Distribution Of Values Research Articles

Maximum non-bounded difference method for overbounding Global Navigation Satellite System errors

AbstractGlobal Navigation Satellite Systems (GNSS) support numerous applications, including mission-critical ones that require high integrity for safe operations such as air, maritime, and land-based navigation. These applications require ensuring the upper bound of the positioning computation, including overbounding any assumptions made in positioning algorithms. One of the main assumptions in the GNSS positioning algorithm is that the error follows a Probability Density Function (PDF), widely assumed to be Gaussian. This assumption should be overbounded to ensure safety. Two mathematical definitions of distribution overbounding have been proposed: Cumulative Distribution Function (CDF) overbounding and paired overbounding. Achieving these definitions has been an open problem for the past 20 years, and to date, no methods have effectively achieved either definition in multimode datasets. This paper proposes the first overbounding method, the Maximum non-Bounded Difference (MnBD) method, capable of achieving both definitions. Its effectiveness is assessed by comparison with the combined CDF bounding and paired overbounding approach. The results show that the MnBD method successfully achieves overbounding in both real datasets and 1000 simulated multimode data, tested on Gaussian and Generalized Extreme Value (GEV) distributions. The latter demonstrates MnBD’s ability to handle asymmetric distributions. The developed approach can be utilized in overbounding the GNSS error at the system level and user level to ensure the system integrity.

Value distribution of meromorphic functions concerning differences

Value distribution of meromorphic functions concerning differences

Precision Detection of Infrared Small Target in Ground-to-Air Scene

Reliable infrared small target detection plays an important role in infrared search and track systems. In recent years, most target detection methods usually use the statistical features of a rectangular window to represent the contrast between the target and the background. When the size of the target is small or the target is close to the background, the statistical features of the rectangular window would reduce the significance of the target. Moreover, such methods have limited effect on interfering targets, high brightness background, background edges, and clutter suppression in complex backgrounds, and are likely to misdetect the target or even miss it. This paper proposes a non-window, structured algorithm for precision detection of infrared small targets under ground-to-air complex scenes. The non-window, structured local grayscale descent intensity and local gradient watershed (LGDI-LGW) filter can detect a 1 × 1 pixel infrared small target, and effectively suppress interfering targets and background edges. By using the adaptive threshold and centroid algorithm on the target area, the precision of target coordinates reaches sub-pixel accuracy. The results of 9 simulation experiments show that the algorithm has the lowest false alarm rate and the highest detection rate compared with the eight baseline algorithms. It can effectively detect targets with Gaussian distribution of grayscale values and targets with grayscale values approximating tree stump structure. The results of 2 engineering experiments show that under simulated near-sun conditions, a uniform target is precisely detected, and the UAV point target is precisely detected in complex ground-to-air scenes.

Two fitness inference schemes compared using allele frequencies from 1,068,391 sequences sampled in the UK during the COVID-19 pandemic.

Throughout the course of the SARS-CoV-2 pandemic, genetic variation has contributed to the spread and persistence of the virus. For example, various mutations have allowed SARS-CoV-2 to escape antibody neutralization or to bind more strongly to the receptors that it uses to enter human cells. Here, we compared two methods that estimate the fitness effects of viral mutations using the abundant sequence data gathered over the course of the pandemic. Both approaches are grounded in population genetics theory but with different assumptions. One approach, tQLE, features an epistatic fitness landscape and assumes that alleles are nearly in linkage equilibrium. Another approach, MPL, assumes a simple, additive fitness landscape, but allows for any level of correlation between alleles. We characterized differences in the distributions of fitness values inferred by each approach and in the ranks of fitness values that they assign to sequences across time. We find that in a large fraction of weeks the two methods are in good agreement as to their top-ranked sequences, \textit{i.e.} as to which sequences observed that week are most fit. We also find that agreement between the ranking of sequences varies with genetic unimodality in the population in a given week.&#xD.

