Improve Data Accuracy Research Articles

Accurate analyses of key petrogenetic minor and trace elements in olivine by electron microprobe

Abundances of minor and trace elements in olivine are increasingly used as petrogenetic indicators for mantle source lithologies, mantle metasomatism history, mantle potential temperatures, and magmatic differentiation. As it is common for olivine to be complexly zoned on a fine-scale, high precision analytical methods for EPMA (electron microprobe microanalyzer, or Electron Microprobe) trace element analysis under high spatial resolution have been developed. However, previous studies have focused more on analytical precision with fewer efforts in examining the accuracy of the data. In this study, we used the Cameca SXFive field emission (FE) EPMA to fully evaluate the effects of beam settings, background offsets and background regression models, and primary calibration standards on the data accuracy of 10 key petrogenetic elements (Na, Al, P, Ca, Ti, Cr, Mn, Co, Ni, and Zn) using MongOlSh11–2 olivine as a reference material. Our results indicate that high voltage, high beam current and long counting time not only improve data precision, but also improve data accuracy, especially on elements with low P/B (peak/background) ratios such as Zn and Cr. Importantly, careful background offsets and background regression models need to be obtained via high resolution WDS relative scans or step scans on each target element. Special care needs to be paid to Co element analysis to avoid or correct for peak interference of Fe Kβ. Among 10 minor and trace elements, exponential background regression models need to be applied to Al, Mn, and Ti elements, whereas other elements require linear background regression. Furthermore, to avoid Al and Zn surface contamination due to alumina polishing or brass presence, ultrasonic cleaning between each intermediate polishing steps and plasma cleaning immediately prior to EPMA experiments is highly recommended. Micro-inclusions such as chromite and spinel in olivine or adjacent Ca-rich phases need to be avoided to minimize primary or secondary fluorescence-related contamination on Al, Cr, or Ca. As a volatile element, Na element needs to be analyzed first with appropriate counting time to minimize the Na loss under high beam conditions. It needs mentioning that major elements (Mg, Fe, and Si) are best analyzed using MongOlSh11–2 or San Carlos olivine as primary standards for calibrations, which can yield more accurate data for both major elements and trace elements because of the improved matrix-corrections. Using our recommended analytical protocols, we have successfully discriminated “depleted” mantle olivine cores from an EMORB in northern East Pacific Rise (EPR) via Ca, Ti, Ni, Co, and Mn abundances. Our olivine data from Siqueiros Transform and the nearby 8°20′ N seamounts also help reveal a metasomatized peridotite mantle beneath the northern EPR. Overall, the protocols proposed in this study can serve as a guide for accurate EPMA olivine trace element analyses, which potentially contributes to the efforts of fostering a comparable olivine database worldwide.

Head Start Health and Nutrition Managers' Perceptions of the Process of Measuring BMI and Communicating Preschooler's Weight Status.

Body mass index (BMI) screenings are conducted as part of Head Start's (HS) health and nutrition assessments. Weight status classifications, which rely on the accuracy of the BMI measurements, are communicated to caregivers to engage them in health behavior change. Limited qualitative research has been conducted on the procedures for BMI measurement and reporting in HS programs. Interviews (n=28) were conducted with HS health/nutrition managers in Ohio and North Carolina to understand the processes used to conduct BMI screenings and disseminate reports and identify related needs. Themes included Personnel, Equipment, and Training for BMI Measurements; Classifying and Communicating BMI and Referrals; Professional Development Opportunities; and Resource, Training/Policy Needs to Support BMI Practices. Programs need additional resources to implement BMI measurement training and improve data accuracy and entry. Clarification of the referral/follow-up process and training around communicating with caregivers is also needed to better support families in implementing behavior change.

