Accurate Method For Assessment Research Articles

TREE COVER AREA ESTIMATION IN EUROPE BASED ON THE COMBINATION OF IN SITU REFERENCE DATA AND THE COPERNICUS HIGH RESOLUTION LAYER ON TREE COVER DENSITY

Abstract. There is a natural tendency from the remote sensing community to extract area statistics (i.e. “Pixel counting”) from EO based geospatial products to produce statistical indicators for various purposes. However, geospatial map products suffer from misclassification errors and “pixel counting” can only be justified when the accuracy of such map products reaches a level when these misclassification errors can be considered negligible, but this is possible only in very specific circumstances. Nevertheless, there has been some effort in the Remote Sensing community to assess the accuracy of map products against some form of reference data to ensure that the maps could reach a sufficient level of accuracy. However, there is generally a lack of standards and guidelines and how to perform rigorous map accuracy assessment and rigorous methods for assessing map accuracies and extracting statistics are still lacking as highlighted by McRoberts (2011). Despite substantial advances in this topic in the scientific literature in recent years notably with the paper from Olofsson et al. (2014), this has yet to be fully implemented in operational projects. In addition, even if map accuracy assessment is performed correctly, high accuracy does not necessarily mean that area statistics can be directly extracted from a map.This study is focused on developing the rigorous and appropriate use (i) of geospatial map products from satellite imagery and (ii) statistically sound methods for reporting area estimates and their associated uncertainty.

An Image Information-Based Objective Assessment Method of Technical Manipulation Skills for Intravascular Interventions.

The clinical success of vascular interventional surgery relies heavily on a surgeon's catheter/guidewire manipulation skills and strategies. An objective and accurate assessment method plays a critical role in evaluating the surgeon's technical manipulation skill level. Most of the existing evaluation methods incorporate the use of information technology to find more objective assessment models based on various metrics. However, in these models, sensors are often attached to the surgeon's hands or to interventional devices for data collection, which constrains the surgeon's operational movements or exerts an influence on the motion trajectory of interventional devices. In this paper, an image information-based assessment method is proposed for the evaluation of the surgeon's manipulation skills without the requirement of attaching sensors to the surgeon or catheters/guidewires. Surgeons are allowed to use their natural bedside manipulation skills during the data collection process. Their manipulation features during different catheterization tasks are derived from the motion analysis of the catheter/guidewire in video sequences. Notably, data relating to the number of speed peaks, slope variations, and the number of collisions are included in the assessment. Furthermore, the contact forces, resulting from interactions between the catheter/guidewire and the vascular model, are sensed by a 6-DoF F/T sensor. A support vector machine (SVM) classification framework is developed to discriminate the surgeon's catheterization skill levels. The experimental results demonstrate that the proposed SVM-based assessment method can obtain an accuracy of 97.02% to distinguish between the expert and novice manipulations, which is higher than that of other existing research achievements. The proposed method has great potential to facilitate skill assessment and training of novice surgeons in vascular interventional surgery.

Measurement of Heart Rate Variability (HRV) and Stress Index After Consumption of 4 Cups of Instant Coffee in 1 Day in A 60-Year-Old Man

The Measurement of Heart Rate Variability is the most accurate method for quantitative assessment of both divisions of the autonomic nervous system (sympathetic and parasympathetic) as well as sympatho-vagal balance.

Development and Validation of TaqMan Chemistry Probe-Based Rapid Assay for the Detection of Echinocandin-Resistance in Candida auris.

