Data Record Research Articles

Sea surface temperature differences between in situ and GHRSST (L4) records in a socio-ecological critical area of the northeastern Pacific Ocean May 2015 to July 2016

Using satellite sensor data for studying sea surface temperature (SST) provides advantages over in situ information, such as obtaining data from large areas and remote access regions in short periods. However, differences between the SST values recorded in situ and those by satellite sensors are due to the intrinsic nature of both methods and meteorological factors. The present study aims to search the difference between SST values from in situ and satellite sensor data of 1×1 km resolution (type L4) from the Group of High-Resolution Sea Surface Temperature (GHRSST) for an annual cycle in a socio-ecological critical area known as the Gulf of Ulloa (GU). The linear regression, linear spline regression, and logic models showed an overestimated SST by satellites compared to in situ data, particularly at temperatures below 20°C. The overestimation can be attributed to the oceanic-atmospheric variations, which are consequences of upwelling and the time lag between the data record morning by in situ and night by satellite sensors. This finding may be relevant in decision-making for marine resource management and conservation in the GU. The information may help to seek the sustainability of this social-environmental system.

Utilising Injection Logging Tool: A Case Study of a Well in An Iraqi Oil Field

Injection logging tools (ILT) have long been used in the industry to evaluate well performance and make strategic decisions. This case study validates the application of ILT in diagnosing anomalies and evaluating well-injection performance. An injector in Iraq's southern oil fields has three perforations, each in a different zone (commingled injection). Throughout the ILT job interpretation, at the flow pass analysis, according to the spinner recording, the first zone perforation takes all the injected water confirmed by the temperature sensor. While in the shut-in pass, the temperature sensor detects an anomaly, a cooling effect, on the entire first zone, not just in front of the injection perforation; there is also a cooling effect across the last perforation that is supposed to take nothing based on the spinner record at the flowing pass. Considering the interpretation, the whole first zone was connected vertically, with good rock quality, which made it cold by the effect of injected water. Also, a small amount of injected water goes to the other perforation, less than the value the spinner can detect. The downhole profile confirmed the perforations contribution.

MPSDynamics.jl: Tensor network simulations for finite-temperature (non-Markovian) open quantum system dynamics.

The MPSDynamics.jl package provides an easy-to-use interface for performing open quantum systems simulations at zero and finite temperatures. The package has been developed with the aim of studying non-Markovian open system dynamics using the state-of-the-art numerically exact Thermalized-Time Evolving Density operator with Orthonormal Polynomials Algorithm based on environment chain mapping. The simulations rely on a tensor network representation of the quantum states as matrix product states (MPS) and tree tensor network states. Written in the Julia programming language, MPSDynamics.jl is a versatile open-source package providing a choice of several variants of the Time-Dependent Variational Principle method for time evolution (including novel bond-adaptive one-site algorithms). The package also provides strong support for the measurement of single and multi-site observables, as well as the storing and logging of data, which makes it a useful tool for the study of many-body physics. It currently handles long-range interactions, time-dependent Hamiltonians, multiple environments, bosonic and fermionic environments, and joint system-environment observables.

Nutrient Shift in the Yucatan Basin during the late Early Miocene: Evidence from calcareous nannofossils and stable isotopes at ODP Leg 165 Site 998 Hole A

Paleoceanographic reconstructions in the Caribbean Sea during past climate transitions, namely the Late Oligocene to Early Miocene (up to the Miocene Climatic Optimum and Monterey Excursion) are crucial to understanding the effects of the Eastern Pacific interchange with the Atlantic Ocean. Studies of the planktonic domain in the Yucatan Basin during the Early Miocene are limited, with most Caribbean research focused on reconstructing the series of events that precede the final closure of the Isthmus of Panama in the Early Pliocene. Furthermore, paleoceanography of the Yucatan Basin is largely unrefined during this time, even though it is an important transition for ocean gateways, and development of Atlantic deepwater and Atlantic Meridional Overturning Current (AMOC), driving global ocean circulation. Here we conduct a relative percent abundance analysis of calcareous nannofossils and stable isotopes from bulk sediment of drill cores spanning the Upper Oligocene to Lower Miocene in the central Yucatan Basin, collected by the Ocean Drilling Program Leg 165 at Site 998 Hole A. Nannofossil assemblage data record a shift from a eutrophic to oligotrophic system ∼19.2–17.65 Ma. Eutrophic taxa (Reticulofenestra spp. and Cyclicargolithus floridanus) decline at ∼20.23 Ma, while oligotrophic taxa (discoasters and sphenoliths) increase as stable carbon isotope values decline. The palaeoecological turnover coincides with a possible restriction in the Central American Seaway (CAS) ∼20.7–21 Ma. Population dynamics of the Hole 998 A nannofossil assemblage track with global declines in C. floridanus relative abundance between ∼20.2 to ∼20.5 Ma, followed by a subsequent decrease in Reticulofenestra spp. These data suggest water mass stratification in the Yucatan Basin during the Early Miocene ∼19.2–17.65 Ma, likely due to restriction of the CAS.