Data and contemporary economics: role, paradox and development

PurposeA more accurate comprehension of data elements and the exploration of new laws governing contemporary data in both theoretical and practical domains constitute a significant research topic.Design/methodology/approachBased on the perspective of evolutionary economics, this paper re-examines economic history and existing literature to study the following: changes in the “connotation of production factors” in economics caused by the evolution of production factors; the economic paradoxes formed by data in the context of social production processes and business models, which traditional theoretical frameworks fail to solve; the disruptive innovation of classical theory of value by multiple theories of value determination and the conflicts between the data market monopoly as well as the resulting distribution of value and the real economic society. The research indicates that contemporary advancements in data have catalyzed transformative innovation within the field of economics.FindingsThe research indicates that contemporary advancements in data have catalyzed disruptive innovation in the field of economics.Originality/valueThis paper, grounded in academic research, identifies four novel issues arising from contemporary data that cannot be adequately addressed within the confines of the classical economic theoretical framework.

Application of the Gait Kinematics Index in Patients with Cerebral Palsy

Due to the complexity of the medical issues connected with cerebral palsy (CP), the classification of gait pathologies seems rather difficult. The aim of this study was to asses the usefulness of the Gait Kinematics Index (GKI) from a clinical point of view in the population of patients with CP. The assessment of the possibilities of using the GKI in a group of patients with CP was conducted on the basis of the correlation of its results with the Gillette Gait Index (GGI) and Gait Deviation Index (GDI) values. The distribution of the index values was also evaluated with attention paid to the CP types and treatment methods. Analyses were performed on the basis of the gait test results in a group of 56 healthy children and 72 patients with CP. The GKI values for patients with CP were 1.55 ± 0.66, as opposed to 0.77 ± 0.17 for the reference group. A strong linear correlation was found between the values of the GKI and GGI (r = 0.8 ÷ 0.85), as well as between the GKI and GDI (r = −0.89 ÷ 0.9), obtained in children with CP. In addition, significant differences were found between the results obtained in all the groups of children with CP divided by treatment method (rehabilitation, botulinum, rhizotomy, p < 0.05), whereas in the groups of children divided by CP type, significant differences (p < 0.05) were found solely between diplegia and hemiplegia and between hemiplegia and quadriplegia. The results obtained were the same in the case of the GKI, GGI and GDI. To conclude, the results presented in this work confirm the clinical utility of the GKI in the population of patients with CP.

Constant Values Distribution Investigation in the C Programs Source Code

Currently, software engineering plays a key role in software development, one of the criteria for the development of which is the investigation of its factology and various scientific and practical patterns. An important aspect of this area is the logic of program execution, operating with internal data, and, in particular, constant values, the identification of patterns in which actualizes this research. The main applications of this pattern include obtaining fundamental knowledge about algorithms, creating new and expanding existing metrics for evaluating and comparing program code, developing methods for its optimization, using it in genetic programming, etc.The purpose of this article is to obtain the frequency distribution of constant values in the source code of programs in the C programming language.The essence of the presented approach is to create a method for statistical analysis of the text of the source codes of programs contained in the ExeBench dataset (which consists of a huge amount of source code of functions in the C programming language, their assembler code for various processor architectures, compilation errors and other information).The proposed method is based on the use of algorithms for lexical and syntactic analysis of source code functions, semantic definition of constant types, and conversion of the recording of programming language symbols into the corresponding numeric or string values.The method has an implementation in the form of a software tool in the Python programming language, given in the form of an intuitive pseudocode. Experiments using this prototype allowed us to obtain the desired distribution of constant values for the source code of programs in the C programming language. Analysis of the obtained results allowed us to make a number of important theoretical and practical conclusions regarding the most frequently used constants, the correspondence of the obtained distribution to the Zipf law and its proximity to the exponential function, the anomalous appearance of a number of constants in the Top 50, etc.The scientific novelty of the proposed approach lies in the fact that the distribution of constant values for the source code of programs in the C programming language is obtained for the first time.The theoretical significance consists in obtaining new fundamental knowledge regarding the features and patterns of source code constructions, which can be extended to other programming languages.The practical significance consists in applying the distribution to a wide range of tasks, including the author's genetic reverse engineering, which in itself is a qualitatively new direction.

