Accurate Analysis Research Articles

Analysis of the Geometric Accuracy of Wax Models Produced Using PolyJet Molds

The article presents an analysis of the geometric accuracy of wax models produced using PolyJet molds. Photopolymer resin was used to make the molds, and the surface of the model was finished in a gloss mode in the 3D printing process. This allows you to obtain a smooth surface of the molded part model. The geometric accuracy of the models was determined by measurements with the GOM ATOS scanner and analysis of the measurement results in relation to the base model made in the 3D-CAD program. Accuracy analysis was the basis for determining the wear of the mold wear when making subsequent models.

A veterinary-calibrated point-of-care glucometer accurately measures blood glucose concentration in dogs and cats.

To determine the clinical and analytical accuracy of a new veterinary-calibrated portable blood glucose monitor (PBGM) compared to a reference laboratory analyzer. Client-owned dogs (n = 77) and cats (n = 64). Peripheral and paired capillary whole-blood glucose concentrations measured via PBGM were compared to plasma glucose concentrations measured via a Cobas c501 reference analyzer (Roche). Analytical accuracy was evaluated with the Spearman rank correlation coefficient, Bland-Altman difference plot analysis, and Deming regression. Clinical accuracy was evaluated with Parkes error grid analysis. Paired peripheral and capillary blood samples were compared with the Wilcoxon matched-pairs signed-rank test. There was a high correlation between PBGM and reference analyzer readings in dogs and cats. Human quality assurance standards (International Organization for Standardization 15197:2013 guidelines) for analytical accuracy were met for 95% of feline peripheral blood samples and 89% of canine samples. Similar veterinary standards (American Society of Veterinary Clinical Pathology guidelines) were met for 89% of canine and 92% of feline peripheral blood glucose measurements. Error grid analysis showed that all peripheral canine and 97% of feline measurements were clinically accurate (zone A). Any altered clinical decision for the remaining feline measurements was expected to minimally impact outcome (zone B). No significant difference was found between peripheral and capillary blood glucose measurements in either species. The PBGM produced clinically accurate results and is suitable for use in veterinary and home settings to measure blood glucose.

Assessment of Analytical Techniques for Precise Quantification of Four Antiviral Drugs in Pharmaceutical Research and Development: A Comprehensive Review

Abstract: Precise measurement of drug concentration in pharmaceutical research is critical, especially for anti-viral drugs like boceprevir, elvitegravir, indinavir, and saquinavir that combat viral infections. It is well-known that analytical techniques play an imperative role in identifying and characterizing active pharmaceutical ingredients in biological samples and drug formulations. Moreover, precise drug assessment directly influences safety, stability, and efficacy while providing in-depth insight into drug pharmacokinetics. Other than this, analytical techniques also aid in identifying impurities, deteriorated products, and potential pollutants. Thus, reliable analytical methods have become crucial for addressing challenges imposed by complex drug formulations. The most commonly used analytical technique is UV spectrophotometry, which does not have the high sensitivity to detect complex drug formulations. In contrast, Liquid Chromatography-Mass Spectrometry/Mass Spectrometry (LC-MS/MS) merges two analytical techniques, chromatography and mass spectrometry, to accurately quantify biological samples. Furthermore, Ultra-Performance Liquid Chromatography (UPLC) provides enhanced resolution, faster analysis in short duration, and low solvent consumption in contrast to HPLC. This comprehensive review aims to critically assess each analytical approach's accuracy, applicability, selectivity, and limitation to provide valuable insights for researchers and analysts. Understanding the weaknesses and strengths of these analytical techniques will enable the researchers to select the suitable analytical method based on their needs and requirements for quality assessment, precise drug quantification, and optimal therapeutic efficiency. Eventually, this review intends to advance pharmaceutical research and development, specifically for anti-viral drugs, by ensuring the effective and secure administration of therapies.