3D Point Cloud Generation Based on Multi-Sensor Fusion

Traditional precise engineering surveys adopt manual static, discrete observation, which cannot meet the dynamic, continuous, high-precision and holographic fine measurements required for large-scale infrastructure construction, operation and maintenance, where mobile laser scanning technology is becoming popular. However, in environments without GNSS signals, it is difficult to use mobile laser scanning technology to obtain 3D data. We fused a scanner with an inertial navigation system, odometer and inclinometer to establish and track mobile laser measurement systems. The control point constraints and Rauch-Tung-Striebel filter smoothing were fused, and a 3D point cloud generation method based on multi-sensor fusion was proposed. We verified the method based on the experimental data; the average deviation of positioning errors in the horizontal and elevation directions were 0.04 m and 0.037 m, respectively. Compared with the stop-and-go mode of the Amberg GRP series trolley, this method greatly improved scanning efficiency; compared with the method of generating a point cloud in an absolute coordinate system based on tunnel design data conversion, this method improved data accuracy. It effectively avoided the deformation of the tunnel, the sharp increase of errors and more accurately and quickly processed the tunnel point cloud data. This method provided better data support for subsequent tunnel analysis such as 3D display, as-built surveying and disease system management of rail transit tunnels.

RPA success remains a non-trivial endeavor

Robotic Process Automation (RPA) is becoming an integral part of today’s businesses that compete in more dynamic business environments. RPA is a technology that enables organisations to automate rule-based and repetitive tasks in processes through the use of software robots. Instead of requiring time consuming process reengineering that leads to heavy investment on IT system transformations, RPA is able to perform such routine processes by running across systems. For an example, customer orders of a third-party logistics service providing company can be easily entered into internal warehouse management system using RPA. Therefore, RPA can be interpreted as a strategic tool which gives benefits such as improved productivity, improved data accuracy, improved compliance levels, reduced process execution time and costs. However, Nitin Bhatt [1] mentions that, nearly 30% to 50% RPA projects are failures. Accordingly, there are some other factors organisations must consider apart from technological know-how when taking RPA initiatives. This article intends to reveal such factors identified through an industry survey and interview sessions conducted with 12 industry experts who have at least three years of experiences in supply chain and RPA disciplines. Overall, fourteen factors were identified under three categories namely: Strategy; Process; and Organisation.

313.15: Cost-Effectiveness of Interventions to Increase Utilization of Kidneys From Deceased Donors With Primary Brain Malignancy in an Australian Setting

Introduction: Kidneys from potential deceased donors with brain cancer are often foregone due to concerns of transmission risk to recipients. This may be due to uncertainty around donors’ medical history and their absolute transmission risk, or risk-averse decision-making among clinicians. However, transmissions are rare and prolonging waiting time for recipients is harmful. Method: We assessed the cost-effectiveness of increasing utilization of these potential donors using a Markov model patient simulation (Figure 1) to estimate costs and consequences from a payer perspective (Australian government) using linked transplant registry data. We estimated costs and quality-adjusted life-years (QALYs) from three interventions: decision support for clinicians in assessing donor risk, improved data accuracy with real-time data-linkage to hospital records and cancer registries, and increased risk-tolerance to allow intermediate-risk donors. Results: Decision support increased donation 0.3% with a 2% transmission rate and was dominant (improved QALYs and cost-saving) in 56% of simulations (Figure 2). Real-time data-linkage increased donation 0.6% with 1.8% transmissions and was dominant in 57% of simulations. Increasing risk tolerance increased donation by 2.1% with 3.3% transmissions and was dominant in 75% of simulations (mean +10.0 QALYs and $1.2m ($904k USD) cost-savings). Conclusion: Accepting intermediate-risk donors with brain cancer is likely to improve patient outcomes and reduce overall healthcare expenditure.This work was funded by a National Health and Medical Research Council Postgraduate Research Scholarship (APP1191066).

Enhancing motion tracking accuracy of a low-cost 3D video sensor using a biomechanical model, sensor fusion, and deeplearning.

