Resource Assessment Research Articles

Assessment of Quality Improvement and Patient Safety Resources, including Event Reporting Systems, in Graduate Medical Education Training Programs - An Institutional National Survey

Background Quality improvement (QI) and patient safety (PS) education were requirements implemented in 2017 by the Accreditation Council for Graduate Medical Education (ACGME) for residents and fellows. Trainees must receive training and experience in QI processes, including an understanding of healthcare disparities, but how training and experience occur remains largely unknown. Our study aimed to determine how institutions have integrated these requirements into medical education. Methods A Google Forms survey was emailed to the Designated Institutional Officials (DIOs) of 650 United States’ institutions with ACGME accredited programs in September 2021. The survey consisted of 34 multiple choice questions related to QI and PS parameters and the event reporting system (ERS); a Likert scale was used. Results More than half (n=392) of the emails were undeliverable, leaving only 258 possible responders; 51 responses were received, and all but one met the inclusion criteria of having at least one training program, which translated to a response rate of 19% (=50/257). The survey found that while 90% of institutions had an institutional QI/PS Committee, only 30% of these institutions also had a Graduate Medical Education-specific QI/PS Committee as well. Most institutions provided QI (88%) or PS training (94%), but only 71% and 83%, respectively, had mandatory training. Most institutions utilized an ERS (96%). While one of the less common methods was education on the ERS, approximately two-thirds of responding DIOs believed that by providing education on the ERS, resident/fellow participation in event reporting would improve, as well as contribute to their total education. Conclusions Further ERS education is warranted to encourage resident/fellow participation to develop various protocols related to QI and PS parameters.

Гипотеза об абсолютной ценности ресурсов недр

Practical assessment of deposits and resources in the Kyrgyz Republic is based on the cost estimation, income and/ or cost methods. The aggregated economic indicators of reduced net cash flows and the internal rate of return, in the dominant sense, reflect the work of capital and premium on capital without legal and economic categories of the mining rent by types of payment for ownership of resources. Currently, the cost of a mining project is defined by the norms of imposed taxes and payments, and, to a large extent, by experimental innovations in the form of a share in the distribution of the net cash flows, bidding and auction payments. Based on the study of the Kumtor mining project for the period of 1996–2020, limits of the mining rent are defined by type, the influence of secondary factors of the economic return from mining and the factor of the project's influence on the macroeconomic indicators of the country. The integrated indicators of the three factors are combined into the category of resource value with the boundaries of revenue sharing for the subsoil owner and the investment capital.

Groundwater recharge estimation in Mediterranean mountain environments by isotope profiles – Partitioning of macropore and matrix flow

Developing and continuously improving effective and feasible methods to estimate groundwater recharge is essential to ensure sustainable resource assessment. Mountain environments represent key areas for the regional water balance. In this study, the isotope profile method was used to investigate water fluxes through the vadose zone and subsequent groundwater recharge in a Mediterranean mountain environment. Six high-resolution soil water isotope profiles were taken at two different locations in the Troodos Massif (Cyprus) in the eastern Mediterranean, focusing on estimating recharge in the soil of hillslopes. Analysis of the isotope profiles yielded recharge rates ranging from 30–70 mm/year, leading to a re-evaluation of previous studies on groundwater recharge. The comparison of results obtained by the peak-shift method and mathematical modeling was used to partition matrix and macropore flow, reaching up to 39 %. Potential methodological limits of the environmental isotope profile method in skeleton-rich soils were found.