Preoperative Contrast-Enhanced CT-Based Deep Learning Radiomics Model for Distinguishing Retroperitoneal Lipomas and Well‑Differentiated Liposarcomas

ObjectivesTo assess the efficacy of a preoperative contrast-enhanced CT (CECT) –based deep learning radiomics nomogram (DLRN) for predicting murine double minute 2 (MDM2) gene amplification as a means of distinguishing between retroperitoneal well-differentiated liposarcomas (WDLPS) and lipomas. MethodsThis retrospective multi-centre study included 167 patients (training/external test cohort, 104/63) with MDM2-positive WDLPS or MDM2-negative lipomas. Clinical data and CECT features were independently measured and analyzed by two radiologists. A clinico-radiological model, radiomics signature (RS), deep learning and radiomics signature (DLRS), and a DLRN incorporating radiomics and deep learning features were developed to differentiate between WDLPS and lipoma. The model utility was evaluated based on the area under the receiver operating characteristic curve (AUC), accuracy, calibration curve, and decision curve analysis (DCA). ResultsThe DLRN showed good performance for distinguishing retroperitoneal lipomas and WDLPS in the training (AUC, 0.981; accuracy, 0.933) and external validation group (AUC, 0.861; accuracy, 0.810). The DeLong test revealed the DLRN was noticeably better than clinico-radiological and RS models (training: 0.981 vs. 0.890 vs. 0.751; validation: 0.861 vs. 0.724 vs. 0.700; both P < 0.05); however, no discernible difference in performance was seen between the DLRN and DLRS (training: 0.981 vs. 0.969; validation: 0.861 vs. 0.837; both P > 0.05). The calibration curve analysis and DCA demonstrated that the nomogram exhibited good calibration and offered substantial clinical advantages. ConclusionsThe DLRN exhibited strong predictive capability in predicting WDLPS and retroperitoneal lipomas preoperatively, making it a promising imaging biomarker that can facilitate personalized management and precision medicine.

Evaluation of a continuous glucose monitoring system in healthy dairy calves and adult goats.

To determine the accuracy of a continuous glucose monitoring system (CGMS) device by comparing glucose concentrations measured over time as determined by the CGMS to those of the chemistry analyzer (reference method). 7 healthy goats and 7 dairy calves. A randomized, crossover design with 3 treatments: control, hypoglycemia, and hyperglycemia. The CGMS device was applied to the neck. Hypoglycemia and hyperglycemia were induced by insulin and xylazine, respectively. Glucose concentrations were measured by the chemistry analyzer CGMS, point-of-care glucometer, and intensive care unit machine at 0 (before treatment), 2, 4, 6, 8, 10, and 12 hours. Agreement between the CGMS and the chemistry analyzer was determined by Bland-Altman plots. The analytical and clinical accuracy of the CGMS was determined using the International Organization for Standardization (ISO) 15197:2013 criteria and the Parkes error grid analysis. In goats, the CGMS overestimated glucose concentrations during the hypoglycemic, normoglycemia, and hyperglycemia treatments. In calves, the CGMS underestimated glucose concentrations during the hypoglycemic treatment but overestimated glucose concentrations in normoglycemia and hyperglycemic treatments. The CGMS met the ISO clinical accuracy criteria for goats and calves, with > 99% of the glucose measurements in zones A and B of the Parkes grid. However, the CGMS did not meet the ISO 15197:2013 criteria for analytical accuracy. The CGMS evaluated in our study only met the ISO 15197:2013 clinical accuracy criteria, not the analytical accuracy. Therefore, the device might be considered for clinical use.