Influence of Fix Schedule on the Location Accuracy of a Low-Cost GPS Data Logger on Cattle

ABSTRACT Global positioning system (GPS) data loggers are commonly used to track the movements and distribution of both wild and domestic animals. However, the expense often poses a challenge for researchers. Recently, there has been a rise in the utilization of affordable and user-friendly GPS data loggers for tracking animal movements, albeit with compromised accuracy. We aimed to identify factors influencing the accuracy of a low-cost GPS data logger (I-gotU GT-600) and to enhance its location accuracy. Initial investigations revealed that recording intervals impacted the location error of the GPS data logger. To elucidate the relationship between recording intervals and location accuracy, we conducted stationary and motion tests. Our findings indicated that recording intervals of less than 15 sec substantially enhances the location accuracy of the low-cost GPS data logger. Our results highlight the relationship between the fix schedule and location accuracy for these GPS data loggers. Our study provides information that enhances the quality of data for researchers using low-cost GPS data loggers for short-term studies in various settings, such as zoos and livestock facilities.

SCADA System for Process Data Exchange in Master Slave RF and Iot Network

In every aspect of our lives, we can observe how automation plays a facilitating role in the performance of responsibilities of a comprehensive rank. Thanks to the development of electronics, today's process measurement systems enable the measurement values of process quantities to be visualized on display, sent remotely, and stored in a file, compatible with future user processing. In this paper are represented results of the development, design and practical implementation of the SCADA system, which enables data exchange between SCADA screen, MASTER and SLAVE stations. The MASTER station is installed in one industrial plant and the SLAVE is installed in another plant. The SCADA monitoring system is built into the MASTER station. The SLAVE station is connected by RF (radio frequency connection) to the MASTER station. In the MASTER and SLAVE station two microcomputers units, as well as appropriate RF modules, are installed. The microcomputer in the MASTER station is serially connected to the IoT microcomputer, for the transfer of measurement data in the Internet network. The solution provides visualization, data log file, and transfer to the IoT network of process data from the two industrial plants.

Predictors of long-term variability in NE Atlantic plankton communities

Anthropogenic pressures such as climate change and nutrient pollution are causing rapid changes in the marine environment. The relative influence of drivers of change on the plankton community remains uncertain, and this uncertainty is limiting our understanding of sustainable levels of human pressures. Plankton are the primary energy resource in marine food webs and respond rapidly to environmental changes, representing useful indicators of shifts in ecosystem structure and function. Categorising plankton into broad groups with similar characteristics, known as “lifeforms”, can be useful for understanding ecological patterns related to environmental change and for assessing the state of pelagic habitats in accordance with the EU Marine Strategy Framework Directive and the OSPAR Commission, which mandates protection of the North-East Atlantic. We analysed 29 years of Continuous Plankton Recorder data (1993–2021) from the North-East Atlantic to examine how trends in plankton lifeform abundance changed in relation to one another and across gradients of environmental change associated with human pressures. Random forest models predicted between 57 % and 80 % of the variability in lifeform abundance, based on data not used to train the models. Observed variability was mainly explained by trends in other lifeforms, with mainly positively correlated trends, indicating bottom-up control and/or shared responses to environmental variability were prevalent. Longitude, bathymetry, mixed layer depth, the nitrogen-to‑phosphorus ratio, and temperature were also significant predictors. However, contrasting influences of environmental drivers were detected. For example, small copepod abundance increased in warmer conditions whereas meroplankton, large copepods and fish larvae either decreased or were unchanged. Our findings highlight recent changes in stratification, reflected by variation in mixed layer depth, and imbalanced nutrient ratios are affecting multiple lifeforms, impacting the North-East Atlantic plankton community. To achieve environmental improvements in North-East Atlantic pelagic habitats, it is crucial that we continue to address climate change and reduce nutrient pollution.

Optimizing Remote Health Monitoring for Digital Platforms: Evaluating the Efficacy of Gamification in Enhancing Data Collection.

