Matrix Engineering (United States)

A working knowledge of theory requires theory translation within and outside the nursing discipline to guide practice and inform generative epistemic nursing knowledge. However, theory focused content for application to practice is scarce in many Doctor of Nursing (DNP) education programs. Communication between faculty and students regarding the use of theory to guide practice is an important action that is not well understood. The terminology to describe this process is nursing theory conversance (NTC). However, NTC has not been defined within the literature. We provide a tentative definition of NTC and explore its importance in DNP education.

Formation bulk density is vital for reservoir evaluationin oiland gas geophysical exploration. The pulsed neutron-gamma density(NGD) logging method, utilizing a pulsed neutron source, providesa safer and eco-friendlier option for formation density measurementcompared to conventional gamma–gamma density (GGD) logging,which employs a 137Cs chemical gamma-ray source. However,the accuracy of NGD is compromised by complex interferences like pairproduction and fast neutron transport, with borehole conditions furtherinfluencing its performance. Using intuitive path diagrams insteadof complex mathematical derivations, we thoroughly analyze variousinterference factors and find that the effects of pair productionrepresent merely a minor aspect of the broader impacts resulting fromchanges in the formation’s chemical composition. Like usingfast neutron terms to characterize neutron transport’s influenceon inelastic gamma-ray production, we integrate gamma-ray spectra,critical indicators of formation chemistry, into our density calculationmodel to account for pair production and other interferences. We applyadvanced machine learning regression algorithms to handle the increasedinput features and mitigate traditional borehole correction methods’accuracy loss and complexity. Integrating gamma-ray spectra with machine-learningregressors significantly improves density prediction accuracy, reducingroot-mean-square errors from over 0.03 g/cm3 to below 0.01g/cm3 in both training and test sets. This method outperformsthe conventional four-detector NGD method even with a single gamma-raydetector, saving costs, enhancing resolution, and demonstrating considerablepractical potential. Moreover, machine learning enables density predictionand borehole correction in a single step, streamlining the workflow,reducing accuracy loss, and achieving impressive results even withouttool standoff information, broadening its applicability.

Dynamic load variations, distributed generation, and environment contribute to the inherent complexity of power grids, which are highly susceptible to various faults. Proper and prompt fault localization is essential in supporting and ensuring stability of the system as well as reducing downtime. The proposed architecture PIA-GNN is a hybrid of graph neural networks and physics-informed attention created to learn topological and physical power grid laws. The framework treats buses as nodes and transmission line as edges and introduces electrical measures, and line parameters in the learning process and applies the laws of Kirchhoff in guiding attention weight. It is experimentally tested on real fault dataset that PIA-GNN achieves an accuracy of 98.99% in fault localization and is sufficiently robust in noisy, incomplete and dynamic working conditions. The attention mechanism has the capability of providing interpretable facts that they extract the role that important buses and lines are involved in the determination of a fault. Such results indicate that the framework is an exceptionally efficient, stable, and explainable model that offers practical help to grid operators to recognize, isolate as well as overcome faults using multi-faceted power systems.

In the contemporary business landscape, organizations are increasingly turning to artificial intelligence (AI) to revolutionize traditional Customer Relationship Management (CRM) processes. This study explores the integration of AI technologies into CRM systems and its profound impact on enhancing customer engagement and overall business performance. The research delves into key AI applications within CRM, including data analysis, predictive analytics, customer segmentation, and personalized marketing. The study emphasizes the role of AI in transforming raw customer data into actionable insights. Through advanced data analysis and predictive analytics, businesses gain the ability to anticipate customer needs, preferences, and behaviors. AI-driven customer segmentation facilitates the development of targeted and personalized marketing strategies, fostering more meaningful and relevant interactions. Moreover, this research also investigates the challenges and opportunities associated with the implementation of AI in CRM. It addresses concerns related to data privacy, ethical considerations, and the need for effective change management. It also explores the evolving landscape of AI technologies and their 156potential to shape the future of customer relationships. It discusses emerging trends, such as natural language processing (NLP), chatbots, and machine learning, and their implications for the evolution of CRM practices.

A fundamental comprehension of the intricate interactions following CO2 injection into the reservoir is critical in geological carbon sequestration. In this study, we performed pore-scale simulation to couple two-phase flow, multicomponent transport, and geochemical reactions. We investigated the dynamic reactive dissolution during the CO2 injection process, considering the influence of capillary number and wettability. Furthermore, we analyzed the temporal and spatial evolution of multiple chemical components (CO2 (aq), CO32-, HCO3-, and H+) within a complex pore structure. Based on these analyses, we proposed an empirical formula to describe the average pH evolution over time. The pore structure significantly influences the dynamic evolution of the two-phase interface, leading to nonuniform reactive dissolution and pronounced differences in hydrochemical responses across different pores. A rapid decrease in pH creates a moderately to strongly acidic environment, while the distribution of CO32- and dissolved CO2 exhibits notable variability. This can result in mineral dissolution in the inlet region and precipitation at the distal end of the formation. Capillary number and wettability play a crucial role in reactive dissolution by modulating the evolution of the two-phase interface. Under medium capillary number and strongly water-wet conditions, the two-phase interface behavior becomes more active, with reactive dissolution further enhancing interface activity and promoting phenomena such as Haines jumps and snap-offs.

Abstract A geotechnical survey was carried out in a swamp location in Niger Delta, Nigeria. The primary aim of the geotechnical investigation is to gather crucial data on the physical characterisation of soil consistency in the area. The investigation was carried out by conducting site characterisation, laboratory testing, and data analysis. As a result, the geotechnical survey was conducted on land, swamp, and water (river). Two (2) boreholes labeled BH1 and BH2 were dug on land, each of which is 40 meters deep. The sample rate for BH1 and BH2 is 0.75 meters. Based on the sample rate of 0.75 meters, soil samples collected for each borehole were 53 samples. A total of 106 soil samples were collected for BH1 and BH2. Six (6) boreholes were dug over water, each at a depth of 20 meters with a sample rate of 1 meter given the total soil samples collected to be 120 samples. The boreholes were dug to determine the contents of the soil in each location. BH1 revealed a clay formation from the ground surface to a depth of 11.0 meters, and fine sand was encountered with a band of clay between 11 meters and 20.25 meters. Clay was discovered from 20.25 meters to the final 40.0 meters of the investigation. Similarly, on BH2, a formation of clay was observed from the ground surface to a depth of 34.50 meters, and fine sand was encountered at 34.50 meters and 40.0 meters depth. The moisture content of the clay layers ranges from 31-163 %, indicating that the clay has a low to high moisture content, as is typical of marine clays, with bulk unit weights ranging from 12.52-19.44 kN/m3. The liquid limit values range from 88-140 %, the plastic limit from 27-42 %, and the plasticity indices from 61%- 98 %. The sand's SPT N-values range from 6 to 14, indicating that it is loose to medium-dense in compaction. Furthermore, the riverbed boreholes revealed entirely clay formation with no trace of sand within the depth explored. This information is vital for designing the surface structures to be erected in this location.