Actual Field Research Articles

Variable gauge length: Processing theory and applications to distributed acoustic sensing

AbstractMost distributed acoustic sensing systems will only process acoustic data with a fixed gauge length for the entire well depth. However, it has been shown that the gauge length is a critical parameter to improve the signal‐to‐noise ratio when used as a function of certain geophysical parameters, such as the apparent velocity. It can also be responsible for significant distortions if tuned incorrectly. In this paper, we first aim to reintroduce the concept of gauge length and derive a robust method to optimize its value based on the geophysical parameters whilst ensuring no distortion to the original signal. We then present a novel method of processing the distributed acoustic sensing data using the concept of a variable gauge length. We finish by showing applications of these techniques on synthetic vertical seismic profiling data and some of the results obtained on actual field datasets.

Analysis of Budget Planning and Control for the Implementation of Sustainable Development Goals (SDGs): A Case Study at Yayasan Kebudayaan Rancage

This study analyzes budget planning and control in a non-profit organization focused on Sustainable Development Goals (SDGs). The main objective is understanding the budget implementation process supporting SDG objectives at Yayasan Kebudayaan Rancage (Rancage Cultural Foundation). Using a qualitative methodology with a single instrumental case study and action research methods, data was collected through observations, interviews, and documentation. Narrative and comparative analysis methods were employed to evaluate the actual field conditions. The study results indicate that the foundation needs a structured budget. Based on these findings, developing a strategic plan, work plan, and detailed budget is recommended. These recommendations aim to create a more systematic budgeting process, enhance awareness, and improve engagement with the SDGs.

Research on the timing for subsequent water flooding in Alkali-Surfactant-Polymer flooding in Daqing Oilfield based on automated machine learning.

Determining the optimal timing for subsequent water flooding in Alkali-Surfactant-Polymer (ASP) flooding is essential to maximizing both the technical and economic outcomes of oilfield blocks. This study identified eight critical parameters that influence the benefits of ASP flooding and established parameter ranges based on data from completed blocks and actual field measurements. The optimal timing for subsequent water flooding was determined by evaluating cumulative net profit variations throughout the ASP flooding lifecycle. Given the complexity and high-dimensional nature of evaluating multiple parameters across diverse blocks, a machine learning-driven optimization model was developed. This model enhances work efficiency by automating complex analyses. However, predictive uncertainties and limitations remain due to the variability in oilfield development and the potential for unpredictable changes in reservoir conditions, external market factors and so on, which may affect the model's results. The model was applied to six blocks in the Daqing oilfield currently in the chemical flooding phase, where injection schemes, such as extending the polymer slug, were adjusted according to the model's optimized results. These adjustments yielded an increase in cumulative net profit of 224.9million CNY compared to the original scheme, with a potential total increase of 752.1million CNY by the end of the flooding process.

Synergistic Effect of Influence Factors on Ammonia Volatilization in Fertilizer Solution: A Laboratory Experiment

Ammonia volatilization, which is one of the main ways that nitrogen gas is released from farmland, restricts promotion of the utilization of nitrogen fertilizer and contains some potential environmental risks. To investigate the general pattern of ammonia volatilization under actual paddy field conditions, we designed an indoor simulated system to measure the amount of ammonia volatilized within a single time period by controlling the pH and concentration of NH4+ (c(NH4+)) in the solution, the gas–liquid interfacial gas velocity, and the ambient temperature. In this paper, the influence of these factors, the synergistic effect on ammonia volatilization, and their quantitative relationship are discussed. We used solutions of ammonium bicarbonate (SAB) and diammonium phosphate (SDP) for the simulation experiments, and the results showed that there is a significant linear relationship between the amount of ammonia volatilization and c(NH4+). The correlation coefficients were between 0.9214 to 0.9897 and 0.8932 to 0.9904 for SAB and SDP, respectively. The quantitative relationship between temperature and pH and the influence factor (CIF) and the initial ammonia volatilization fluxes (IAVFs) was analyzed by the least-squares method, and the degrees of polynomial were one and two, respectively. The regression equations of the SAB and SDP among the amount of ammonia volatilization with the concentration of ammonium nitrogen, the temperature, and the pH were calculated by using MATLAB. Considering the effects of temperature and pH on the CIF and IAVFs under individual conditions, we used a binary cubic model to fit the relationship between temperature and pH to the CIF and IAVFs. The simulation results showed that the correlation coefficients between the CIF and IAVFs for SAB were 0.9980 and 0.9680, respectively, and the correlation coefficients were 0.9946 and 0.9708 for SDP, respectively. The quantitative equation took into account the coefficient of determination and degrees of polynomial, and the ammonia volatilization fluxes can be calculated by using these equations.