A web GIS based integrated water resources assessment tool for Javeh Reservoir

The creation of tools and services for tracking, estimating, and predicting water resource indices that might provide authorities access to information in close to real-time is essentially a must. The considerable development of Web-Geographic Information System Decision Support System (Web-GIS DSS) allows for the analysis of geospatial data and scenarios via the internet without a traditional desktop GIS. The web GIS technology is coupled to techniques which determine water resources Carlson, and Organization for Economic Corporation and Development (OECD) Trophic Status Indices (TSIs), the water resource vulnerability index (WRVI), and social water stress index (SWSI). The water quality indices were determined by helping machine learning (ML) techniques, Decision Tree, Random Forest, and XGBoost. The performances of aforementioned MLs were compared, and the best efficient one, XGBoost, was included in the Web-GIS DSS tool. The developed Web-GIS DSS was examined in Javeh Reservoir, Kurdestan, Iran.The developed Web-GIS DSS allows the users to define the relevant features of the Javeh Reservoir system in pre-defined time horizons such as meteorological, hydrological, inflow water quality, reservoir operation strategies, downstream water demands, geographical external water transfers, etc. The results indicate that the Javeh Reservoir experiences eutrophication status according to the Carlson and OECD indices over time horizons. The water quantity assessment reveals the Javeh Reservoir's susceptibility based on WRVI, although no stress is indicated by SWSI. This susceptibility may be attributed to limited precipitation and water inflow during the simulation period, suggesting a need for comprehensive water resources management.The conclusion drawn from this research underscores the essential role of the developed Web-GIS DSS in decision-making processes for water and environment management, substantiating its practicality with accurate and user-friendly water and environmental data analysis. The tool exemplifies the potential of integrating ML and Web-GIS technologies to promote sustainable environmental practices, thereby establishing a model for future research and policy-making initiatives.

Wind energy resource assessment and wind turbine selection analysis for sustainable energy production

The objective of this study is to perform an analysis to determine the most suitable type of wind turbine that can be installed at a specific location for electricity generation, using annual measurements of wind characteristics and meteorological parameters. Wind potential analysis has shown that the analyzed location is suitable for the development of a wind farm. The analysis was carried out for six different types of wind turbines, with a power ranging from 1.5 to 3.0 MW and a hub height set at 80 m. Wind power potential was assessed using the Weibull analysis. The values of the scale coefficient c were determined, and a large monthly variation was observed, with values ranging from 1.92 to 8.36 m/s and an annual value of 4.95 m/s. Monthly values for the shape coefficient k varied between 0.86 and 1.53, with an annual value of 1.07. Additionally, the capacity factor of the turbines was determined, ranging from 17.75 to 22.22%. The Vestas turbine, with a nominal power of 2 MW and a capacity factor of 22.22%, proved to be the most efficient wind turbine for the specific conditions of the location. The quantity of greenhouse gas emissions that will be reduced if this type of turbine is implemented was also calculated, considering the average CO2 emission intensity factor (kg CO2/kWh) of the national electricity system.

Exploring the Relationship between Built Environment Attributes and Physical Activity in Lower-Income Aging Adults: Preliminary Insights from a Multi-Level Trial.

The built environment has been linked to physical activity (PA) behaviors, yet there is limited knowledge of this association among lower-income midlife and older adults who are insufficiently active. The present cross-sectional study utilized baseline data collected between October 2017 and November 2019 from a clustered randomized controlled trial to determine how built environment attributes were associated with PA behaviors among midlife and older adults (n = 255) residing in or near affordable housing sites (n = 10). At each site, perceptions of the built environment were collected and scored at the participant level via the abbreviated Neighborhood Environment Walkability Survey (NEWS-A), while objective built environment attributes were measured and scored by trained research staff using the Physical Activity Resource Assessment (PARA). Multiple PA behaviors-walking, total PA, and moderate-to-vigorous PA (MVPA) (min/wk)-were measured using the validated Community Healthy Activities Model Program for Seniors (CHAMPS) questionnaire. Adjusted linear regression models examined associations between NEWS-A measures and PA behaviors, and site-level correlations between PARA measures and PA behaviors were examined using Spearman's rank correlations. At the participant level, adjusted models revealed that a one point increase in the NEWS-A aesthetics score was associated with a 57.37 min/wk increase in walking (β = 57.37 [95% CI: 20.84, 93.91], p = 0.002), with a similar association observed for street connectivity and MVPA (β = 24.31 min/wk [95% CI: 3.22, 45.41], p = 0.02). At the site level, MVPA was positively correlated with the quality of the features of local, PA-supportive environmental resources (ρ = 0.82, p = 0.004). Findings indicate that participant- and site-level measures of the built environment may play a role in promoting PA behavior among this demographic and similar populations. Results also suggest that improvements in aesthetic attributes and street connectivity, along with enhancing the quality of local, PA-supportive environmental resources, may be effective strategies for promoting physical activity among lower-income midlife and older adults.