Candida auris is a multidrug-resistant yeast pathogen causing outbreaks in health care facilities worldwide, and the emergence of echinocandin-resistant C. auris is a concern. Currently used Clinical and Laboratory Standards Institute (CLSI) and commercial antifungal susceptibility tests (AFST) are phenotype-based, slow, and not scalable, limiting their effectiveness in the surveillance of echinocandin-resistant C. auris. The urgent need for accurate and rapid methods of assessment of echinocandin resistance cannot be overstated, as this class of antifungal drugs is preferred for patient management. We report the development and validation of a TaqMan chemistry probe-based fluorescence melt curve analysis (FMCA) following asymmetric polymerase chain reaction (PCR) to assess mutations within the hot spot one (HS1) region of FKS1, the gene responsible for encoding 1,3-β-d-glucan synthase that is a target for echinocandins. The assay correctly identified F635C, F635Y, F635del, F635S, S639F or S639Y, S639P, and D642H/R645T mutations. Of these mutations, F635S and D642H/R645T were not involved in echinocandin resistance, while the rest were, as confirmed by AFST. Of 31 clinical cases, the predominant mutation conferring echinocandin resistance was S639F/Y (20 cases) followed by S639P (4 cases), F635del (4 cases), F635Y (2 cases), and F635C (1 case). The FMCA assay was highly specific and did not cross-react with closely and distantly related Candida and other yeast and mold species. Structural modeling of the Fks1 protein, its mutants, and docked conformations of three echinocandin drugs suggest a plausible Fks1 binding orientation for echinocandins. These findings lay the groundwork for future evaluations of additional FKS1 mutations and their impact on the development of drug resistance. The TaqMan chemistry probe-based FMCA would allow rapid, high throughput, and accurate detection of FKS1 mutations conferring echinocandin resistance in C. auris.

Integrity assessment methodology of casing ovality deformation in shale gas wells

Casing deformation frequently occurs in shale gas wells during hydraulic fracturing. While the failure mechanism of casing ovality deformation is unclear due to the interweaving and compounding reasons from geological and engineering complexity. The multi-arm caliper tool can quantify the severity of casing deformation, and the spatial and temporal occurrence of casing deformations is analyzed. A finite element model of fractured shale squeezing casing is established to simulate the process of casing deformation. The simulated casing deformation is compared with logging data to validate the model with which the deformation mechanism would be revealed. Simulation results indicate that casing deformation in the Luzhou shale gas field belongs to compressive deformation. The length of casing deformation and the diameter shrinkage are local and small, and the deformation points are scattered along the horizontal well trajectory. The fracturing fluid pressure and fractured shale block generate a nonuniform load on the casing, which leads to stress yield and ovality deformation. The fluid pressure, formation degradation and cementing material are the key controlling factors to mitigate casing ovality deformation. This work provides a prompt & accurate assessment method and controlling measures for casing integrity in shale gas wells.

Risk Assessment Method for the Safe Operation of Long-Distance Pipeline Stations in High-Consequence Areas Based on Fault Tree Construction: Case Study of China–Myanmar Natural Gas Pipeline Branch Station

The safe operation of natural gas long-distance pipeline stations is the critical link for its sustainable transmission. Therefore, station operation risk assessment and space-time expression in high consequence areas have become a critical problem that must be urgently addressed. The conventional way to assess the probability of gas pipeline failure cannot meet the requirements of the natural gas operating companies for the safety assessment of natural gas stations. Taking the example of the China–Myanmar natural gas pipeline branch station, based on the fuzzy fault tree model (FFT), a comprehensive pipeline safety evaluation method (CPSE-FFT) has been introduced, which evaluates the risk factor of pipeline operation in the station area and the social security risk brought by it. In this case study, the method is applied to a branch station of the China–Myanmar long-distance pipeline. In the results, the probability of pipeline accidents in the area of the station is 5.79×10−4 times/year, and six buildings with higher risk levels among the 37 buildings are also exposed on the risk distribution map. The overall social risk level is also expressed. CPSE-FFT is a practical, reasonable, and accurate assessment method that can quantitatively reflect the risks posed by the safe operation of natural gas stations. This method is suitable for most natural gas station risk assessments. The results of the risk assessment provide a reference for managers and decision-makers to reduce the risk of natural gas stations.

Time series prediction based on machine learning: A case study, temperature forecasting in Vietnam

In recent years, there has been a surge in interest in the subject of machine learning for prediction. In this study, a temperature dataset of Vietnam’s stations is examined in order to anticipate temperature. Several forecasting models are used to accomplish this goal. First, a traditional time-series forecasting approach such as Seasonal Autoregressive Integrated Moving Average is used (SARIMA). Then, more complex approaches such as XGBoost, Encoder-Decoder, and Prophet are used. The models' performances are compared using several accuracy assessment methods (e.g., Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE)). The findings demonstrate the superiority of the deep learning approach over the other methods.