Low-cost 3D video sensors equipped with routines for extracting skeleton data facilitate the widespread use of virtual reality (VR) for rehabilitation. However, the accuracy of the extracted skeleton data is often limited. Accuracy can be improved using a motion tracker, e.g., using a recurrent neural network (RNN). Yet, training an RNN requires a considerable amount of relevant and accurate training data. Training databases can be obtained using gold-standard motion tracking sensors. This limits the use of the RNN trackers in environments and tasks that lack accessibility to gold-standard sensors. Digital goniometers are typically cheaper, more portable, and simpler to use than gold-standard motion tracking sensors. The current work suggests a method for generating accurate skeleton data suitable for training an RNN motion tracker based on the offline fusion of a Kinect 3D video sensor and an electronic goniometer. The fusion applies nonlinear constraint optimization, where the constraints are based on an advanced shoulder-centered kinematic model of the arm. The model builds on the representation of the arm as a triangle (the arm triangle). The shoulder-centered representation of the arm triangle motion simplifies constraint representation and consequently the optimization problem. To test the performance of the offline fusion and the RNN trained using the optimized data, arm motion of eight participants was recorded using a Kinect sensor, an electronic goniometer, and, for comparison, a passive-marker-based motion tracker. The data generated by fusing the Kinect and goniometer recordings were used for training two long short-term memory (LSTM) RNNs. The input to one RNN included both the Kinect and the goniometer data, and the input to the second RNN included only Kinect data. The performance of the networks was compared to the performance of a tracker based on a Kalman filter and to the raw Kinect measurements. The accuracy of the fused data was high, and it considerably improved data accuracy. The accuracy for both trackers was high, and both were more accurate than the Kalman filter tracker and the raw Kinect measurements. The developed methods are suitable for integration with immersive VR rehabilitation systems in the clinic and the home environments.

GROUPING OF SEWING TOOL ASSISTANCE RECIPIENTS USING K-MEANS CLUSTERING ANALYSIS

Aid programs for underprivileged communities need continuous data collection in overcoming welfare problems, what has happened so far is by providing direct assistance to very poor families in every village in Indonesia. Related to this, the problem that has occurred so far is that the direct assistance program is not right on target, because many deserving families should not receive the assistance. The data obtained from the village government is not accurate, it is found that data is considered invalid. This study aimed to determine the distribution of sewing equipment recipients in the best cluster, for sewing equipment recipients in Greged Village, Cirebon Regency. As one way to improve data accuracy, a computational method or model is needed in the form of a data mining algorithm using the k-means clustering method to generate priority groups among hundreds of citizens or the poor. The stages start from data collection, training data, and testing data that consider several criteria from household information, economic conditions, housing conditions, and the number of household members in Greged Village, Cirebon Regency. The results of the tests carried out using 155 data with the best level of accuracy were in the K3 cluster with the Davies Bouldin Index’s value o: - 0.584. With the K-Means method, it is very appropriate to determine the recipient of the sewing equipment program in Greged Village.

Practical kriging models with divide and conquer algorithms for spatial heights forecast

In environmental management, fast decisions and judgments can minimize the severity of the damage. Computer programming techniques and interpolation methods can be utilized to save time and improve data accuracy. This paper presents two algorithms (DVC and SDVC). The first algorithm employs the divide and conquer strategy, whereas the second approach enhances the first by introducing a slope threshold parameter. The kriging models are used to predict spatial heights at unknown locations. The DVC algorithm offers six different kriging models to choose from. The experimental findings demonstrated that our four exponential-based models outperform the Gaussian and Spherical models in terms of accuracy. The two best performances of exponential-based models are utilized to improve the SDVC algorithm. Regarding processing time, the DVC algorithm significantly reduces the waiting time from 7705 to 33 min. The SDVC algorithm decreases the waiting time further down to 4 min but with a trade-off in model accuracy. Finally, we recommend using the SDVC algorithm with a slope threshold of 8%. This research leads to a significant reduction in decision-making time and a practical increase in computation speed when generating 3D surface plots for environmental management applications such as flood protection, transport planning, and farming management.

Sequence Capture From Historical Museum Specimens: Maximizing Value for Population and Phylogenomic Studies