Implementation and efficient evaluation of backpropagation network training algorithms in parametric simulations of soil hydraulic conductivity curve

The estimation of the soil hydraulic conductivity curve (SHCC) through machine learning tools for determining pedotransfer functions (PTFs) is essential in various agriculture, hydrological, and environmental management-related studies. The accuracy and reliability of machine learning tools such as backpropagation neural network (BNN) for estimating SHCC mainly depends on the optimization of its architecture, specifically the selection of the network training algorithms. By comparing the performance of network training algorithms, various researchers related to agriculture science, hydrology and environmental can choose the appropriate algorithm that provides the best balance of accuracy, speed, computational efficiency, and robustness for hydrological modeling purposes. Therefore, the present study assessed and compared the performance of 4 training algorithms including, gradient descent with momentum (GDM), Quasi-Newton (QN), Bayesian regularization (BR) and Levenberg-Marquardt (LM) for the estimation of the parametric-based PTFs of the van Genuchten-Mualem hydraulic conductivity equation as novelty of this research, which has not been investigated, so far. In this study four GDM, BR, LM and QN-based PTFs were derived to estimate the SHCC from different combinations of readily available properties of 86 soil samples from UNSODA database. The integral root mean square error (IRMSE) and coefficient of determination (R2) and were used to assess the estimations. The performance of BR algorithm-based PTFs on testing data varied from 4.634 to 6.690 with average 5.476 cm day−1 in terms of IRMSE, and 0.958 to 0.966 with average 0.963 in terms of R2, which provided better simulation than other training algorithms. The outcome of the present study on testing data showed that the BR algorithm was stable and robust to heterogeneous and noisy data compared to other training algorithms. Finally, development of accurate and reliable PTFs using efficient algorithms can play a crucial role in soil resource assessments, water resource management, irrigation planning, and designing effective drainage systems, because accurate knowledge of SHCC is vital for optimizing irrigation and fertilization practices and minimizing wastage of water and nutrients.

Assessment of EHR Efficiency Tools and Resources Associated with Physician Time Spent on the Inbox

BackgroundPhysicians are experiencing an increasing burden of messaging within the electronic health record (EHR) inbox. Studies have called for the implementation of tools and resources to mitigate this burden, but few studies have evaluated how these interventions impact time spent on inbox activities.ObjectiveExplore the association between existing EHR efficiency tools and clinical resources on primary care physician (PCP) inbox time.DesignRetrospective, cross-sectional study of inbox time among PCPs in network clinics affiliated with an academic health system.ParticipantsOne hundred fifteen community-based PCPs.Main MeasuresInbox time, in hours, normalized to eight physician scheduled hours (IB-Time8).Key ResultsFollowing adjustment for physician sex as well as panel size, age, and morbidity, we observed no significant differences in inbox time for physicians with and without message triage, custom inbox QuickActions, encounter specialists, and message pools. Moreover, IB-Time8 increased by 0.01 inbox hours per eight scheduled hours for each additional staff member resource in a physician’s practice (p = 0.03).ConclusionsPhysician inbox time was not associated with existing EHR efficiency tools evaluated in this study. Yet, there may be a slight increase in inbox time among physicians in practices with larger teams.