Routine Use of Lateral Vertebral Assessment With DXA Scan for Detection of Silent But Debilitating Vertebral Fractures

Reduced bone mineral density is a major public health dilemma with high prevalence. Vertebral fracture (VF) is an independent risk factor for fragility fracture. Lateral vertebral assessment (LVA) in dual-energy x-ray absorptiometry is a reliable, low-radiation, accurate, and cost-effective method for VF assessment. Five hundred seventy-five scans of oncologic and nononcologic patients were retrospectively reviewed irrespective of age or sex. Patients' symptoms, bone mineral density, and risk factors were also evaluated. Scans in which LVA was not acquired or had previously known VFs were excluded. The mean age of patients was 66 ± 11.5 years. Eleven percent of patients had VFs on LVA, of which 7 were excluded due to known VFs. Ten percent had new VFs, most of whom were women (n = 42). The most common risk factor was secondary osteoporosis in women and rheumatoid arthritis in men. Sixty-eight percent of the patients had solitary fractures, whereas 32% had multiple fractures. Most of these patients had underlying osteopenia (n = 19). FRAX was calculated twice: once with the history of personal fracture marked and the other time unmarked as these would not have been discovered if LVA was not acquired. Statistically significant mean percent difference of 5.4% was found in probability of major osteoporotic fracture and 2.1% in the mean risk of hip fracture. In our population, 10% patients had unsuspected VFs on LVA in dual-energy x-ray absorptiometry scan. Most of these were nononcologic patients with associated risk factors. Based on the FRAX tool, there is a significant difference in the 10-year risk of fracture when unsuspected fractures discovered on LVA are marked.

Application of geophysical prospecting methods ERT and MASW in the landslide of Daofu County, China

Natural disasters such as debris flow caused by earthquakes seriously threaten the local infrastructure and economy, as well as the lives of people in the area. As the material source of debris flow, it has significance to accurately and effectively study the underground structure of the landslide to prevent debris flow disasters. A landslide has a complex structural system, and its underground characteristics play an important role in its stability. The early identification of fracture surfaces and unstable bodies, and assessment of potential hazards are essential for prevention and protection. The research object of this paper is a landslide that occurred in Yige Village, Xianshui Town, Daofu County, which is on the Xianshui River Earthquake Zone, an area subject to frequent earthquakes. In western Sichuan, the frequent occurrence of landslides has caused considerable economic losses. Developing methods for efficient and accurate risk assessment is a top priority. The Daofu landslide is a typical example of a landslide directly threatening the road below and forming a debris flow channel. The lithology is composed of Jurassic sedimentary rocks, such as marl and clay, covered by limestone. In this study, we combined traditional methods (drilling and field investigation) with two geophysical techniques, multichannel analysis of surface waves (MASW) and electrical resistivity tomography (ERT) to effectively determine the electrical characteristics, velocity characteristics and spatial structure of the landslide. It is found that the buried depth of the sliding surface of the landslide is about 16–20 m. The sliding body above the sliding surface forms a low velocity and low resistivity Quaternary cover. The rock mass below the sliding surface is Triassic Zhuwo Formation sandstone and slate with high velocity and high resistivity. According to comprehensive analysis, the landslide lacks sufficient stability under rainstorm. Our study shows that the use of MASW and ERT can quickly and effectively characterize the subsurface of landslides to assess landslide risk and prevent debris flow hazards.

Dynamic Bayesian network risk probability evolution for third-party damage of natural gas pipelines