The application of high-throughput, short-read sequencing to degraded DNA has greatly increased the feasibility of generating genomic data from historical museum specimens. While many published studies report successful sequencing results from historical specimens; in reality, success and quality of sequence data can be highly variable. To examine predictors of sequencing quality, and methodological approaches to improving data accuracy, we generated and analyzed genomic sequence data from 115 historically collected museum specimens up to 180 years old. Data span both population genomic and phylogenomic scales, including historically collected specimens from 34 specimens of four species of Australian rock-wallabies (genus Petrogale) and 92 samples from 79 specimens of Australo-Papuan murine rodents (subfamily Murinae). For historical rodent specimens, where the focus was sampling for phylogenomics, we found that regardless of specimen age, DNA sequence libraries prepared from toe pad or bone subsamples performed significantly better than those taken from the skin (in terms of proportion of reads on target, number of loci captured, and data accuracy). In total, 93% of DNA libraries from toe pad or bone subsamples resulted in reliable data for phylogenetic inference, compared to 63% of skin subsamples. For skin subsamples, proportion of reads on target weakly correlated with collection year. Then using population genomic data from rock-wallaby skins as a test case, we found substantial improvement in final data quality by mapping to a high-quality “closest sister” de novo assembly from fresh tissues, compared to mapping to a sample-specific historical de novo assembly. Choice of mapping approach also affected final estimates of the number of segregating sites and Watterson's θ, both important parameters for population genomic inference. The incorporation of accurate and reliable sequence data from historical specimens has important outcomes for evolutionary studies at both population and phylogenomic scales. By assessing the outcomes of different approaches to specimen subsampling, library preparation and bioinformatic processing, our results provide a framework for increasing sequencing success for irreplaceable historical specimens.

Advancing UN Comtrade for physical trade flow analysis: Addressing the issue of missing values

Trade contributes to the redistribution of resources among countries and regions. One of the most widely used data sources is the United Nations Commodity Trade Statistics Database (UN Comtrade). Nevertheless, data issues still limit its validity, trustworthiness, and use. A critical issue is the lack of commodity weight information. It relies heavily on data quality to determine the global market's suppliers and consumers. Thus, trade needs reliable methods for filling in the missing physical values. Using statistical approaches, we estimate missing physical values for commodities, countries/areas, and years. The impact of handled data on countries and commodities varies considerably; for example, South Africa's net weight rose by 117% and clocks’ and watches’ by 63% (HS0, 1988–2019), compared with their original data. The directions of net trade flows for 10594 records have been reversed. Finally, the bilateral asymmetry problem improved. Overall, this paper introduces a novel approach for improving data accuracy.

Curating 62 Years of Walnut Gulch Experimental Watershed Data: Improving the Quality of Long-Term Rainfall and Runoff Datasets

The curation of hydrologic data includes quality control, documentation, database development, and provisions for public access. This article describes the development of new quality control procedures for experimental watersheds like the Walnut Gulch Experimental Watersheds (WGEW). WGEW is a 149 km2 watershed outdoor hydrologic laboratory equipped with a dense network of hydro-climatic instruments since the 1950s. To improve data accuracy from the constantly growing instrumentation networks in numerous experimental watersheds, we developed five new QAQC tools based on fundamental hydrologic principles. The tools include visual analysis of interpolated rainfall maps and evaluating temporal, spatial, and quantitative relationships between paired rainfall-runoff events, including runoff lag time, runoff coefficients, multiple regression, and association methods. The methods identified questionable rainfall and runoff observations in the WGEW database that were not usually captured by the existing QAQC procedures. The new tools were evaluated and confirmed using existing metadata, paper charts, and graphical visualization tools. It was found that 13% of the days (n = 780) with rainfall and 7% of the runoff events sampled had errors. Omitting these events improved the quality and reliability of the WGEW dataset for hydrologic modeling and analyses. This indicated the effectiveness of application of conventional hydrologic relations to improve the QAQC strategy for experimental watershed datasets.

A Machine Learning Approach for Automation of Resume Recommendation System

Abstract: Finding qualified candidates for a vacant post can be difficult, notably if there are many applications. It can stifle team growth by making it difficult to hire the right person at the right time. "A Resume Recommendation System" has the potential to significantly simplify the time-consuming process of fair screening and shortlisting. It would certainly enhance candidate selection and decision-making. This system can handle a large number of resumes by first classifying them using multiple classifiers and then recommending them based on the job description. We offer research to improve data accuracy and completeness for resource matching by combining unstructured data sources and incorporating text mining algorithms. Our method identifies categories by extracting and learning new patterns from employee resumes.