Optimal thermometry theory of three-channel wide spectrum based on three-directional difference

The constant spectral emissivity decoupling method within current wide-spectrum thermometry theories stands as a primary factor contributing to accuracy degradation. This creates a deadlock in the current radiation thermometry framework, where the system’s two-dimensional analytical capabilities and resolution accuracy cannot be concurrently achieved, becoming a major theoretical obstacle in the development of this technique. Consequently, based on the Taylor series de-integration method under the wide spectral framework, and taking the first and second derivative terms of spectral emissivity as the starting point, a wide spectral optimization temperature solution theory based on three-directional difference method is proposed. It ensures compatibility and stable solving conditions for imaging systems, while fundamentally removing the dependency on the constantization of spectral emissivity treatment, and realizing the decoupling and inversion of three-channel spectral emissivity. The handling effects of different cutoff precision differential methods on spectral emissivity derivatives are discussed, and the temperature and spectral emissivity solving capabilities of the method are theoretically validated under various spectral emissivity models. Furthermore, this method is used to monitor the continuous temperature rise processes of two different samples. Maximum average relative temperature calculation errors below 6% and 5% are achieved, and the target spectral emissivity variation rate and trend are well reproduced, yielding conclusions consistent with simulations.

Prediction of arsenic concentration in groundwater of Chapainawabganj, Bangladesh: machine learning-based approach to spatial modeling.

Groundwater in northwestern parts of Bangladesh, mainly in the Chapainawabganj District, has been contaminated by arsenic. This research documents the geographical distribution of arsenic concentrations utilizing machine learning techniques. The study aims to enhance the accuracy of model predictions by precisely identifying occurrences of groundwater arsenic, enabling effective mitigation actions and yielding more beneficial results. The reductive dissolution of arsenic-rich iron oxides/hydroxides is identified as the primary mechanism responsible for the release of arsenic from sediment into groundwater. The study reveals that in the research region, alongside elevated arsenic concentrations, significant levels of sodium (Na), iron (Fe), manganese (Mn), and calcium (Ca) were present. Statistical analysis was employed for feature selection, identifying pH, electrical conductivity (EC), sulfate (SO4), nitrate (NO3), Fe, Mn, Na, K, Ca, Mg, bicarbonate (HCO3), phosphate (PO4), and As as features closely associated with arsenic mobilization. Subsequently, various machine learning models, including Naïve Bayes, Random Forest, Support Vector Machine, Decision Tree, and logistic regression, were employed. The models utilized normalized arsenic concentrations categorized as high concentration (HC) or low concentration (LC), along with physiochemical properties as features, to predict arsenic occurrences. Among all machine learning models, the logistic regression and support vector machine models demonstrated high performance based on accuracy and confusion matrix analysis. In this study, a spatial distribution prediction map was generated to identify arsenic-prone areas. The prediction map also displays that Baroghoria Union and Rajarampur region under Chapainawabganj municipality are high-risk areas and Maharajpur Union and Baliadanga Union are comparatively low-risk areas of the research area. This map will facilitate researchers and legislators in implementing mitigation strategies. Logistic regression (LR) and support vector machine (SVM) models will be utilized to monitor arsenic concentration values continuously.