Failure and leakage of natural gas pipelines can lead to serious ecological losses and casualties. Third-party damage has become an important cause of pipeline failure and leakage, which urgently needs an accurate risk assessment method to assess the risk. Conventional qualitative risk analysis methods can only point out the critical events of failure accidents but fails to predict the failure probability. This paper proposes a dynamic risk probability analysis method based on Dynamic Bayesian network (DBN), which is validated by a third-party damage case under uncertainty. First, human factors are taken as the main analysis object in the risk analysis, by which two subcategories of intentional and unintentional factors are classified. A complete risk factor analysis is performed by combining expert recommendations with the fault tree analysis method and developing a coupled model with the event sequence diagram. Second, in order to deal with the uncertainty of risk factors, the coupled model is mapped to a DBN model. The prior probabilities of the input DBN model are obtained by database, fuzzy set theory, and Dempster-Shafer evidence theory. Weibull distribution is applied to construct the probability transfer process between time segments, which better fits the characteristics of third-party disruptive factors in onshore pipelines. Finally, the practicality and advantages of the proposed method are demonstrated by a real case study, which identifies 6 critical events and predicts the probabilistic information in different time slices. Furthermore, the method predicts the probability of failure events and potential consequences by processing the time series information, and it is found that the probability of structural damage and explosion is higher than other consequences. In this way, some risk management countermeasures are proposed in a targeted manner. The results show that compared with the conventional BN model which only performs probabilistic inference once, the DBN model can perform temporal dynamic inference to achieve the prediction of failure probability, and it can effectively achieve the numerical prediction of risk failure probability.

Fatigue Life Assessment of API Steel Grade X65 Pipeline Using a Modified Basquin Parameter of the Magnetic Flux Leakage Signal.

This paper presents a modified fatigue life model of the Basquin equation using the stress parameter of the magnetic flux leakage signal. Most pipeline steels experience cyclic loading during service and the influence of the load history makes assessing fatigue behaviour more difficult. The magnetic flux leakage signal's response to a uniaxial cyclic test of API X65 steel was measured with eight levels of ultimate tensile stress loads. The influence of dH(y)/dx on fatigue failure was the main concern in this study, the aim being to represent localised stress parameters in the modified Basquin equation. Both fatigue lives, experimental and predicted from the modified Basquin equation, were validated through reliability analysis, producing a 60% value when approaching 1.8 × 105 cycles. The fatigue data from the experiment produced a higher mean-cycle-to-failure value than the prediction data, with slightly different values of 3.37 × 105 and 3.28 × 105. Additionally, the modified Basquin equation's predicted and the experimental fatigue lives were found to have a high R2 correlation value of 0.9022. The Pearson correlation also showed a good relationship between the fatigue lives, with an r value of 0.9801. Finally, the modified Basquin equation based on dH(y)/dx signals provided an accurate and alternative method for durability assessment.

Evaluation of the Fast Synchrophasors Estimation Algorithm Based on Physical Signals

The goal of this study is to evaluate the performance of the fast algorithm for synchrophasor estimation proposed on the basis of a physical system. The test system is represented by a physical model of a power system with four synchronous generators (15 and 5 kVA). Three synchronous machines represent steam turbine generators, while the fourth machine represents a hydro generator. The proposed method of accuracy assessment is based on comparison of the original and the recovered signals, using values of amplitude and phase angle. The experiments conducted in the study include three-phase faults, two-phase faults and single-phase faults at various buses of the test model. Functional dependencies of initial signal standard deviation from the recovered signal are obtained, as well as those for sampling rate and window width. Based on the results, the following requirements for measurement system and window width are formulated: sampling rate of analog-to-digital converter should be 10 kHz; and window width should start from 5 ms. In addition, the fast algorithm of synchrophasor estimation was tested on event recorder signals. The sampling rate of these signals was 2 kHz. Acceptable window width for event recorder signals is 8 ms. The algorithm was implemented using programming language Python 3 for the testing purposes. The proposed fast algorithm of synchrophasor estimation can be applied in methods for emergency control and equipment state monitoring with short time response.