Wind Shear Model Considering Atmospheric Stability to Improve Accuracy of Wind Resource Assessment

An accurate wind shear model is an important prerequisite in extrapolating the wind resource from lower heights to the increasing hub height of wind turbines. Based on the 1-year dataset (collected in 2014) consisting of 15-minute intervals collected at heights of 2, 10, 50, 100, and 150 m on an anemometer tower in northern China, the present study focuses on the time-varying relationship between the wind shear coefficient (WSC) and atmospheric stability and proposes a wind shear model considering atmospheric stability. Through the relationship between Monin–Obukhov (M-O) length and gradient Richardson number, the M-O length is directly calculated by wind data, and the WSC is calculated by combining the Panofsky and Dutton (PD) models, which enhances the engineering practicability of the model. Then, the performance of the model is quantified and compared with two alternative methods: the use of annual average WSC and the use of stability change WSC extrapolation. The analysis demonstrates that the proposed model outperforms the other approaches in terms of normal root mean square error (NRMSE) and normal bias (NB). More specifically, this method reduces the NRMSE and NB by 24–29% and 76–95%, respectively. Meanwhile, it reaches the highest extrapolation accuracy under unstable and stable atmospheric conditions. The results are verified using the Weibull distribution.

SRMA: a dual-branch parallel multi-scale attention network for remote sensing images sea-land segmentation

ABSTRACT The use of deep learning-based high resolution remote sensing image sea and land segmentation method has become prevalent in various fields such as environmental monitoring, resource assessment, and urban planning. However, the segmentation process faces challenges due to the complex coastline and the similarity in spectral colour difference between the sea and land in certain areas. The remote sensing images obtained from satellites often exhibit unclear boundaries between the sea and land, as well as intricate coastlines. Consequently, the segmentation of sea and land becomes a difficult task. The existing sea-land segmentation methods have limitations in accurately segmenting long and narrow waters, as well as areas with low differentiation between sea and land. In this paper, we propose a new sea-land segmentation method called SRMA (Swin-Res-Multi-Attention). SRMA performs parallel feature extraction using a dual-branch parallel: Swin-Transformer branch and ResNet branch. The combination of Swin-Transformer’s self-attention and ResNet’s residual design helps reduce the loss of feature information, thereby avoiding incorrect segmentation in regions with low distinctness. To improve overall stability, we introduce the MCA (Multiscale channel attention module) module, which combines feature information from different scales. The multi-scale pyramid design of MCA module ensures stability and improves segmentation accuracy in narrow waters. Experimental results on a public dataset demonstrate that our model outperforms existing methods. Additionally, we evaluate the model’s performance in complex scenarios by creating a new dataset that includes a complex urban water body and cloud interference. The results showcase the robustness and superiority of our model.

Origin, distribution, and CO2 EOR and CO2 storage resource assessment of selected residual oil zone fairways in the Illinois Basin

Residual Oil Zones (ROZs) are volumes of rock with low, largely immobile, oil saturations that provide opportunities for low carbon intensity oil production via carbon dioxide enhanced oil recovery (CO2-EOR) and associated CO2 storage. Oil shows of varying degree have been observed throughout the Illinois Basin (ILB). This work is the first to assess these shows as ROZs.In the ILB, the distribution of ROZ fairways and prospects is controlled by the geologic setting and oil migration history of the basin. ILB ROZ fairways that are stratigraphically proximal to the source rock are generally widespread whereas fairways developed in formations with a greater thickness of intervening strata are more discrete, an indication of influence from fault systems and fracturing associated with major structures. Within these fairways, 41 ROZ prospects were defined across three formations (Tar Springs Sandstone, Cypress Sandstone, Carper Sandstone) containing a best estimate (P50) of five billion barrels (bbls) of original oil in place (OOIP). For context, this volume is 35% of the estimated 14.1 billion bbls of OOIP in the conventional reservoirs in the ILB. In comparison to the Permian Basin, the ILB ROZ OOIP volume is 20% of the estimated 25 billion bbl of ROZ OOIP within a nine-county area of the San Andres Formation.The CO2-EOR resource of the ILB ROZ prospects exceeds 518 million bbls of oil using CO2 injection via pattern flooding. Associated CO2 storage is estimated to be 411 million tonnes of CO2. This study builds on past efforts to establish the CO2-EOR and associated CO2 storage resource potential of the ILB and presents a methodology that can be used to assess ROZ resources in other parts of the world.