Assessment of the Production Variability and Composite Performance Index for Conventional and High Reclaimed Asphalt Pavement Balanced Mix Design Mixtures

The balanced mix design (BMD) concept enables the design of engineered mixtures containing conventional and high reclaimed asphalt pavement (HRAP) contents, moving beyond the constraints of traditional volumetric design methodologies. During production, the designed mixture undergoes verification and potential modifications at the plant to accommodate actual production and field circumstances, regardless of the mix design method. This study assessed the impact of production and associated performance variability on a volumetrically designed control mixture and five mixtures designed with the BMD concept. This investigation showed relatively precise gradation control, but exceedances of volumetric property tolerances were observed in BMD-optimized mixtures during production. Performance, including durability, cracking, and rutting susceptibility, was evaluated using the Cantabro test, indirect tensile cracking test (IDT-CT), and asphalt pavement analyzer (APA) test, respectively. Test results uncovered that produced mixtures may become unbalanced. Observations from the Cantabro test and IDT-CT highlighted the necessity and effectiveness of employing the BMD for HRAP mixtures. The potential aging effect introduced during the reheating process may compromise durability and cracking resistance. In addition, a three-dimensional plot with a revised composite performance index (CPIR) was used to optimize the process of evaluating the mixture “balance” status among multiple primary performances. It revealed that almost all produced HRAP mixtures demonstrated a well-balanced status. Finally, agencies can use the CPIR as part of their acceptance program for BMD mixtures to determine a pay factor for possible bonuses or penalties.

Development of a Prediction Model for Specific Fuel Consumption in Rotary Tillage Based on Actual Operation

Tractor fuel consumption has typically been predicted using indoor test results under specific conditions. This study analyzes the factors affecting fuel consumption during rotary tillage in actual fields and develops a prediction model. The test field was divided into sections using a grid method, and rotary tillage operations were performed to measure various parameters, including soil strength, tractor’s transmission and PTO gear stages, tillage pitch, travel speed, engine and PTO shaft torque and speed, and fuel consumption. Pearson correlation identified variables affecting specific fuel consumption, and regression analysis was used to develop a prediction model. The model’s accuracy was analyzed using the coefficient of determination (R2) and root mean square error (RMSE), and it was compared with the ASABE’s fuel consumption prediction model. The test results showed that higher transmission and PTO gear stages, and tillage pitch decreased specific fuel consumption, while soil strength had no significant effect. Thus, operating at higher gear and PTO stages within suitable conditions enhances energy efficiency in rotary tillage. Statistical analysis showed that specific fuel consumption significantly correlated with tractor travel speed, PTO shaft power, and PTO shaft speed. The prediction model, including these variables, had the highest accuracy with R2 of 0.91 and RMSE of 0.011 L/kW·h. The developed prediction model showed significantly improved accuracy compared to the ASABE model, indicating that it can predict specific fuel consumption based on key operational variables in actual rotary tillage operations.