An Elevation Accuracy Assessment Method for Open DEM Data

An Elevation Accuracy Assessment Method for Open DEM Data

Quantitative risk assessment of typhoon storm surge for multi-risk sources

Typhoon storm surge (TSS) is a complex marine disaster affected by multi-risk sources. Quantitative risk assessment is an important prerequisite for identifying risk areas and designing risk reduction strategies. This paper aims to propose a rapid, accurate, and comprehensive quantitative risk assessment method for TSS under multi-risk sources, including disaster occurrence probability and severity. First, identify the primary risk sources according to the disaster-causing mechanism of TSS. Then, based on the official public data from 1989 to 2020, the dependence structure among multi-risk sources is constructed using Copulas to calculate the probability of each superposition scenario. Meanwhile, build visual scenario databases employing Geographical Information System (GIS) techniques. Subsequently, the extent and depth of inundation are translated into economic risk and population risk using GIS and depth-damage functions. Finally, taking the “Mangkhut” as a case study, the method’s feasibility and accuracy are verified. The results show that the primary risk sources of TSS are storm tide, astronomical tide and coastal waves. The Gumbel Copula is optimal, with OLS (ordinary least squares) and D of 0.0186 and 0.1831, respectively. The probability assessment under different superposition scenarios indicates that the greatest threat of TSS in Guangzhou comes from the storm tide and the astronomical tide. As for the “Mangkhut” case study in Jiangmen City, the assesses occurrence probability is 0.0355%, the accuracy of economic risk assessment (except mariculture) is 95.28%, and the accuracy of population risk assessment is 98.60%. Residences and the disaster-bearing bodies in 0–3 m inundation depth are most severely affected by TSS disasters. Measures such as locating residential and important buildings away from the shoreline (at least 10 km) and ground (above 3 m), formulating disaster emergency plans, and developing the forecast and prevention of storm tides and astronomical tides will help ensure the safety of residents’ life and property. This paper provides an efficient and accurate method, which is of great significance for disaster control, sustainable development, and decision-making.

ACCURACY ASSESSMENT METHOD FOR RIFLED CIVIL WEAPONS

ACCURACY ASSESSMENT METHOD FOR RIFLED CIVIL WEAPONS

Accurate gingival recession quantification using 3D digital dental models

ObjectivesTo develop and validate a method for accurate quantitative assessment of gingival recessions based on superimposition of serial 3D digital models.Materials and methodsGingival recessions of mild (0.5–2 mm) and increased (3–7 mm) severity were simulated on stone casts and surface models were created. The outlines of the gingival margins of the mild (A) and severe recessions (B) were compared to the original gingival margins following 3D best fit superimposition through a gold standard technique (GS), which used intact adjacent structures, and the tested method (CC), which used single tooth crowns at the position of recessions, as superimposition reference. The primary outcome was the distance between the most apical point of each corresponding gingival margin along the respective tooth long axis.ResultsFor mild recessions, the median difference of the test methods (CC_A) from the reference method (GS_A) was 0.008 mm (IQR: 0.093; range: − 0.143, 0.147). For severe recessions, the median difference of the test method (CC_B) from the reference method (GS_B) was 0.009 mm (IQR: 0.091; range: − 0.170, 0.198). The proposed method (CC) showed very high intra- and inter-operator reproducibility (median: 0.025 and 0.033 mm, respectively).ConclusionsThe suggested method offers highly accurate monitoring of gingival margin changes and diagnosis of gingival recessions using 3D digital dental models. The method is applicable irrespective of changes in tooth position or form, allowing for assessments over any time span.Clinical relevanceThe accurate detection and visualization of gingival margin changes in 3D will enhance diagnosis and patient-doctor communication.

Assessing the lidar revolution in the Maya lowlands: A geographic approach to understanding feature classification accuracy

It has been well over a decade since lidar-based research began in earnest in the Maya Lowlands of southern Mexico, Guatemala, Belize, and Honduras. Most investigations have an archaeological focus, with a few integrating studies of the ancient Maya with analyses of local ecology and land-use. A review of frequently cited publications reveals a lack of consistency in assessing the accuracy of archaeological feature classifications in lidar data with variables such as sensor type, class definitions, and ground-truthing methods differentially affecting assessment results across the Lowlands. In general, area-based ground-truthing approaches to classifications of full waveform lidar data present the most comprehensive accuracy assessments. New assessment data from the Buenavista Valley of north-central Guatemala are presented to compare against existing studies and to demonstrate how a geographic approach (a comprehensive, landscape-scale study of features over space and time) to classification error assessment can enhance understanding of classification accuracy. Results show that meaningful comparisons of archaeological features across lidar datasets cannot be considered reliable without more uniform and detailed presentations of accuracy assessment methods, analyses, and results. The article concludes with recommendations for how such collaborations might proceed.