In recent years, the integration of game-like elements into non-gaming contexts has shown promise in enhancing user engagement and motivation. This study assesses the impact of gamification elements on data collection efficacy in m-health applications. An ad-hoc mobile application was developed and used in a randomized two-arm pilot study. Participants interacted either with the gamified meal-logging application or with its non-gamified version for ten days. The results from this study emphasize the benefits of incorporating gamification techniques into health applications embedded in digital platforms. While both versions were well-received, reaching high System Usability Scale (SUS) scores (91 and 93.5) and generally positive feedback, the gamified app demonstrated a distinct advantage in promoting user engagement and consistent data logging. This highlights the importance of gamification in health research, suggesting its potential to ensure thorough and consistent data collection, which is essential for producing reliable research outcomes.

Global soil respiration predictions with associated uncertainties from different spatio-temporal data subsets

Soil respiration (Rs), the second-largest flux in the global carbon cycle, is a crucial but uncertain component. To improve the understanding of global Rs, we constructed single global models, and specific models classified by climate type, land cover type, year of the data record, and elevation range using the random forest algorithm to predict global Rs values and explore the associated uncertainty in the models. The results showed a similar overall predictive performance for the models, with an R-squared value greater than 0.63; however, significant differences were observed compared to the global Rs estimate (23 Pg C). All the models estimated larger values of Rs than the single global model, mainly owing to imbalances in the sample data on which the prediction models were based. One exception to this result is the land cover model, which estimates a smaller global Rs for 2020 (95.1 Pg C). Overall, the single global model estimates were closer to those obtained for temperate zones owing to differences in the training data distribution, which resulted in smaller global estimates than those of other classification-specific models. Prediction models using observations before 2000 tend to underestimate the global Rs. However, the use of classification-specific Rs models proved helpful in addressing the persistent temporal and spatial imbalances in Rs sampling. Expanding the coverage of Rs records both temporally and spatially and updating the global Rs database promptly would improve the estimation accuracy of global Rs prediction models while enhancing the understanding of the overall global carbon budget and the feedback of soil carbon with regard to climate warming.

IPLog: An Efficient Log Parsing Method Based on Few-Shot Learning

Log messages from enterprise-level software systems contain crucial runtime details. Engineers can convert log messages into structured data through log parsing, laying the foundation for downstream tasks such as log anomaly detection. Existing log parsing schemes usually underperform in production environments for several reasons: first, they often ignore the semantics of log messages; second, they are often not adapted to different systems, and their performance varies greatly; and finally, they are difficult to adapt to the complexity and variety of log formats in the real environment. In response to the limitations of current approaches, we introduce IPLog (Intelligent Parse Log), a parsing method designed to address these issues. IPLog samples a limited set of log samples based on the distribution of templates in the system’s historical logs, and allows the model to make full use of the small number of log samples to recognize common patterns of keywords and parameters through few-shot learning, and thus can be easily adapted to different systems. In addition, IPLog can further improve the grouping accuracy of log templates through a novel manual feedback merge query strategy based on the longest common prefix, thus enhancing the model’s adaptability to handle complex log formats in production environments. We conducted experiments on four newly released public log datasets, and the experimental results show that IPLog can achieve an average grouping accuracy (GA) of 0.987 and parsing accuracy (PA) of 0.914 on the four public datasets, which are the best among the mainstream parsing schemes. These results demonstrate that IPLog is effective for log parsing tasks.

First insights of integrating the Bonn Internship Curriculum for Point-of-Care Ultrasound (BI-POCUS): progress and educational aspects

BackgroundPoint-of-care ultrasound (POCUS) is rapidly gaining prominence in various clinical settings. As its use becomes more widespread, there is a growing need for comprehensive ultrasound training in medical education to ensure that future healthcare professionals are proficient in this essential diagnostic tool.ObjectiveThis study is the first attempt by the University of Bonn to seamlessly integrate ultrasound courses and the use of ultrasound devices into the regular activities of final year medical students and to evaluate the usage of these devices.MethodsA total of forty students in their practical year were provided with a hendheld ultrasound device for a period of four months. During this time, they were invited to take part in eight optional ultrasound courses in which they acquired images and those images were rated using a specially developed rating system. At the end of the tertial, students were able to take part in a voluntary survey on the use of the equipment.ResultsParticipation in the optional ultrasound courses was well received, with the Introduction and FAST module drawing the largest number of participants (29). Among the ultrasound images acquired by students, those of the lungs obtaining the highest rating, with 18.82 (SD ± 4.30) points out of 23 points, while the aorta and vena cava images scored lowest, with an average of 16.62 (SD ± 1.55) points. The overall mean score for all images was 17.47 (SD ± 2.74). Only 21 students responded to the survey. Of the participating students, 67% used the device independently four times or fewer during the tertial.ConclusionThe study aimed to enhance the BI-POCUS curriculum by improving students' ultrasound skills during their practical year. However, device usage was lower than expected, with most students using it only once a month or less. This raises concerns about the justification of the effort and resources. Future initiatives will focus on technical improvements, better login data provision, and closer monitoring of usage and progress, emphasizing the need for practical ultrasound training in medical education.