Vortex safety when flying at an assigned flight level

Air traffic intensity between countries and within individual countries is increasing year by year. As a rule, airways follow the same routes. As a result, so-called “roads in the sky” are formed. And where there are roads, there are bumps by the time, in the form of CAT, updraughts and downdraughts and increased turbulence. Horizontal and vertical separation plays an important role in ensuring flight safety on route. Currently, a variety of regulatory documents, defining safe separation at the flight level, has been adopted. Thus, provided there is turbulence in the vortex wake, longitudinal separation is based on the arrangement of aircraft types by three categories according to their maximum certified take-off weight. Since November 2011, the Western standard of vertical separation RVSM (Reduced Vertical Separation Minimum) has been introduced in Russia. Vertical separation is the distance between the vertical flight levels on route. Previously, this distance amounted to 600 m (2000 ft), but due to the increasing intensity of air traffic, it was decided to reduce the vertical separation to 300 m (1000 ft). Hence, at the most common flight level, the vertical separation is 300 m. The question arises if this separation ensures the safety of air transportation? The fact is that the altitude of the flight level does not necessarily coincide with the actual aircraft height. Aircraft altimeters are, inherently, calibrated barometers, that calculate the altitude by the difference in pressure on the ground and in the air. To calculate the height above ground, it would be necessary to constantly input atmospheric pressure data to altimeters at each waypoint and take into consideration the waypoint altitude above the sea level. Consequently, it is customary to use standard pressure. If the same pressure values are set on the altimeter on all aircraft, then, altitude readings on the instrument at an assigned point of airspace will be similar. Therefore, from a certain moment during the climb (transition level) to a certain moment during the descent (transition level), the aircraft height is calculated according to the standard pressure. The value of the standard pressure (QNE) is the same all over the world and amounts to 760 mmHg (1013.2 hectopascals). Thus, the flight on route is controlled by an altimeter, a barometric altimeter, which is comprised into the integrated flight and navigation system. An analysis of the instrument accuracy shows that when atmospheric pressure drops, altimeter readings may differ from true reading by ±100 m. It is known that a trailing vortex forms behind a flying plane. By the time, the trailing vortex descends and may be found at another flight level. May this cause air bump at the flight level? To answer this question, the A-380 aircraft was chosen as the object of research. This is one of the largest aircraft in the world. Therefore, the study of a trailing vortex behind the A-380 at the flight level, as the most dangerous in terms of the impact of its trailing vortex on other aircraft, will allow us to understand how safe and reasonable the accepted vertical and horizontal separation is. For the study, the special computational software system, based on the discrete vortex method, was used. This complex has passed the evaluation test and the state registration.

Vortex safety when flying at an assigned flight level

Air traffic intensity between countries and within individual countries is increasing year by year. As a rule, airways follow the same routes. As a result, so-called “roads in the sky” are formed. And where there are roads, there are bumps by the time, in the form of CAT, updraughts and downdraughts and increased turbulence. Horizontal and vertical separation plays an important role in ensuring flight safety on route. Currently, a variety of regulatory documents, defining safe separation at the flight level, has been adopted. Thus, provided there is turbulence in the vortex wake, longitudinal separation is based on the arrangement of aircraft types by three categories according to their maximum certified take-off weight. Since November 2011, the Western standard of vertical separation RVSM (Reduced Vertical Separation Minimum) has been introduced in Russia. Vertical separation is the distance between the vertical flight levels on route. Previously, this distance amounted to 600 m (2000 ft), but due to the increasing intensity of air traffic, it was decided to reduce the vertical separation to 300 m (1000 ft). Hence, at the most common flight level, the vertical separation is 300 m. The question arises if this separation ensures the safety of air transportation? The fact is that the altitude of the flight level does not necessarily coincide with the actual aircraft height. Aircraft altimeters are, inherently, calibrated barometers, that calculate the altitude by the difference in pressure on the ground and in the air. To calculate the height above ground, it would be necessary to constantly input atmospheric pressure data to altimeters at each waypoint and take into consideration the waypoint altitude above the sea level. Consequently, it is customary to use standard pressure. If the same pressure values are set on the altimeter on all aircraft, then, altitude readings on the instrument at an assigned point of airspace will be similar. Therefore, from a certain moment during the climb (transition level) to a certain moment during the descent (transition level), the aircraft height is calculated according to the standard pressure. The value of the standard pressure (QNE) is the same all over the world and amounts to 760 mmHg (1013.2 hectopascals). Thus, the flight on route is controlled by an altimeter, a barometric altimeter, which is comprised into the integrated flight and navigation system. An analysis of the instrument accuracy shows that when atmospheric pressure drops, altimeter readings may differ from true reading by ±100 m. It is known that a trailing vortex forms behind a flying plane. By the time, the trailing vortex descends and may be found at another flight level. May this cause air bump at the flight level? To answer this question, the A-380 aircraft was chosen as the object of research. This is one of the largest aircraft in the world. Therefore, the study of a trailing vortex behind the A-380 at the flight level, as the most dangerous in terms of the impact of its trailing vortex on other aircraft, will allow us to understand how safe and reasonable the accepted vertical and horizontal separation is. For the study, the special computational software system, based on the discrete vortex method, was used. This complex has passed the evaluation test and the state registration.