Analysis of the air gap magnetic field in cylindrical magnetic couplings based on mathematical and finite element approach

The air gap magnetic field of a magnetic coupling plays a crucial role in the examination of its static torque and eddy current losses. Precise characterization of the air gap magnetic field is essential for ensuring the accuracy of performance assessments of the magnetic coupling. This study focuses on the cylindrical magnetic coupling as the subject of investigation, employing mathematical analysis and finite element methods to evaluate and quantify the magnetic field properties. The study establishes a mathematical model of the magnetic field of a magnetic coupling based on electromagnetic field principles and the superposition theorem. An analytical formula for the magnetic flux density distribution of the air-gap magnetic field is derived. Subsequently, a three-dimensional magnetic field finite element model is created using the finite element method to numerically calculate the air gap magnetic field and validate the analytical formula. The research also explores the periodic distribution characteristics of the magnetic field in the air gap, analyzing axial differences in magnetic flux density value and direction distribution influenced by end effects.

Colours in clothes and psychological functioning: the impact on emotions and self-esteem

Background: Colours are the object of study in various areas of humanitarian sciences, such as linguistics, cultural studies, and art. In psychology, the effects of colour on behaviour and emotional states are analyzed. The purpose of this research is to explore the preference for colours in clothes Indian students wear and report the results of the “Colour Challenge Week” project. Methods: Simultaneous mixed methods were used in this study: qualitative (a survey, documents analysis: students’ reports) and quantitative (two standardized tests: The Rosenberg Self-Esteem Scale and the PANAS-SF). Results: The article introduces the survey results among 246 university students aged 18-22 from different states of India. It shows the colours black, white, and blue in clothes are of preference. Pearson’s Chi-Square test was used to examine the Frequency distribution of values. To find the answer to the research question: “What is the impact of colours in clothes on students’ emotions and self-esteem?” a thematic analysis of students’ reports was performed. Conclusions: The exploration of this topic is far from complete and to uncover all relationships further intensive research is required. Future studies are recommended utilizing bigger sample sizes with a more equal proportion of males to females

Modeling of Reversed Fréchet Distribution under Linear Normalizing

Extreme value theory (EVT) is becoming increasingly important as a suitable model of extreme events to represent phenomena in various fields of applications where extreme values may appear and have detrimental effects. In EVT, the Reversed Fréchet (RF) distribution is a special case of the Reversed generalized extreme value distribution (RGEVD) for modeling extreme data under linear normalizing. The purpose of this paper is divided into four objectives: Firstly, we discuss the practical aspects of the two methods that belong to the domain attraction of RF distribution. The second objective is to use RF distribution to model the behavior of two extreme air pollutants. The third objective is based on a diagnostics plot and hypothesis testing used to evaluate an appropriate shape of the tail distribution. Lastly, for the fourth objective, we attempt to obtain the return level period (RLP) that is expected to be exceeded. Maximum likelihood estimation (MLE) is derived and used to estimate the parameters of RF distribution. The results show that applications are significant for modeling by RF distribution in all cases of two air pollutants. Software that was used in all computations and graphics is the R statistical program with packages fExtreme and ismev.

Prediction and assessment of meteorological drought in southwest China using long short-term memory model