Scalable business models for startups in renewable energy: Strategies for using GIS technology to enhance SME scaling

The renewable energy sector has witnessed remarkable growth in recent years, driven by global efforts to mitigate climate change and transition towards sustainable energy sources. Startups play a crucial role in this transition, often pioneering innovative solutions and technologies. However, for these startups to thrive and contribute significantly to the renewable energy landscape, scalable business models are essential. This abstract explores strategies for leveraging Geographic Information System (GIS) technology to enhance scalability in small and medium-sized enterprise (SME) startups operating in the renewable energy sector. GIS technology offers a powerful framework for analyzing and visualizing spatial data, providing valuable insights into site selection, resource assessment, and infrastructure planning. By integrating GIS into their business models, startups can optimize operations, reduce costs, and accelerate growth. This review discusses various strategies for utilizing GIS technology, including market analysis, site suitability assessment, supply chain optimization, and infrastructure planning. Drawing on case studies and industry examples, this review highlights the effectiveness of GIS in enhancing scalability for renewable energy startups. It also addresses the challenges and limitations associated with GIS implementation, such as technical complexities, data accessibility, and cost considerations. Furthermore, this review discusses emerging trends in GIS technology and their potential impact on the renewable energy sector, offering insights into future opportunities for startups. In conclusion, this review emphasizes the importance of GIS technology in facilitating scalable business models for renewable energy startups. By harnessing the power of GIS, SMEs can unlock new growth opportunities, drive innovation, and contribute significantly to the transition towards a sustainable energy future. Keywords: Scalable Business Models, Startups, Renewable Energy, GIS Technology, SME Scaling.

Exploring Medium and Low-Temperature Convective Geothermal Resources Through Controlled Source Audio Magnetotelluric Mothod

Medium and low-temperature convective geothermal resources are a crucial component of China's geothermal wealth, primarily concentrated in the eastern coastal regions. To maximize the utilization of these resources, geophysical surveys are essential, with controlled source audio magnetotelluric(CSAMT) being a prominent and effective method employed in geothermal resource assessments. This study focuses on applying CSAMT to survey specific geothermal sites, including the Xiu Yan Gou Tang Hot Spring, Fengcheng Gou Tang Hot Spring, and Hai Cheng Xi Huang di Geothermal Field. The results obtained through this method provide detailed insights into the distribution of both deep and shallow thermal storage spaces within these fields, shedding light on the intricate thermal circulation systems associated with them. These findings serve as a reliable foundation for planning and executing further geothermal resource development projects. CSAMT emerges as a comprehensive tool for understanding the nuances of medium and low-temperature convective geothermal resources.

Surface water quality prediction in the lower Thoubal river watershed, India: A hyper-tuned machine learning approach and DNN-based sensitivity analysis

The accurate and efficient monitoring, assessment, and prediction of water resources is crucial for the sustenance of life and health of the environment. Traditional methods for assessing water quality can be laborious and time-consuming, but the use of machine learning algorithms can improve the speed and precision of these predictions. The study aimed to evaluate the quality of surface water in the lower Thoubal river using a comprehensive approach that analyzed 12 physico-chemical properties of the water samples collected from 16 different sites. Four machine learning algorithms namely, deep neural network (DNN), gradient boost model (GBM), generalized linear model (GLM), and random forest (RF) were applied and compared for the prediction efficiency of a water quality index (WQI). Sensitivity and uncertainty analysis of the parameters were carried out using a DNN-based model. The study incorporated model evaluation using learning curve along with five key performance assessors. The findings revealed marked variation in the WQI in the watershed. Of the samples tested, about 70 % were found to have good water quality, 18 % had excellent water quality, 11 % had poor water quality, 1 % had very poor water quality, and less than 1 % were deemed unsuitable. Turbidity, BOD, and COD were found to be the most influential parameter towards WQI prediction. The effectiveness of the predictive models ranked as GLM>DNN>RF>GBM, GLM>GBM>DNN>RF, and GLM>DNN>RF>GBM; based on testing, training and 5-fold cross-validation. The GLM model consistently showed superior performance with the lowest RMSEs of 0.14783 in training, 0.15936 in validation, and 0.26115 in 5-fold cross-validation. Conversely, during the training, RF displayed the worst RMSEs of 5.38354, 4.79754 by GBM in validation, and 7.04887 by GBM in 5-fold cross-validation. The analysis using the Taylor diagram and learning curve further supported that GLM was the most effective model for predicting surface water quality. Biological oxygen demand, chemical oxygen demand, electrical conductivity, turbidity and total hardness were found to be the key parameters influencing the water quality. Unregulated and illegal sand mining from the riverbed, garbage dumping into the water bodies, runoffs from agricultural fields have significantly deteriorated the water quality in the study area. The scalable approach of the study and its results can benefit the local water managers and water research community.