Precommercial Thinning but Not Commercial Thinning Increases the Merchantable and Large Sawlog Volume Production of Lodgepole Pine on Fair Sites

Abstract The effect of thinning on stand structure and volume production is well studied. However, the effect of combinations of precommercial (PCT) and commercial thinning (CT) on the production of different log grades and differential growth responses is less understood, although it is crucial for forest managers to choose an optimal management regime. Starting with actual operational field data from a lodgepole pine (Pinus contorta var.latifolia) PCT trial on fair quality sites (site index: 16–18 m at 50 years), we used an individual tree growth model to forecast development over the entire rotation. We found thinning changed the structure (density, quadratic mean diameter) of the stands but not the total volume production. However, in terms of merchantable volume and large sawlog volume, stands with only PCT provided the largest volume, more than either CT or a combination of PCT and CT, or the unthinned control. Moreover, our study findings also showed that, by doing only PCT, stands reached maximum merchantable or large sawlog mean annual increment earlier than other thinned or unthinned stands. This indicates that PCT might shorten the rotation length and contribute to an enhanced supply of timber. Study Implications: The effect of commercial thinning (CT) on stand structure and volume production is well studied. However, the effect of combinations of precommercial (PCT) and commercial thinning (CT) on the production of different log grades and differential growth responses is less studied, although it is crucial for forest managers to choose a management option. Starting with actual operational field data from a lodgepole pine (Pinus contorta var.latifolia) PCT trial in a fair-quality site (site index [SI]: 16–18), we used an individual tree growth model to develop the stands over the entire rotation. We found thinning changed the structure (density, quadratic mean diameter) of the stands but not the total volume production. However, in terms of merchantable volume and large sawlog volume, stands with only PCT provided the largest volume, more than either CT or a combination of PCT and CT, or the unthinned control. Moreover, our study findings also showed that, by doing only PCT, stands reached maximum merchantable or large sawlog MAI earlier than other thinned or unthinned stands. This indicates that PCT might shorten the rotation length and contribute to the continuous supply of timber production.

A study on the water content in distribution pole transformer using random forest model

This study proposes and validates an artificial intelligence (AI)-based method for estimating the water content in the insulating oil of distribution-level transformers. The methodology includes data augmentation using noise addition, outlier removal via Isolation Forest, and data normalization through square root transformation. A Random Forest (RF) model is developed to estimate water content based on the usage period of the transformer. Correlation analyses identified the usage period as the key variable affecting water content. The model demonstrated high estimation accuracy with an R-squared value of 0.83, closely aligning estimated values with measured data. This approach provides a practical solution for real-world applications, expanding the focus to distribution-level transformers and ensuring reliable estimations through validation with actual field data. Despite limitations due to a dataset comprising 100 samples of transformer usage and oil analysis data, the method shows promise for accurate transformer lifespan assessment and efficient asset management. Future research will enhance model performance by incorporating diverse environmental conditions and comparative analyses with other machine learning (ML) algorithms, aiming to optimize estimation reliability and safety for distribution-level transformers. Consistency in the methodology description and actual models used will be maintained to avoid discrepancies.

Numerical simulation of the temporal and spatial evolution of sandstone pore type reservoir damage types and severity

During the oilfield development process, various factors can cause different types of reservoir damage, leading to reduced oil well production or even shutdown, and decreased water injection capacity in water wells, resulting in significant economic losses for the oilfield. However, formation damage control measures must be based on the quantitative diagnosis of the types and degrees of reservoir damage. This paper establishes a spatiotemporal evolution numerical model for 12 common types of damage during the oil and gas exploration and development process, based on the material balance theory and Fick’s diffusion law in reservoir damage processes. This model achieves numerical simulation of the degree of various reservoir damage types in different spatiotemporal domains. The overall damage degree of a specific well’s evolution with time and space is further simulated in simple superposition way. The sequential core flow experiment was carried out in the laboratory, and then compared with the calculation results. The accuracy is above 90%. Finally, using field test data, the simulation results show a 95% or higher degree of agreement with the actual field measurements, proving that the reservoir damage spatiotemporal evolution quantitative simulation technology established in this paper has high accuracy and practicality.

Cobalt and Tungsten Extraction from Diamond Core Drilling Crowns by Aqua Regia Leaching.