The INDDEX24 Dietary Assessment Platform Compared to Pen and Paper 24-Hour Dietary Recalls: Evidence on Cost, Cost-Effectiveness, and Accuracy From Viet Nam and Burkina Faso

ObjectivesThe INDDEX24 Dietary Assessment Platform (INDDEX24) is an integrated dietary research platform that leverages the INDDEX24 Mobile App and the Global Food Matters Database to accelerate food and nutrition research in low- and middle-income countries. The goal in designing INDDEX24 was to increase the collection of actionable dietary data by developing a solution that would reduce the overall cost and time of surveys, while also improving accuracy.MethodsTo test the accuracy of INDDEX24, rigorous validation studies were conducted comparing INDDEX24 to pen-and-paper 24 HR, with both evaluated against the benchmark of observer-weighed food records (WFR). These interviews were carried out for a sample of women between the age of 18 and 49 in Burkina Faso (n = 231) and Viet Nam (n = 234). An accompanying cost-effectiveness study measured the time and cost required to perform a 24 HR, the time per interview, and the cost-per-unit of accuracy of INDDEX24 compared to pen-and-paper and the WFR. This study used activity-based costing methods to account for the time and money spent to prepare for the study, implement the survey, and process the 24 HR data to produce usable results. To estimate the time per interview, additional respondents (n = 60) were randomly selected from urban and rural areas, and the time of each interview was recorded.ResultsThe accuracy of INDDEX24 was the same or slightly better than pen-and-paper when compared to the weighed food record, and varied by country, food group, and nutrient. From a cost perspective, we found that using INDDEX24 reduced the cost by $65 per respondent in Viet Nam where pen-and-paper cost $820 and INDDEX24 cost $755 per respondent, and by $5 per respondent in Burkina Faso where pen-and-paper cost $544 and INDDEX24 cost $539. INDDEX24 was also more cost-effective (i.e., lower cost per unit of relative accuracy) compared to pen-and-paper due to superior accuracy of a composite nutrient intake measure and lower time and personnel costs for survey administration and data entry.ConclusionsThe rigorous validation studies in both Viet Nam and Burkina Faso concluded that the use of INDDEX24 reduces the costs and time required to implement 24-hour recall studies, while maintaining or improving data accuracy.Funding SourcesBill & Melinda Gates Foundation.

Clearing the air: underestimation of youth smoking prevalence associated with proxy-reporting compared to youth self-report

BackgroundSmoking remains a leading cause of disease burden globally. Declining youth smoking prevalence is an essential feature of effective tobacco control; however, accurate data are required to assess progress. This study investigates bias in youth smoking prevalence estimates by respondent type (proxy-reported, self-report with parent present, or self-report independently) for Aboriginal and Torres Strait Islander and total populations of Australia.MethodsRepeated cross-sectional analysis of representative Aboriginal and Torres Strait Islander Health and National Health Surveys, 2007–2019. Data were restricted to participants aged 15–17 years. Prevalence ratios (PR) and 95% Confidence Intervals (CI) for ever-smoking by respondent type were calculated using Poisson regression with robust standard errors. National youth current-smoking prevalence was estimated if all data were collected by youth self-report; estimates and trends were compared to observed estimates.ResultsOver 75% of all smoking status data were reported by proxy or with parent present. Ever-smoking prevalence among youth self-reporting independently versus proxy-reported was 1.29 (95% CI:0.96–1.73) to 1.99 (95% CI:1.39–2.85) times as high for Aboriginal and Torres Strait Islander youth, and 1.83 (95% CI:0.92–3.63) to 2.72 (95% CI:1.68–4.41) times as high for total population youth. Across surveys, predicted national current-smoking prevalence if all youth self-reported independently was generally higher than observed estimate.ConclusionsEstimates of youth smoking prevalence are likely inaccurate and underestimated if data are collected by proxy or with parent present. Increased reliance on data reported by youth independently is crucial to improve data accuracy, including to enable accurate assessment of national prevalence.

Privacy Protection of Healthcare Data over Social Networks Using Machine Learning Algorithms.