Abstract Drought prediction is crucial for mitigating risks and designing measures to alleviate its impact. Machine learning models have been widely applied in the field of drought prediction in recent years. This study concentrated on predicting meteorological droughts in southwest China, a region prone to frequent and severe droughts, particularly in areas with sparse meteorological station coverage. The long short-term memory (LSTM) predictive model, which is a deep learning model, was constructed by calculating standardized precipitation evapotranspiration index (SPEI) values based on 144 weather station observations from 1980 to 2020. The 5-fold cross-validation method was used for the hyperparameter optimization of the model. The LSTM model underwent comprehensive assessment and validation through multiple methods. This included the use of several accuracy assessment indicators and a comparison of results. The comparison covered different drought characteristics among the LSTM predictive model, the benchmark random forest (RF) predictive model, the historical drought situations, and the calculated SPEI values based on observations from 144 weather stations. The results showed that the training results of the LSTM predictive model basically agreed with the SPEI values calculated from weather station observations. The model-predicted variation trend of SPEI values for 2020 was similar to the variation in SPEI values calculated based on weather station observations. On the test set, the coefficient of determination (R 2), the root mean square error, the explained variance score, the Nash–Sutcliffe efficiency, and the Kling–Gupta efficiency were 0.757, 0.210, 0.802, 0.761, and 0.212, respectively. The total consistency rate of the drought grade was 59.26%. The spatial correlation distribution of SPEI values between LSTM model prediction and calculation from meteorological stations in 2020 was more than 0.5 for most regions. The correlation coefficients exceeded 0.6 in western Tibet and Chengdu Plains. Compared to the RF model, the LSTM model excelled in all five performance evaluation metrics and demonstrated a higher overall consistency rate for drought categories. The Kruskal–Wallis test for both the LSTM and RF models all indicated no significant difference in the distributions between the predicted and observed data. Scatter plots revealed that the prediction accuracy for both models in 2020 was suboptimal, with the SPEI showing a comparatively narrow range of values. Nonetheless, the LSTM model significantly outperformed the RF model in terms of prediction accuracy. In summary, the LSTM model demonstrated good overall performance, accuracy, and applicability. It has the potential to enhance dynamic drought prediction in regions with complex terrain, diverse climatic factors, and sparse weather station networks.

METHODS FOR ANALYZING THE EFFECTIVENESS OF INFORMATION SYSTEMS FOR INVENTORY MANAGEMENT

Context. Information systems for inventory management are used to forecast, manage, coordinate, and monitor the resources needed to move goods smoothly, in a timely, cost-effective, and reliable manner. The more efficiently the system works, the better results a company can achieve. A common problem with existing performance measurement methods is the difficulty of interpreting the relationship between performance indicators and the factors that influence them. Objective. The purpose of the study is to describe a method for evaluating the effectiveness of information systems, which allows to establish a link between performance indicators and factors that influenced these indicators. Method. A set of indicators characterizing the effective operation of inventory management information systems is proposed. The rules for quantifying the factors that influence the performance indicators are proposed. The factors arise during events that affect the change in order, delivery, balance, target inventory level, parameters of the forecasting algorithm, etc. The proposed method performs an iterative distribution of the quantitative value of factors among performance indicators and thus establishes the relationship between performance indicators and factors. Results. The implementation of the proposed method in the software was carried out and calculations were made on actual data. Conclusions. The calculations carried out on the basis of the method have demonstrated the dependence of performance indicators on factors. The use of the method allows identifying the reasons for the decrease in efficiency and making the company’s management more efficient. Prospect for further research may be to detail the factors, optimize software implementations, and use the method in inventory management information systems in various areas of activity.

Automatic Experimental Numerosity Generation and Numerical Training for Rodents.

Non-symbolic stimuli representing numerosities are invariably associated with continuous magnitudes, complicating the interpretation of experimental studies on numerosity perception. Although various algorithms for experimental numerosity generation have been proposed, they do not consider the quantifiable distribution of values of continuous magnitudes and the degree of numerosity-magnitudes association. Consequently, they cannot thoroughly exclude the possibility of magnitudes integration or strategy switch between different magnitudes in numerical stimulus perception. Here, we introduce a protocol for numerosity generation, animal training, and behavior outcomes analysis that takes the aforementioned issues into consideration. This protocol has been applied to rodents and is applicable to other animals in numerosity studies. © 2024 Wiley Periodicals LLC. Basic Protocol 1: Algorithm for generating non-symbolic numerical stimuli Alternate Protocol: General algorithm for generating non-symbolic numerical stimuli Basic Protocol 2: Numerical training and testing for rodents.