In this work, a hydrometallurgical process for the recycling of diamond core drilling crowns by means of aqua regia leaching and subsequent alkali leaching was investigated. This investigation continues a previous study in which nitric acid was used for the acid leaching phase. In the current study, higher tungsten recovery was achieved, reaching 98.2%, which is an improvement of about 1.5%. Another advancement of this study was the high Co recovery (97.21%) and the high purity of the tungsten trioxide obtained, comparable to the previously proposed technological process. Furthermore, a novel laboratory method for testing recycled diamond drilling crowns based on infrared thermography was introduced. Although this innovative approach is not the most accurate, it is fast and cost-effective and provides valuable results before the actual field test is conducted as a final evaluation. In addition, the infrared thermography method offers the advantage of non-destructive testing, ensuring that the diamond drilling crowns can be assessed without compromising their structural integrity. Other instrumental methods used to characterize the products and intermediates were X-ray diffraction (XRD), scanning electron microscope with energy dispersive X-ray spectroscopy (SEM-EDS), and laser desorption ionization mass spectrometry (LDI-MS). The analytical method for the concentrations in all working solutions was ICP-AES.

A nonlinear inversion method for Young’s modulus and shear modulus based on the exact Zoeppritz equations

IntroductionYoung’s modulus and shear modulus are essential mechanical parameters for evaluating subsurface rocks, playing a pivotal role in the exploration and development of unconventional resources. Young’s modulus indicates the brittleness of the reservoir, while shear modulus determines the ease of fracturing rock layers.MethodsTraditional methods estimate these moduli through indirect calculations and approximate expressions, which are prone to cumulative errors and rely on multiple assumptions, reducing inversion accuracy. This paper presents a direct inversion method for acquiring of Young’s modulus and shear modulus using the exact Zoeppritz equations, integrated within a Bayesian framework for pre-stack inversion. The quantum particle swarm optimization (QPSO) algorithm is introduced to achieve a nonlinear solution to the objective function.ResultsTests on synthetic and actual field data demonstrate the feasibility and effectiveness of the proposed method, yielding more accurate inversion results compared to traditional methods.DiscussionThese findings provide valuable insights for predicting reservoir brittleness and characterizing reservoirs in unconventional shale gas exploration and development.

Data-Driven Clustering Analysis for Representative Electric Vehicle Charging Profile in South Korea.

As the penetration of electric vehicles (EVs) increases, an understanding of EV operation characteristics becomes crucial in various aspects, e.g., grid stability and battery degradation. This can be achieved through analyzing large amounts of EV operation data; however, the variability in EV data according to the user complicates unified data analysis and identification of representative patterns. In this research, a framework that captures EV charging characteristics in terms of charge-discharge area is proposed using actual field data. In order to illustrate EV operation characteristics in a unified format, an individual EV operation profile is modeled by the probability distribution of the charging start and end states of charge (SoCs).Then, hierarchical clustering analysis is employed to derive representative charging profiles. Using large amounts of real-world, vehicle-specific EV data in South Korea, the analysis results reveal that EV charging characteristics in terms of the battery charge-discharge area can be summarized into seven representative profiles.

Simulation Experiment Research on the Production of Large Box Girders

This paper introduces system simulation technology into large-scale beam field production and uses the simulation software Arena (14.0) to construct a simulation model of the beam field production system considering the randomness of the actual beam field production process operation time. The relationship between the production efficiency of the beam yard and the working time system was studied. In this paper, the improvement in beam-making efficiency in the existing beam field that is achieved by the commonly used reinforcement pre-binding method in the existing beam field is analyzed and calculated, and the improvements in the production efficiency in the ordinary beam field and the intelligent beam field are quantitatively calculated and compared. The results show that (1) when the working time system is increased from 8 h/d to 12 h/d, the average occupancy time of the traditional beam-making pedestal is shortened by 11.5 h when the working time is extended by 1 h per day; (2) with the extension of the working time system, the advantages of the pre-binding method of reinforcement gradually decrease; and (3) the application of intelligent technology not only improves the production efficiency of the beam yard but also makes the beam yard’s production more flexible and more resistant to risks.