Abstract 15710: Point-of-Care Ultrasonography: Artificial Intelligence in the Assessment of Left Ventricular Function

Introduction: Point-of-care ultrasonography (PoCUS) has become routine for the bedside assessment of patients. Current evidence shows that image acquisition and interpretation of left ventricular function can be achieved reliably by trained users, but evidence is lacking for interpretation by artificial intelligence. We sought to evaluate the accuracy of PoCUS devices with integrated artificial intelligence in comparison to transthoracic echocardiograms for assessment of left ventricular ejection fraction (LVEF). Methods: This is a prospective, multicenter cohort study using the KOSMOS portable ultrasound device (EchoNous, Redmon, WA). PoCUS studies included an apical four and two chamber views and were obtained by either novice trainees or experienced ultrasonographers. A real-time, automated LVEF was calculated by the device using a modified Simpson’s method. These patients also underwent a formal transthoracic echocardiogram, with image acquisition and interpretation completed by blinded ultrasonographers and level-3 trained echocardiographers, respectively. Results: A total of 449 patients were enrolled in the study with 227 and 222 studies completed by novice and experienced scanners, respectively. Images of suitable quality were obtained in 208 of the novice cohort (91.6%) and 216 of the experienced cohort (97.3%). In comparison to formal echocardiography, linear regression shows a R 2 value of 0.82 for the total cohort, and 0.85 for the novice and 0.72 in experienced scanner subgroups. Incorporating an intra-observer variability of ± 5%, the sensitivity and specificity for an abnormal (≤50%) and severely reduced (≤30%) LVEF was 99.2% and 97.7%, and 86.8% and 99%, respectively. Conclusions: Our study is one first to assess the accuracy of PoCUS devices with integrated artificial intelligence. Based on our study, a real-time, automated LVEF can be acquired by PoCUS with strong correlation to formal echocardiography. This study also highlights that diagnostic quality studies can be acquired by novice scanners with comparable accuracy to experienced scanners. This provides evidence for an immediate and inexpensive method for accurate LVEF assessment in emergent and resource-limited environments.

Effect of a sleepless night combined with 2 cups of instant coffee on heart rate variability in an elderly man

Insomnia is harmful and has negative effects on the human body. Coffee is the most commonly used beverage to invigorate. Measurement of heart rate variability is the most accurate method for quantitative assessment of the sympatho-vagal balance of the autonomic nervous system, which determines adaptive capacity to stress. In our Case Report, after 1 sleepless night, the stress index rises and the sympathetic over parasympathetic prevails.

Characteristics of gastroesophageal reflux disease in children with interstitial lung disease.

Childhood interstitial lung disease (chILD) is a rare group of respiratory tract disorders. One of the factors suggested to be associated with its etiopathogenesis is microaspiration related to gastroesophageal reflux disease (GERD). The aim of the present study was to determine the frequency and characteristics of GERD in children with chILD, with a particular focus on proximal GER episodes. This was a prospective cross-sectional study. Consecutive patients with chILD underwent 24-h multichannel intraluminal pH-impedance monitoring. Different types of gastroesophageal reflux episodes (GER) were recorded and compared with regard to the GERD diagnosis. Sixty-two children (median age of 1.22 years) were included. GERD was diagnosed in 20 (32.3%) of them. The GERD (+) and GERD (-) subgroups differed mainly in their esophageal exposure to acid content (2.9 vs. 1.0%, p = 0.02) and bolus exposure (3.0 vs. 1.4%, p < 0.0001), as well as total number of GER (72.5 vs. 42.0 p = 0.0004), acid GER (35.5 vs. 15.0 p = 0.004), and acid proximal GER (21.0 vs. 12.0 p = 0.02). There were no differences in the number of proximal GER comparing GERD (+) and GERD (-) subgroups. The frequency of GERD seems to be relatively high in the population of children with chILD used in this study. However, it has not demonstrated an association between proximal GER and GERD diagnosis in chILD, which casts uncertainty over the microaspiration theory proposed to link the two diseases. The latter conclusion, however, needs to be confirmed using more accurate aspiration assessment methods.