Automatic recognition of airliners wake turbulence using various techniques of machine intelligence

Airplanes generate severe turbulent airflow, known as trailing vortices, which can have an adverse control impact on following airplanes. Aviation authorities worldwide impose separation standards between leading and following airplanes to reduce the capacity of airports to handle large volumes of traffic. Optimizing these separations is highly sought after. These separations are optimized based on several factors such as airplane size, weight, and design in addition to weather conditions. The recognition of trailing vortices is usually done manually through examination of pilot reports and analysis of flight data recorders (FDRs). This process can be subjective and depends greatly on the analyst's experience. Automated processes based on Artificial Intelligence (AI), such as Machine Learning (ML) and Computational Intelligence (CI), have shown a potential to better deal with such problems. Results obtained from these various techniques are presented to show their degree of appropriateness. Flight event identification, such as encounters with airplane vortices, wind shear, turbulence, and uncommanded control, can help establish aircraft warning and monitoring systems to enhance automatic flying, especially during landing and take-off. The developed model can attain great assurance recognition of the aircraft wake turbulence by supporting the air traffic controllers with the decision information in actual work to ensure aircraft safety. The success rates obtained from the investigated CI models compare favorably well to results obtained by either manual and/or other CI-based techniques when applied to vortex identification.

Rancang Bangun Green Portable Cooler Box

Freon is one of the refrigerants used in refrigeration systems. The refrigeration system has a very important role, namely to preserve food ingredients so that food can last longer. The use of freon is not only used in ships but is widely found in home appliances in refrigeration systems used in freezers, air conditioners, refrigerators, and dispensers. If more freon is used, the negative impact that arises is greater, namely the depletion of the ozone layer and global warming which can have a bad influence on health, the life of living things and the environment. The purpose of this researcher is to create a tool to reduce the negative impact of freon use, namely by using peltier as a cooling medium with environmentally friendly thermoelectric modules. This study uses trial and error methods. Where the trial process is carried out by turning on one of the working push buttons and observing the temperature value. Then it is processed by the control system, namely Arduino Mega which will later provide a PWM output signal to the IRF540 mosfet when the output temperature value is in line with the reference value and vice versa. The test of this tool was carried out by comparing measurements between the author's prototype tool and the standard measuring tool, namely the digital thermometer. Based on the test results, this device can produce a minimum temperature of 140C at 30-minute intervals without any media. The trial using 330ml bottled water can reach a temperature of 170C within 30 minutes in the cooler box. This tool is also equipped with an automatic monitoring system using a PWM signal in the form of a change in fan speed, which is 255 RPS maximum speed and when getting a PWM signal it changes to 50 RPS. The percentage of errors resulting from the comparison of standard measuring instruments and sensor values had the highest and lowest percentages of 2.37% and 0.84% in cold dispersion conditions without charge. Then the highest and lowest error percentages were 1.69% and 0.72% in the state of containing a load of 1 330ml mineral bottle. This ptototype uses data recording using Micro-SD as a data logger.

Smart IoT SCADA System for Hybrid Power Monitoring in Remote Natural Gas Pipeline Control Stations