Accuracy Analysis of Complex Transmission System with Distributed Tooth Profile Errors

Tooth profile errors are the internal excitations that cause gear meshing errors, which are critical error factors affecting gear transmission accuracy. In existing studies, it is usually regarded as a constant or random distribution function. However, the actual machined tooth profile error is not a constant, so this estimation is inconsistent with the actual situation, resulting in an inaccurate evaluation of transmission accuracy. This paper proposes a method for representing tooth profile errors using distribution errors (including systematic and random errors), and a mathematical model of distributed tooth profile errors is presented. The contact stresses of the complex transmission system were compared with those obtained by formulas, proving that tooth profile errors increase contact stress. A method for calculating gear meshing error is proposed to evaluate the actual output accuracy of the complex transmission system. Compared with the test, the output accuracy is reduced by 13.8% under the temperature environment and distributed tooth profile errors. The proposed methods can accurately predict the transmission accuracy of precision transmission systems at the design stage and provide theoretical support for reducing systematic and random errors at the gear machining stage.

Analysis and comparison of the orbit determination accuracy of TianQin based on multiple ground-based measurements

TianQin project, a Chinese initiative in space gravitational wave detection, demands high precision in satellite orbit during both entry and scientific operations. As means of developmental maturation, ground-based measurements play a vital role in ensuring the smooth execution of TianQin satellite’s detection mission. This paper conducts a simulation analysis by utilizing various ground-based measurement data, including the China Deep Space Network (CDSN), S/Ka-band ranging system, and Satellite Laser Ranging (SLR). The main focus is to explore the distinctions in Precise Orbit Determination (POD) capabilities among different methods and to enhance POD accuracy through the integration of multiple techniques for TianQin satellites. The results indicate: (1) Leveraging a strategically positioned station distribution, CDSN stations offer extended observation time, averaging 17.3 h per satellite daily, compared to S/Ka’s 10.5 h. (2) In single-measurement POD scenarios, S/Ka proves superior, achieving accuracy better than 10 m and 0.4 mm s−1 for TianQin satellites with a 7-day orbit arc length. This superiority is attributed to its exceptional observational accuracy, outperforming CDSN’s 40 m and 2.2 mm s−1 for POD accuracy. (3) By integrating high-precision SLR data on the foundation of CDSN or S/Ka observations, the POD accuracy of TianQin satellites is further enhanced, despite the limited SLR data quantity.

Corpus-based Approaches for Sentiment Analysis: A Review

The investigation studied the state of art on corpus-based approaches for sentiment analysis. Thus, detailing its methodologies, evaluation metrics, limitations, and future directions. The importance of sentiment analysis in fields such as marketing, customer feedback analysis, social media monitoring, financial analysis, and political science is emphasized. The methodology for corpus-based approaches in sentiment analysis includes the following key steps: data collection, preprocessing, feature extraction, and sentiment classification. The lexicon-based approaches include the corpus-based or bag of words (BOW) and dictionary (also called opinion lexicon). Evaluation of the corpus-based sentiment analysis approach is addressed through performance metrics such as accuracy, precision, recall, F1-score, and comparative analysis with other approaches including hybrid and rule-based systems. Limitations of corpus-based sentiment analysis, such as data sparsity and domain adaptation, are acknowledged, alongside potential enhancements and research directions including ensemble learning, deep learning architectures, and multimodal data integration. The conclusion emphasizes the versatility and scalability of corpus-based sentiment analysis, while ongoing research efforts aim to address its limitations and further enhance its applicability in diverse domains.