With the rapid development of mobile medical care, medical institutions also have the hidden danger of privacy leakage while sharing personal medical data. Based on the k-anonymity and l-diversity supervised models, it is proposed to use the classified personalized entropy l-diversity privacy protection model to protect user privacy in a fine-grained manner. By distinguishing solid and weak sensitive attribute values, the constraints on sensitive attributes are improved, and the sensitive information is reduced for the leakage probability of vital information to achieve the safety of medical data sharing. This research offers a customized information entropy l-diversity model and performs experiments to tackle the issues that the information entropy l-diversity model does not discriminate between strong and weak sensitive features. Data analysis and experimental results show that this method can minimize execution time while improving data accuracy and service quality, which is more effective than existing solutions. The limits of solid and weak on sensitive qualities are enhanced, sensitive data are reduced, and the chance of crucial data leakage is lowered, all of which contribute to the security of healthcare data exchange. This research offers a customized information entropy l-diversity model and performs experiments to tackle the issues that the information entropy l-diversity model does not discriminate between strong and weak sensitive features. The scope of this research is that this paper enhances data accuracy while minimizing the algorithm's execution time.

Fusion Scheme and Implementation Based on SRTM1, ASTER GDEM V3, and AW3D30

Multi-source data fusion can help to weaken the original data’s shortcomings while improving data accuracy. The experimental area in this research is Taiyuan City in Shanxi Province, China. Using SRTM1 DEM, ASTER GDEM V3, and AW3D30 DEM, the optimal resolution of the Fused DEM in the research area is determined by analyzing the topographic factor information entropy. Then the optimally weighted fusion coefficient of the DEM with root mean square error (RMSE) as the criterion under different slope classes is determined by traversal exploration and quantitatively evaluates the fusion effect. The results show that the optimal resolution of the Fused DEM is 40 m under the terrain feature constraint of Taiyuan city. The fused DEM decreases by 33.8%, 57.9%, and 11.5% for mean absolute error (MAE), 36.3%, 54.6%, and 1.4% for standard deviation (STD), and 32.8%, 54.2%, and 9.7% for root mean square error (RMSE) compared with SRTM1, ASTER GDEM V3, and AW3D30. The weighted average fusion of multiple intensities increased the accuracy of the original data. The reduced topographic factor errors, such as slope, profile curvature, and TPI, improved the Fused DEM’s topographic representation capacity. Furthermore, the results confirm the high accuracy of Fused DEM in complex mountainous regions.

An improved ovine reference genome assembly to facilitate in-depth functional annotation of the sheep genome.

BackgroundThe domestic sheep (Ovis aries) is an important agricultural species raised for meat, wool, and milk across the world. A high-quality reference genome for this species enhances the ability to discover genetic mechanisms influencing biological traits. Furthermore, a high-quality reference genome allows for precise functional annotation of gene regulatory elements. The rapid advances in genome assembly algorithms and emergence of sequencing technologies with increasingly long reads provide the opportunity for an improved de novo assembly of the sheep reference genome.FindingsShort-read Illumina (55× coverage), long-read Pacific Biosciences (75× coverage), and Hi-C data from this ewe retrieved from public databases were combined with an additional 50× coverage of Oxford Nanopore data and assembled with canu v1.9. The assembled contigs were scaffolded using Hi-C data with Salsa v2.2, gaps filled with PBsuitev15.8.24, and polished with Nanopolish v0.12.5. After duplicate contig removal with PurgeDups v1.0.1, chromosomes were oriented and polished with 2 rounds of a pipeline that consisted of freebayes v1.3.1 to call variants, Merfin to validate them, and BCFtools to generate the consensus fasta. The ARS-UI_Ramb_v2.0 assembly is 2.63 Gb in length and has improved continuity (contig NG50 of 43.18 Mb), with a 19- and 38-fold decrease in the number of scaffolds compared with Oar_rambouillet_v1.0 and Oar_v4.0. ARS-UI_Ramb_v2.0 has greater per-base accuracy and fewer insertions and deletions identified from mapped RNA sequence than previous assemblies.ConclusionsThe ARS-UI_Ramb_v2.0 assembly is a substantial improvement in contiguity that will optimize the functional annotation of the sheep genome and facilitate improved mapping accuracy of genetic variant and expression data for traits in sheep.