A pipeline network is the most efficient and rapid way to transmit natural gas from source to destination. The smooth operation of natural gas pipeline control stations depends on electrical equipment such as data loggers, control systems, surveillance, and communication devices. Besides having a reliable and consistent power source, such control stations must also have cost-effective and intelligent monitoring and control systems. Distributed processes are monitored and controlled using supervisory control and data acquisition (SCADA) technology. This paper presents an Internet of Things (IoT)-based, open-source SCADA architecture designed to monitor a Hybrid Power System (HPS) at a remote natural gas pipeline control station, addressing the limitations of existing proprietary and non-configurable SCADA architectures. The proposed system comprises voltage and current sensors acting as Field Instrumentation Devices for required data collection, an ESP32-WROOM-32E microcontroller that functions as the Remote Terminal Unit (RTU) for processing sensor data, a Blynk IoT-based cloud server functioning as the Master Terminal Unit (MTU) for historical data storage and human–machine interactions (HMI), and a GSM SIM800L module and a local WiFi router for data communication between the RTU and MTU. Considering the remote locations of such control stations and the potential lack of 3G, 4G, or Wi-Fi networks, two configurations that use the GSM SIM800L and a local Wi-Fi router are proposed for hardware integration. The proposed system exhibited a low power consumption of 3.9 W and incurred an overall cost of 40.1 CAD, making it an extremely cost-effective solution for remote natural gas pipeline control stations.

Mouse Exploratory Behaviour in the Open Field with and without NAT-1 EEG Device: Effects of MK801 and Scopolamine.

One aspect of reproducibility in preclinical research that is frequently overlooked is the physical condition in which physiological, pharmacological, or behavioural recordings are conducted. In this study, the physical conditions of mice were altered through the attachments of wireless electrophysiological recording devices (Neural Activity Tracker-1, NAT-1). NAT-1 devices are miniaturised multichannel devices with onboard memory for direct high-resolution recording of brain activity for >48 h. Such devices may limit the mobility of animals and affect their behavioural performance due to the added weight (total weight of approximately 3.4 g). The mice were additionally treated with saline (control), N-methyl-D-aspartate (NMDA) receptor antagonist MK801 (0.85 mg/kg), or the muscarinic acetylcholine receptor blocker scopolamine (0.65 mg/kg) to allow exploration of the effect of NAT-1 attachments in pharmacologically treated mice. We found only minimal differences in behavioural outcomes with NAT-1 attachments in standard parameters of locomotor activity widely reported for the open field test between the drug treatments. Hypoactivity was globally observed as a consistent outcome in the MK801-treated mice and hyperactivity in scopolamine groups regardless of NAT-1 attachments. These data collectively confirm the reproducibility for combined behavioural, pharmacological, and physiological endpoints even in the presence of lightweight wireless data loggers. The NAT-1 therefore constitutes a pertinent tool for investigating brain activity in, e.g., drug discovery and models of neuropsychiatric and/or neurodegenerative diseases with minimal effects on pharmacological and behavioural outcomes.

Assessing the impact of climate change on aircraft engine performance during different flight phases (take-off and cruise) and the environmental consequences

PurposeClimate change significantly impacts global temperatures, posing challenges to various sectors, including aviation. The purpose of this study is to assess the impact of climate change on aircraft engine performance during different flight phases (take-off and cruise) and the environmental consequences.Design/methodology/approachThis study examines the effects of rising temperatures on aircraft engine performance using real-time data from a Boeing 787-8 equipped with GEnx-1B engines, which are collected via Flight Data Recorder of the engines and were analyzed for the take-off and cruise phases on the ground. Exhaust gas temperature (EGT), fuel flow and take-off weights were evaluated.FindingsThe analysis revealed a significant increase in EGT at the cruising altitude of 38,000 ft during the summer months compared to expected standard atmospheric values. This increase, averaging over 200 °C, is attributed to global warming. Such elevated temperatures are likely to accelerate the degradation of turbine components, resulting in increased fuel consumption: higher EGT signifies inefficient engine operation, resulting in more fuel burned per unit thrust; early engine aging: elevated temperatures accelerate wear and tear on turbine components, potentially reducing engine lifespan and increasing maintenance costs and enhanced atmospheric pollution: incomplete combustion at high EGTs generates additional emissions, contributing to local air quality concerns.Practical implicationsThe research findings have practical implications for understanding the potential operational challenges and environmental impacts of climate change on aircraft engine performance. This lets us explore mitigation strategies and adapt operational procedures to ensure sustainable regional aviation practices.Originality/valueThis research enhances environmental consequences by assessing the impact of climate change on aircraft performance.

Dynamometer card generation for pumping units based on CNN and electrical parameters

In the actual production process of oil fields, the real-time and accurate acquisition of dynamic recorder data from the pumping unit is of great significance for the diagnosis of well failures. The traditional method of obtaining the card of the dynamometer usually includes installing a load sensor on the auxiliary head of the pumping unit. However, due to the harsh environment of the oil field production site, these load sensors often suffer from damage, distortion, and aging, resulting in large measurement errors and low reliability. This paper proposes a mixed model of pumping based on motor electrical parameter data and CNN convolutional neural network, which has good consistency with actual data in terms of predictive performance. Thus, the highlights of this paper can be summed up in two points: (1) Based on the mathematical model of the AC motor, the speed of the motor and the torque output of the motor are accurately estimated. (2) The convolutional neural network is introduced to compensate for the errors caused by the defects of the pumping unit mechanism model.