Topological embedding and directional feature importance in ensemble classifiers for multi-class classification

Cancer is the second leading cause of disease-related death worldwide, and machine learning-based identification of novel biomarkers is crucial for improving early detection and treatment of various cancers. A key challenge in applying machine learning to high-dimensional data is deriving important features in an interpretable manner to provide meaningful insights into the underlying biological mechanismsWe developed a class-based directional feature importance (CLIFI) metric for decision tree methods and demonstrated its use for the The Cancer Genome Atlas proteomics data. The CLIFI metric was incorporated into four algorithms, Random Forest (RF), LAtent VAriable Stochastic Ensemble of Trees (LAVASET), and Gradient Boosted Decision Trees (GBDTs), and a new extension incorporating the LAVA step into GBDTs (LAVABOOST). Both LAVA methods incorporate topological information from protein interactions into the decision function.The different models' performance in classifying 28 cancers resulted in F1-scores of 92.6% (RF), 92.0% (LAVASET), 89.3% (LAVABOOST) and 85.7% (GBDT), with no method outperforming all others for individual cancer type prediction. The CLIFI metric enables visualisation of the model's decision-making functions. The resulting CLIFI value distributions indicated heterogeneity in the expression of several proteins (MYH11, ERα, BCL2) across different cancer types (including brain glioma, breast, kidney, thyroid and prostate cancer) aligning with the original raw expression data.In conclusion, we have developed an integrated, directional feature importance metric for multi-class decision tree-based classification models that facilitates interpretable feature importance assessment. The CLIFI metric can be combined with incorporating topological information into the decision functions of models to introduce inductive bias, enhancing interpretability.

Comparison of Machine Learning Models and Feature Importance Investigation of Intelligent Fault Diagnosis Methods for Robots Based on Datasets Across Various Distributions

Intelligent fault detection is an important component of the industrial and automation fields. However, conventional research on intelligent fault detection mainly focuses on industrial production equipment, while there is little research on intelligent fault detection scenarios for robots. Based on a hexapod robot joints dataset, this paper investigates intelligent fault diagnosis tasks in the field of robotics. Firstly, the dataset is clustered into two new datasets with different distributions through k-means method, which is used to simulate the practicality of imbalanced distribution between healthy and fault data. Afterwards, several multioutput machine learning classification models were established to predict robot joints with faults. In two datasets with different distributions, the larger one is used as the training set and the smaller one is used as the testing set. The article compares the performance of these models with prediction accuracy as the main indicator. And based on the results, the paper selects the model with the highest accuracy for further exploration of feature importance. Finally, the article explains the significance of the results and analyzes possible reasons. The experimental results show that the random forest model better overcomes the problem of data distribution differences and has the highest accuracy. In the random forest model, the importance of position data representing position error is higher than that of slope with respect to the axis data representing angle error. This result may be related to the distribution of feature values and the fact that position data contains more crucial information.

Regional frequency analysis of extreme wind in Pakistan using robust estimation methods

The quantile estimates of extreme wind speed are needed for various areas of interest using regional frequency analysis (RFA) and extreme value theory. These calculations are crucial for the coding of wind speed. The data was taken from the NASA official website at a 10-meter distance and measured in meters per second (m/s). RFA of annual maximum wind speed (AMWS) using L-moments is performed utilizing annual maximum wind speed data from sixteen sites (16) in Pakistan’s Khyber Pakhtunkhwa province. There are no sites that are found to be discordant. The wards method is used to construct a homogenous region and make two homogenous regions from 16 sites. The heterogeneity test justifies that both clusters are homogeneous. The most appropriate probability distribution from the Generalized Normal (GNO), Generalized Logistic (GLO), Pearson Type-3 (P3), Generalized Pareto (GPA), and Generalized Extreme Value (GEV) distributions is chosen to calculate regional quantiles. According to the L-moments diagram and Z statistics, the GEV for Cluster- Ι and GLO for Cluster- ΙΙ are the best suggestions from the others. Both clusters’ robustness is measured utilizing relative bias (RB) and relative root mean square error (RRMSE). Overall, GEV distribution is fit for cluster-Ι, and the GLO distribution is fit for cluster-ΙΙ. Utilizing the site mean and median as index parameters, we can also find at-site quantiles from regional quantiles. The study’s quantile estimates can be employed in codified structural designs with policy consequences.