Assessment of Second Victim Trauma Resources for Obstetrics and Gynecology Residents [ID 2683424

INTRODUCTION: Second victim trauma (SVT) encompasses psychological, cognitive, or physical symptoms experienced by health care providers after adverse events. Obstetrics and gynecology trainees may be more vulnerable to SVT given their exposure to adverse events and lack of experience. Our objective was to assess the background knowledge of SVT among obstetrics and gynecology residency program directors (PDs) and the curricula and resources available to support obstetrics and gynecology trainees. METHODS: An 18-question survey was developed and disseminated to all 295 obstetrics and gynecology PDs. The survey assessed baseline knowledge of SVT, psychological resources available, knowledge regarding the SVT validated screening tool, and personal experiences. Descriptive and statistical analysis was performed using STATA, Version 12. RESULTS: A total of 99 PDs completed the survey (34% response rate). 92% of PDs had heard of SVT, and 82% felt they could identify its signs/symptoms. 41% of programs had SVT in the curricula. 28% of programs exist in hospitals with an SVT program, and 60% have trained clinician peer supports. Program directors who have an SVT curriculum were twice as likely to be familiar with validated survey tools for SVT (P=.004), and 2.7 times more likely to identify warning signs that a resident may need additional support (P<.001). 86% of PDs would like additional education on SVT. CONCLUSION: The majority of obstetrics and gynecology residency PDs are aware of SVT and have an idea of how to identify this in their residents. The rates of embedded resources vary across the country. Additional education and curriculum development focused on SVT may benefit obstetrics and gynecology PDs and trainees.

Some Approaches to the Assessment of Shale Oil and Gas Resources

Some Approaches to the Assessment of Shale Oil and Gas Resources

Potential recovery assessment of resources embodied in the organic component of municipal wastewater: The case of Zvishavane Urban, Zimbabwe

Traditionally, wastewater treatment plants are designed for the removal of nutrient rich and bacterial components from water to enable disposal into surface water bodies. However, growing population, depleting resource and the need for sustainability call for the implementation of circular economy practices in all aspects. This paper aims to assess the resource recovery potential from municipal wastewater in Zvishavane Urban, Zimbabwe. The potential for water reclamation, energy and nutrient (nitrogen, phosphorous and potassium) recovery from municipal wastewater treatment was based on wastewater data and material flow analysis using the linear and mathematical relationships between physico-chemical parameters of wastewater and the quantities of resources to be recovered. Data input and analysis was done using the Resource Value Mapping online tool and was presented using descriptive statistics. Findings from the study highlighted that 103 660 m3 of water are available for reuse which will meet 22% of water requirements for local agriculture and landscape/greenspace irrigation in Zvishavane Urban. Further, the results demonstrate that valorisation of sewage sludge could potentially yield 92 940Nm3 of biogas, 1557 tonnes of solid fuel and up to US$ 1 739 142.80 in revenue per annum. In terms of nutrient recovery, the study demonstrated that 11 861 tonness of digestate and 3613 tonnes of compost can be derived from Mabula wastewater treatment plant. In terms of nutrient content, there is potential to recover phosphorous (589 tonnes), nitrogen (663.2 tonnes) and potassium (294 tonnes). Potential revenues from nutrient recovery amounted to US$ 1 413 600. The study demonstrates the resource recovery potential of municipal wastewater streams in Zvishavane, a growing town in a developing country. This knowledge is often missing in studies on circular economy in developing countries. Considering wastewater for resource recovery is well aligned with Zimbabwe's National Development Strategy 1 and international priorities as specified in the Sustainable Development Goals and both local. This paper gives testament that wastewater in Zvishavane urban has significant resources embedded in it which can be recovered. Results from this study are important for planners and decision maker in Zvishavane Urban and Zimbabwe as they provide insight into the benefits of resource recovery. This is a necessary way forward in providing sound wastewater management and mapping pathways towards resource recovery from municipal wastewater within the context of not only Zimbabwe but also other developing countries within the region and beyond.