Four years of global carbon cycle observed from the Orbiting Carbon Observatory 2 (OCO-2) version 9 and in situ data and comparison to OCO-2 version 7

Abstract. The Orbiting Carbon Observatory 2 (OCO-2) satellite has been providing information to estimate carbon dioxide (CO2) fluxes at global and regional scales since 2014 through the combination of CO2 retrievals with top–down atmospheric inversion methods. Column average CO2 dry-air mole fraction retrievals have been constantly improved. A bias correction has been applied in the OCO-2 version 9 retrievals compared to the previous OCO-2 version 7r improving data accuracy and coverage. We study an ensemble of 10 atmospheric inversions all characterized by different transport models, data assimilation algorithms, and prior fluxes using first OCO-2 v7 in 2015–2016 and then OCO-2 version 9 land observations for the longer period 2015–2018. Inversions assimilating in situ (IS) measurements have also been used to provide a baseline against which the satellite-driven results are compared. The time series at different scales (going from global to regional scales) of the models emissions are analyzed and compared to each experiment using either OCO-2 or IS data. We then evaluate the inversion ensemble based on the dataset from the Total Carbon Column Observing Network (TCCON), aircraft, and in situ observations, all independent from assimilated data. While we find a similar constraint of global total carbon emissions between the ensemble spread using IS and both OCO-2 retrievals, differences between the two retrieval versions appear over regional scales and particularly in tropical Africa. A difference in the carbon budget between v7 and v9 is found over this region, which seems to show the impact of corrections applied in retrievals. However, the lack of data in the tropics limits our conclusions, and the estimation of carbon emissions over tropical Africa require further analysis.

APLIKASI PENDAFTARAN SISWA BARU SEBAGAI SISTEM INFORMASI MANAJEMAN BUKU INDUK SISWA SMK AL QOLAM KOTAAGUNG

Admission of new students is a process found in educational institutions in public or private schools. The process of accepting new students is the beginning of the administrative management of student profiles in schools. In an effort to improve the school administration of SMK Al-Qolam Kotaagung, Tanggamus Regency, trying to use Mobile technology as an effort to improve administration and electronic archives by implementing new Student Admissions with Mobile Web. The research method uses the waterfall stage in application development, in collecting data using the interviews, interviews, and library studies. The result of this research is the creation of a Mobile-based Al-Qolam Kotaagung Vocational New Student Admission application that helps prospective students in online registration with the Mobile application. With this application, prospective new students can fill out the registration form for the registration of new students so that it will be easier to recap the data of prospective students, as well as improve data accuracy. The recorded data will be a reference for re-registration and will then become the main book data for each student, thus providing convenience in the administration of Administrative Management and Academic Management at SMK Al-Qolam Kotaagung.

QDC-2D: A Semi-Automatic Tool for 2D Analysis of Discontinuities for Rock Mass Characterization

Quantitative characterization of discontinuities is fundamental to define the mechanical behavior of discontinuous rock masses. Several techniques for the semi-automatic and automatic extraction of discontinuities and their properties from raw or processed point clouds have been introduced in the literature to overcome the limits of conventional field surveys and improve data accuracy. However, most of these techniques do not allow characterizing flat or subvertical outcrops because planar surfaces are difficult to detect within point clouds in these circumstances, with the drawback of undersampling the data and providing inappropriate results. In this case, 2D analysis on the fracture traces are more appropriate. Nevertheless, to our knowledge, few methods to perform quantitative analyses on discontinuities from orthorectified photos are publicly available and do not provide a complete characterization. We implemented scanline and window sampling methods in a digital environment to characterize rock masses affected by discontinuities perpendicular to the bedding from trace maps, thus exploiting the potentiality of remote sensing techniques for subvertical and low-relief outcrops. The routine, named QDC-2D (Quantitative Discontinuity Characterization, 2D) was compiled in MATLAB by testing a synthetic dataset and a real case study, from which a high-resolution orthophoto was obtained by means of Structure from Motion technique. Starting from a trace map, the routine semi-automatically classifies the discontinuity sets and calculates their mean spacing, frequency, trace length, and persistence. The fracture network is characterized by means of trace length, intensity, and density estimators. The block volume and shape are also estimated by adding information on the third dimension. The results of the 2D analysis agree with the input used to produce the synthetic dataset and with the data collected in the field by means of conventional geostructural and geomechanical techniques, ensuring the procedure’s reliability. The outcomes of the analysis were implemented in a Discrete Fracture Network model to evaluate their applicability for geomechanical modeling.