Design of waveguide with double layer diffractive optical elements for augmented reality displays

We investigated a diffraction optical waveguide structure with a double layer coupling configuration. This double layer-coupled diffraction optical waveguide structure modulates light information through wavefront modulation for propagation within the optical waveguide and then reproduces the light information through further wavefront modulation, thus achieving optical information transmission. Analysis indicates that the theoretical maximum field of view can reach 90° × 90°. With an actual field of view set to 53° × 53°, an entrance pupil size of 3.2 × 3.2 mm², an eye relief of 10 mm, and 50 pupil expansion, the system’s total energy transmission efficiency is 37%, with field of view uniformity at 91% and uniformity within the eye movement range reaching 97%.

교사멘토링이 초등교사의 교사효능감 및 교직만족도에 미치는 영향 분석

Objectives This study aimed to empirically analyze whether elementary school teachers' participation in mentoring programs affects teacher efficacy and satisfaction. Methods The regression analysis was conducted through propensity score matching using the Teaching and Learning International Survey (TALIS) 2018 data. Results As a result of analysis, it was confirmed that elementary school teachers' participation in mentoring had a negative effect on teacher efficacy. In particular, according to the type of mentoring participation of teachers, the negative effect was greater on teachers participating as mentee than teachers participating as mentor. In addition, it was confirmed that the participation of elementary school teachers in mentoring negatively affects the satisfaction of teaching profession according to the type of participation. In the case of participation as a mentor or mentee in the mentoring program provided by the school and the case as a mentor teacher, the effect on the satisfaction of the teaching profession did not show statistically significant results, but in the case of participation as a mentee teacher, statistically significant negative results were found. Conclusions This study is significant in that it is the first domestic study to empirically verify the causal effect of elementary school teachers' mentoring participation. However, in interpreting the results of this study, if a qualitative study on the negative aspects of the mentoring program felt by teachers in the actual educational field is conducted as a follow-up study in that results contrast to previous studies have been drawn.

환경그림책을 이용한 가정연계 놀이가 5세 유아의 환경 감수성 및 환경 친화적 태도에 미치는 영향

Objectives The purpose of this study is to explore ways to cultivate children's environmental sensitivity and environment-friendly attitudes in early childhood education by examining the effects of home-linked play using environmental picture books on children's environmental sensitivity and environment-friendly attitudes. Methods This study aims to find out the effects of home-based play using environmental picture books on the environmental sensitivity and environmentally friendly attitude of 5-year-old children, and to find ways to foster environmental sensitivity and environmentally friendly attitude of 5-year-old children in early childhood education settings. Results It was found that home-linked play using environmental picture books had a significant difference in the environmental sensitivity and environmentally friendly attitude of 5-year-old children.This shows that home-linked environmental play using environmental picture books is effective in improving the environmental sensitivity and environmentally friendly attitude of 5-year-old children, and it suggests that continuous guidance linked to the home is necessary for the practice of environmental education for young children. Conclusions It is meaningful to verify the effectiveness of home-linked play using environmental picture books and suggests that it can be used for education to form environmental sensitivity and environmentally friendly attitudes as an effective play for young children in the actual early childhood education field.

Deep Learning Transient Electromagnetic Inversion for Seawater Intrusion

Abstract To enhance the capability of transient electromagnetic method(TEM) in detecting seawater intrusion and delineating the boundaries in coastal areas, we developed a deep learning inversion method for TEM data based on the Swin Transformer model in this study. First many standardized resistivity models were designed and generated to describe t the subsurface resistivity structures associated with seawater intrusion in coastal areas .Then, TEM forward modelling was performed to compute the corresponding TEM responses, thereby constructing a seawater intrusion-oriented training dataset. Then, the robust Swin Transformer model was employed as the backbone network to build a deep learning inversion model, named SITEMNet, to derive a direct nonlinear transformation that maps TEM responses to subsurface resistivity models. The proposed SITEMNet inversion technique was validated using simulated data scenarios and actual field TEM measurements, showing great promise in accurately identifying seawater intrusion interface and geological formations.