Study on the Evolution Pattern of Complex Topographic Flow Field in Typical Tableland and Hogback Ridge

The flow field in complex mountainous wind farms is highly intricate, affecting the accuracy of wind resource assessments and the reliability of wind turbine operation. In order to study the evolving characteristics of the flow field in typical complex terrains under different inflow wind directions and wind speeds, this paper, based on the open-source software OpenFOAM, adopts a neutral atmospheric boundary layer model combined with Reynolds-averaged turbulence model to construct a high-precision flow field model for complex terrains. The research reveals that this model requires fewer computational resources and can effectively simulate the flow characteristics around complex terrains. Wind resource distribution in tableland terrains and hogback ridge terrains is more influenced by the height of the mountains, and the recovery speed of the wake behind the mountains decreases with a reduction in slope. In hogback ridge terrains, under a wind direction of 0 °, the wind acceleration effect at the top of the hogback ridge is stronger than under a wind direction of 270°, as it experiences more intense compression from the mountains. This paper can provide reference for micro-siting in complex terrain.

Interpolation of wind lidar data supported by MLP

To address the issue of invalid values in the measured wind speed data of the wind lidar, a data interpolation experiment was conducted by using the wind speed data outputted by the wind lidar as the base data source. Based on data preprocessing, the interpolation model was constructed by integrating MLP and the mean wind speed shear index. The accuracy of the interpolated wind speed data was evaluated. The experimental results show that the proposed interpolation method for wind lidar data achieved high accuracy, with a root mean square error of less than 0.75 m/s and an average relative error of less than 3.5%. This approach enhances the reliability and integrity of wind lidar data, providing a reference for the use of wind lidar data in wind resource assessment.

How do Porosity, Size and Interconnectivity of Pores Influence The Values of the Archie Equation Exponents in Carbonate and Sandy Rocks?

Objective: The aim of this study is to investigate the exponents of the Archie equation in order to support the evaluation of hydrocarbon or groundwater reserves through electrical resistivity measurements of subsurface rocks. Theoretical Framework: The Archie equation describes the relationship between the electrical resistivity of a rock and its water saturation and depends on the cementation (m) and saturation (n) exponents. Several authors state that electrical resistivity and the exponents m and n depend on the size, geometry, and connectivity of the pores. Method: This article applies the finite element method to simulate the propagation of the electric field in digital rock models and thus evaluate the Archie exponents. Results and Discussion: Our results indicate that the relationship between these exponents and pore attributes is different for values below or above a threshold. The variables with the greatest influence on m are the porosity of macropores and their interconnectivity, as well as the volumetric fraction of the microporous phase. As for the saturation exponent, the variables with the greatest influence are the porosity of macropores and the interconnectivity of both potentially conductive domains. Research Implications: The practical and theoretical implications of this research are discussed, providing insights into how the results can be applied or influence practices in the field of underground natural resource assessment. These implications may cover the sectors of hydrocarbon production, groundwater, geo-environmental investigations, and geotechnical studies. Originality/Value: This study contributes to the literature by presenting an innovative method for determining the exponents of the Archie equation. The relevance and value of this research are evidenced by its high economic impact on the aforementioned sectors and by its applicability anywhere.