Unveiling suspicious phishing attacks: enhancing detection with an optimal feature vectorization algorithm and supervised machine learning

IntroductionThe dynamic and sophisticated nature of phishing attacks, coupled with the relatively weak anti-phishing tools, has made phishing detection a pressing challenge. In light of this, new gaps have emerged in phishing detection, including the challenges and pitfalls of existing phishing detection techniques. To bridge these gaps, this study aims to develop a more robust, effective, sophisticated, and reliable solution for phishing detection through the optimal feature vectorization algorithm (OFVA) and supervised machine learning (SML) classifiers.MethodsInitially, the OFVA was utilized to extract the 41 optimal intra-URL features from a novel large dataset comprising 2,74,446 raw URLs (134,500 phishing and 139,946 legitimate URLs). Subsequently, data cleansing, curation, and dimensionality reduction were performed to remove outliers, handle missing values, and exclude less predictive features. To identify the optimal model, the study evaluated and compared 15 SML algorithms arising from different machine learning (ML) families, including Bayesian, nearest-neighbors, decision trees, neural networks, quadratic discriminant analysis, logistic regression, bagging, boosting, random forests, and ensembles. The evaluation was performed based on various metrics such as confusion matrix, accuracy, precision, recall, F-1 score, ROC curve, and precision-recall curve analysis. Furthermore, hyperparameter tuning (using Grid-search) and k-fold cross-validation were performed to optimize the detection accuracy.Results and discussionThe findings indicate that random forests (RF) outperformed the other classifiers, achieving a greater accuracy rate of 97.52%, followed by 97.50% precision, and an AUC value of 97%. Finally, a more robust and lightweight anti-phishing model was introduced, which can serve as an effective tool for security experts, practitioners, and policymakers to combat phishing attacks.

Accuracy analysis of UAV aerial photogrammetry based on RTK mode, flight altitude, and number of GCPs

Abstract The optimization of an unmanned aerial vehicle (UAV) aerial photogrammetry scheme is crucial for achieving higher precision mapping results. Three representative factors, namely the real-time kinematic (RTK) mode, flight altitude, and the number of ground control points (GCPs) were selected to analyze their impact on UAV aerial photogrammetry accuracy. Four flight altitude tests were conducted separately in two RTK modes, and five GCP layout schemes were designed. Based on this, the root mean square error (RMSE) values of 40 aerial photogrammetric results were analyzed. The results showed a significant correlation between flight altitude and resolution of the UAV aerial photogrammetric results. Further, conversion formulas between actual image resolution and flight altitude for different GCP values were also derived in RTK and non-RTK modes. In the case of precise positioning, the horizontal and vertical accuracy of the aerial photogrammetric image decreased with increasing flight altitude. Under the same flight altitude, the addition or no addition of GCPs, including changes in GCP numbers, had no significant effect on improving the accuracy of aerial photogrammetry in RTK mode. However, in non-RTK mode, the number of GCPs significantly affected accuracy. The horizontal and vertical RMSE values decreased rapidly with the increase in GCP numbers and then stabilized. However, regardless of whether RTK was activated, an excessive number of GCPs was not conducive to improving the accuracy of aerial photogrammetric results. The mapping accuracy of UAVs in RTK mode without GCPs was equivalent to that in non-RTK mode with GCPs. Therefore, when using RTK-UAVs, deploying GCPs is unnecessary under suitable circumstances. Finally, practical suggestions for optimizing the UAV aerial photogrammetry scheme are provided as a reference for related applications.

A New Type of Linear Resolver Proposal and Its Accuracy Analysis Under Normal State and Air-Gap Asymmetry Fault

A New Type of Linear Resolver Proposal and Its Accuracy Analysis Under Normal State and Air-Gap Asymmetry Fault