Design Development and Performance Evaluation of Solar Operated Pigeon Pea (Cajanus cajan L. Millsp.) Pruner

Pigeon pea (Cajanus cajan L. Millsp.) is a vital grain legume known for its contribution to food security and soil fertility. However, pruning, which enhances plant architecture and yield, remains a significant challenge in its cultivation, especially in large-scale operations. Manual pruning is labor-intensive, time-consuming, and costly, which has led to the need for mechanized alternatives, a solar-operated pruner was developed at IGKV, Raipur, to reduce labour, time, and costs. This study focuses on the design, development, and performance evaluation of a solar-operated pruner tailored specifically for pigeon pea. The pruner integrates key components such as a cutting blade, DC motor, solar panel, battery, and charge controller. A 12 V DC motor, generating 10.53 N of force and operating at 8500 RPM, ensures efficient pruning. The motor is powered by a 7.2 Ah battery, which can be charged in 4 hours using an AC adaptor or 8 hours via a 10 W solar panel. The battery allows for 4-6 hours of continuous operation. The pruner's lightweight PVC frame makes it portable and user-friendly, while the protective blade guard ensures operator safety. Performance evaluation of the pruner indicated a field efficiency of 81% and pruning efficiency of 76%. The actual field capacity was measured at 0.097 ha/h, with a walking speed of 1.2 km/h. Losses due to half-cut stems were calculated at 9.04%. The pruner demonstrated its ability to reduce labor costs, improve pruning efficiency, and promote sustainable agriculture through its solar-powered operation. This study highlights the economic and environmental benefits of adopting solar-operated pruners for pigeon pea cultivation.

Pipe experiment elucidates biochar application depth affects nitrogen leaching under crop present condition

Nitrogen leaching, resulting from the inefficient use of fertilizers, pollutes the environment, such as groundwater. Biochar can be applied to farmlands to mitigate nitrogen leaching. The effect depends on the application depth. However, the effect has not been examined under crop-farming conditions. Evaluating the interactions between biochar application depth and crop growth is indispensable for considering depth in the actual field. To address this, we conducted a pipe experiment with four treatments, no biochar (control), surface (0–5 cm), plow layer (0–30 cm), and subsurface (25–30 cm) applications, and compared the results with no-crop conditions from a previous study. Biochar application depth affected soil NO3−−N and NH4+−N absorption ability and also influenced soil-water stress conditions, affecting crop growth. Surface biochar application improved nitrogen absorption and reduced soil-water stress, improving crop growth. The NO3−−N leaching was reduced to 87.7%. Plow layer application worsened nitrogen absorption and resulted in frequent dry stress in the shallow-soil layer, preventing root growth in this layer. The NO3−−N and NH4+−N leaching increased 106.4% and 264.1%, respectively. The effects of subsurface application were similar to those in the control. Selecting an appropriate application depth can simultaneously improve crop growth and reduce nitrogen leaching.

MXene and cellulose nanofibers reinforced hydrogel with high strength and photothermal self-healing performances for marine antifouling

Biological fouling is a considerable threat to the marine industry because attached microcontaminants and microorganisms inflict serious damage on marine installations and aquacultures. Although there has been tremendous effort in researching marine antifouling, it is yet challenging to develop a long-term and consistent antifouling strategy and materials. Here, polyhexamethylene-biguanide (PHMB) and MXene are introduced into the carboxylated cellulose nanofibers (CNF) reinforced polyvinyl alcohol (PVA) hydrogel to obtain a self-healing and ultra-high toughness antifouling hydrogel. The as-prepared double network (DN) hydrogels have extremely high toughness (16,050 kJ/m3), superior anti-protein adhesion (100 %) and anti-algal adhesion (100 %), exceptional antibacterial activity (99.97 %), and remarkable photothermal self-healing capability. Furthermore, the DN hydrogel provide outstanding antifouling performance for at least six months in the actual marine field. This work provides an effective and feasible strategy to prevent marine biofouling and also initiates new approaches to the application of hydrogel.