Abstract This study explores the associations between artificial intelligence (AI) usage patterns and perceived critical thinking among university students in China, with a focus on three types of AI use: Information Retrieval Use (IRU), Content Generation Use (CGU), and Text Revision Use (TRU). Grounded in Self-Determination Theory (SDT), the research investigates how academic motivation moderates these relationships. Cognitive Load Theory (CLT) provides an interpretive lens for understanding the observed patterns, though load was not directly measured. A cross-sectional survey was conducted with 342 undergraduate students, collecting data on their AI usage behaviors, academic motivation, and perceived critical thinking dispositions. The results reveal that IRU is positively associated with perceived critical thinking, while CGU shows a negative association. TRU showed no significant association. Regarding moderation effects, intrinsic motivation strengthened the positive association between IRU and perceived critical thinking, but, contrary to expectations, also amplified the negative association between CGU and perceived critical thinking. In contrast, extrinsic motivation buffered the negative association of CGU: at high levels of extrinsic motivation, the negative association between CGU and perceived critical thinking was entirely eliminated. These findings indicate that the cognitive correlates of AI use depend on both usage patterns and motivational factors. The study highlights the importance of considering both the type of AI use and students’ motivational orientation when designing educational strategies for AI integration in higher education.
MCDCNet: a multimodal cotton disease classification model for visually variable leaf symptoms under natural field conditions
Biodiversity and natural capital in ecologically sensitive regions
Abstract Biodiversity and natural capital support economic systems, human well-being and climate resilience. Yet conservation and planning have focused mainly on visible ecosystems such as forests, wetlands and agricultural landscapes, while overlooking belowground biodiversity and the complex interactions between rural and rapidly urbanizing regions. This Collection analyses species, community and microbiome responses to environmental gradients, management interventions and climate constraints, and the effects of these responses on productivity, habitat quality, ecosystem functioning and natural capital. Several contributions highlight that climate-adapted seed sourcing in grand fir can maintain forest growth and carbon sequestration under changing moisture regimes, and that diverse multi-crops in boreal conditions raise biomass and net energy yields while lowering environmental pressures. Other studies introduce integrated indicators such as habitat quality indices for wetland waterfowl, soil functional networks in shaded coffee systems centred on total organic carbon, and multidimensional niche assessments for zooplankton communities. Together, these papers demonstrate complementary approaches for treating biodiversity as natural capital and for sustaining the ecosystem services that support human well-being, sustainable production and informed conservation and management decisions.