Segmentation Method of Concrete Small Cracks Based on UAV Images

Introduction:: Cracks are one of the major problems in modern concrete buildings, especially in locations that are difficult to map manually, such as bridges and high-rise buildings. Accurate analysis of unmanned aerial vehicle (UAV) images has become the key to determining whether a building needs maintenance. Methods:: Traditional image processing methods are easily interfered by high-frequency background. Neural network methods need fine datasets, which increase labor costs. Therefore, this paper proposes a segmentation algorithm based on UNet3+ network. After obtaining the UAV image, the rough location of the crack can be obtained by only rough labeling. And then, the sample balance can be carried out by clipping the target area. The UNet3+ network is used to train the processed datasets and extract the region of interest to ignore the non-target texture. Finally, the region of interest is further segmented by color clustering and edge detection methods. Results:: The proposed method can detect the cracks accurately. In all test images, the relative errors are less than 13%. Especially in test images whose width is less than 0.2mm, the maximum absolute error is only 0.0237mm, which is completely acceptable in actual production. The proposed method has higher practicability in the detection of concrete crack images taken by UAV. The results show that the proposed method outperforms the cutting-edge method published in the journal "Sensor", when the background is complex. Conclusion:: The proposed method can segment and detect cracks effectively, which can remove the high-frequency interference region from the images.

Depth Accuracy Analysis of the ZED 2i Stereo Camera in an indoor environment

Accurate depth information is crucial for autonomous systems to navigate and interact safely with their surroundings. Passive stereo-vision cameras, such as the ZED 2i, obtain depth information through stereo-image analysis and triangulation. The study measures and assesses the true capabilities of the ZED 2i camera in a real indoor office environment. Furthermore, the study provides a standard test setup to reproduce similar benchmarks with different depth cameras. To achieve the set goals, an experiment was devised and carried out in an office environment to determine the camera depth error and Root Mean Square Error (RMSE) of the depth estimates at different distances using four different image resolutions. The results reveal that the depth error has heavy tails, implying that outliers substantially impact accuracy. Hence, depth errors should not be modeled as normally distributed errors. Moreover, only two out of four resolutions provided the capability of acquiring depth data up to 18 m. These insights provide guidelines for understanding the ZED 2i camera's true capabilities, determining its suitability for different applications and environments, and giving baselines for future tests of other competing sensor units. Furthermore, the study offers a simple, inexpensive, and laboratory space-free, yet effective setup that does not need extensive equipment or complex configurations to facilitate the benchmarking of depth cameras in different working environments.

A Comparative Analysis of Accuracy in Ability Estimation Methods in Multistage Adaptive Testing(MST)

A Comparative Analysis of Accuracy in Ability Estimation Methods in Multistage Adaptive Testing(MST)

Are analysts’ Forecasts Reliable? A Machine Learning-Based Analysis of the Target Price Accuracy

Are analysts’ Forecasts Reliable? A Machine Learning-Based Analysis of the Target Price Accuracy

Semi-Automatic Detection of Errors in Genome-Scale Metabolic Models.

Genome-Scale Metabolic Models (GSMMs) are used for numerous tasks requiring computational estimates of metabolic fluxes, from predicting novel drug targets to engineering microbes to produce valuable compounds. A key limiting step in most applications of GSMMs is ensuring their representation of the target organism's metabolism is complete and accurate. Identifying and visualizing errors in GSMMs is complicated by the fact that they contain thousands of densely interconnected reactions. Furthermore, many errors in GSMMs only become apparent when considering pathways of connected reactions collectively, as opposed to examining reactions individually. We present Metabolic Accuracy Check and Analysis Workflow (MACAW), a collection of algorithms for detecting errors in GSMMs. The relative frequencies of errors we detect in manually curated GSMMs appear to reflect the different approaches used to curate them. Changing the method used to automatically create a GSMM from a particular organism's genome can have a larger impact on the kinds of errors in the resulting GSMM than using the same method with a different organism's genome. Our algorithms are particularly capable of identifying errors that are only apparent at the pathway level, including loops, and nontrivial cases of dead ends. MACAW is capable of identifying inaccuracies of varying severity in a wide range of GSMMs. Correcting these errors can measurably improve the predictive capacity of a GSMM. The relative prevalence of each type of error we identify in a large collection of GSMMs could help shape future efforts for further automation of error correction and GSMM creation.