Variation Characteristics of Actual Evapotranspiration and Uncertainty Analysis of Its Response to Local Climate Change in Arid Inland Region of China

Exploring the variation characteristics of actual evapotranspiration (ETa) and its response to climate change in the arid inland region of China is of great significance for strengthening regional water resources management and maintaining ecological environment security and stability. Taking the Dulan River Basin as the research area, based on the meteorological data from the Wulan Station and hydrological data from the Shanggaba Station from 1981 to 2020, the variation characteristics of ETa at the annual scale were analyzed. The ETa estimation model and joint distribution model of P and potential evapotranspiration (ET0) was constructed based on climate factors, and the uncertainty of ETa response to climate change was explored with the water balance method, multiple linear regression, marginal distribution function, Copula function, and Monte Carlo algorithm. The results showed that the multi-year mean value of ETa in the study area was 261.6 mm, and the interannual process showed an insignificant upward trend, and had no abrupt change during the period. There were two obvious main cycles, which were 19-year periodic changes on the 30-year time scale and 6-year periodic changes on the 9-year time scale. The ETa estimation model based on precipitation (P) and ET0 had good simulation accuracy. The optimal marginal distributions of P and ET0 were Pearson-III (P-III) distribution and Generalized Extreme Value (GEV) distribution, respectively. The Copula joint distribution probability density of P and ET0 was a symmetric saddle-shaped distribution. ETa showed an inverted ‘S’ distribution with the change in joint guarantee rate of P and ET0, ranging from 116.9 mm to 498.6 mm. ETa was an interval range under a certain joint guarantee rate. The research results can provide support for the assessment of ETa, and help to further understand the driving mechanism of climate change on ETa in the arid inland region of China.

Quantitative myocardial blood flow distribution derived from CCTA and determination of myocardium at risk in the FAST TRACK CABG trial

Abstract Background In the anatomic SYNTAX score derived from invasive coronary angiography, the ischemic impact of each stenotic coronary segment is weighed with a fixed score based on the severity of luminal diameter narrowing and the theoretical amount of blood flow supplied to the myocardium(1). This method neither accounts for individual variation nor reflects the true extent of myocardium ischemia. Purpose We aim to provide a quantitative CT SYNTAX score derived from coronary computed tomography angiography (CCTA) with customized percent blood flow distribution to the myocardium supplied by each stenotic coronary segment to determine the myocardium at risk of ischemia. Methods Patients with 3VD and/or left main coronary artery disease (CAD) were included in the first-in-human FAST TRACK CABG trial and received surgical revascularization guided solely by CCTA and fractional flow reserve derived from CCTA (FFRCT). The recently reported anatomic CT-SYNTAX score was calculated for all cases. The FFRCT value and percent myocardial blood flow distribution(%MBF) were computed for all 16 SYNTAX score segments using the validated method of Keulards et al(2). On the pre-CABG CCTA, the segments located distal to the site where the FFRCT value dropped below 0.80 were considered at risk of ischemia, and the individual weight point per segment was calculated as 6×%MBF for each segment. The SYNTAX score derived from %MBF is compared to the anatomic and functional CT-SYNTAX score. Results Out of 114 patients enrolled, FFRCT and %MBF were analyzable in 106 patients. After excluding vessels with total occlusion, the coronary flow distribution between the right and left coronary systems was 20.5% vs 79.5%, respectively, similar to the 1:5 ratio in the anatomic SYNTAX score. The anatomic and functional CT-SYNTAX scores were 43.6 and 41.1, respectively. The average total %MBF per patient was 72.0(19.1)%, and the average SYNTAX score derived from %MBF was 38.0. There is a strong correlation between functional SYNTAX score and SYNTAX score derived from %MBF (Peason’s R=0.93). Passing-Bablock regression trends showed that the functional SYNTAX score slightly overestimated the SYNTAX score derived from %MBF (Figure 1). The coronary segment weight varied significantly per individual, especially in the presence of total occlusion. Overall, the fixed weight of the anatomic SYNTAX score is a good surrogate for the individualized weighing based on %MBF (Figure 2). Conclusion CCTA-derived %MBF enabled clinicians to quantify the individualized myocardium at risk of ischemia or injury in patients with severe CAD. Retrospectively, the fixed weight score used in the anatomical SYNTAX score appeared to be a robust surrogate for myocardial blood flow distribution.Figure 1Figure 2