Evaluating high-resolution aviation emissions using real-time flight data

Amidst robust global economic growth and advancing globalization, the aviation market is poised for significant expansion. Consequently, the environmental impact of aviation emissions is growing in significance. However, due to limitations in real flight data and aviation emissions index models, further clarification of the emission characteristics throughout entire flights is necessary. To better assess the emission characteristics of entire flights, this study employs real Quick Access Recorder (QAR) data and a high-precision aviation emissions index model, yielding four-dimensional emission data (time, longitude, latitude, altitude) from flights. The analysis compares QAR data with emissions from scheduled flight data (SFD) and Broadcast Automatic Correlation Monitoring (ADS-B) projections, explores seasonal variations in aviation emissions, and assesses the impact of sustainable aviation fuels (SAFs) on emissions reductions. For both number and mass of nvPM emissions, as well as nitrogen oxide emissions, the rankings are: ADS-B-E > SFD-E > QAR-E; for CO, SFD-E > ADS-B-E > QAR-E, particularly during the climb-cruise-descent (CCD) cycle. There are significant differences in the emission of aviation pollutants in airport area and high-altitude area in different seasons. Employing four types of Sustainable Aviation Fuels (SAFs) significantly reduces both the mass and the number of nvPM emissions. Therefore, it is recommended to utilize more QAR data to refine the assessment of the environmental impact of aviation emissions.

»Relationships are Key« A Semantic Relationship Awareness Framework for Operational Technology Monitoring

Critical infrastructures in areas like road traffic management naturally rely on the broad use of "Operational Technology (OT)" to ensure efficient and safe road traffic monitoring (RTM) through "OT objects", like sensors and actuators, whereby monitoring OT itself ("OTM") is evenly crucial. OTM is highly challenging, not least due to massive heterogeneity of OT, immense complexity and size, and omnipresence of evolution. As a consequence, knowledge about interdependencies between OT objects in form of semantic relationships is often outdated or simply not available. Thus, in case of incidents, detection of cause and effect in the sense of a situational picture of OT is missing. In order to counteract this fundamental deficiency, we aim to automatically discover semantic relationships between OT objects, to build up an ontological knowledge base as prerequisite for achieving OT situation awareness. Thereby, the contribution of this paper is threefold. First, a systematic exploration of the induced challenges is provided, derived from an in-depth analysis of real-world OT message logs in the area of RTM. Based on that, we sketch out a research roadmap, thereby guiding the identification of existing concepts and technologies appearing to be useful for realizing a framework for semantic relationship awareness, being the crucial pre-step for achieving OT situation awareness. Finally, a first proof-of-concept prototype is put forward, complemented by an evaluation of its applicability and a detailed comparison to related approaches.

Shrub encroachment of coastal ecosystems depends on dune elevation

Woody plant encroachment is influenced by interactions between the physical environment and vegetation, which create heterogenous microenvironments some of which favor shrub recruitment through mitigation of the abiotic environment. Encroachment of native shrub, Morella cerifera into grasslands on Hog Island, Virginia has been attributed to warmer winter temperature; however, recruitment of seedlings in grasslands may be impacted by multiple factors at the level of the microhabitat. Our study focuses on a critical gap in understanding factors specifically influencing M. cerifera seedling recruitment and survival. By experimentally planting M. cerifera seedlings at varying dune elevations and grass densities, we tested hypotheses that dune elevation influences the microclimate, soil characteristics and vegetation cover and that grass cover/density is related to shrub establishment. We tested these hypotheses through gathering data from temperature data loggers, conducting soil water content and chloride analyses, and determining percent cover of grasses relative to dune elevation. Results indicate that dune elevation was positively related to moderated temperatures with reduced temperature extremes and vegetation cover/composition that led to favorable locations for M. cerifera establishment and growth. Where dune elevation is > 2 m, we document an upper limit of grass cover on natural seedling establishment, suggesting a switch from facilitative to competitive effects with grass density. Overall, our work demonstrates interactions between dune elevation and medium grass density has a facilitative influence on M. cerifera establishment and can be used for future predictions of shrub growth with rising sea-levels.