Assessment of surgical revascularization completeness with quantitative myocardial blood flow distribution derived from CCTA in severe coronary artery disease

Abstract Background Complete revascularization remained the "holy grail" in the treatment of patients with severe coronary artery disease (CAD). However, there is no universally accepted definition of complete revascularization after coronary bypass graft surgery (CABG). The current assessment methods are visual, categorical, and biased by the operator's intention. Moreover, no method to date provides an individualized quantification of residual ischemia burden. Purpose We aim to provide an individualized method to assess the completeness of surgical revascularization through quantitative myocardial blood flow distribution derived from coronary computed tomography angiography (CCTA) that could reflect the actual functional ischemia burden after CABG. Methods Patients with 3VD and/or left main CAD were enrolled in the first-in-human FAST TRACK CABG trial for surgical revascularization guided solely by CCTA and fractional flow reserve derived from CCTA (FFRCT). Following CABG, the study protocol mandated a 30-day follow-up CCTA, which provides graft patency and topographical adequacy of the bypass graft anastomoses. The pre- and post-CABG CCTA were analyzed. The FFRCT value and percent myocardial blood flow distribution (%MBF) were computed for all 16 SYNTAX score segments using the validated method of Keulards et al(1). On pre-CABG CCTA, the myocardium located distal to the site where the FFRCT value dropped below 0.80 on the vessel centerline was considered ischemic, and the %MBF of the subtended myocardium was summed into the total percent ischemic myocardium (Figure 1). Following CABG, the segment was considered adequately revascularized when a non-narrowed graft was anastomosed distally to the site of FFRCT≤0.8 on the pre-operative CCTA. The change in percent ischemic myocardium and the residual ischemic burden were calculated for each patient (Figure 2). Results CCTA, FFRCT, and %MBF were obtained pre- and post-CABG in 96 patients, and the percent ischemic myocardium was computed per patient. At baseline, the average percent ischemic myocardium was 72.0(19.1)%. Post-CABG, the residual percent ischemic myocardium was 13.6(15.1)%. Residual percent ischemic myocardium <10% was achieved in 44 patients (44.8%). The first diagonal branch(n=13), distal LCX(n=12), and the first obtuse marginal branch(n=10) were the most frequent segments not revascularized, which subtended an average MBF of 12.2(3.5)%, 16.7(8.4)%, and 11.3(7.3)%, respectively. The main reason for inadequate revascularization was surgical deferral, with graft occlusion accounting for only 13.6% of the residual ischemic segments. Conclusion Percent ischemic myocardium and the completeness of surgical revascularization can be assessed with CCTA-derived %MBF. This novel method allows clinicians to assess the individualized ischemia burden and the completeness of revascularization that could be incorporated into personalized CABG planning.Figure 1Figure 2

Modeling of Some Egg Characteristics in Henna Partridges

In this study, the distributions of egg count (daily and cumulative), width, length and weight values of chukar partridges obtained in two different egg production seasons were statistically modeled. For this purpose, in modeling cumulative egg production curves; Logistic, Gompertz, Gamma, Schunute, Brody, Richard, Negative Exponential, von Bertalanffy, Cubic Piecewise and Cubic models were used, and in evaluating daily egg production curves; Gompertz, Logistic, Richard, McNally, Gamma, Cubic Piecewise, Quadratic, Quadratic Piecewise and Modified Compartmental models were used. In modeling egg width values; Gompertz, Gamma, Cubic Piecewise and Cubic models were used, in modeling length values; Logistic, Gamma, Cubic Piecewise and Cubic models were used, and in modeling weight values; McNally, Gamma, Cubic Piecewise and Cubic models were used. In all modeling studies, as comparison and evaluation criteria; Error Mean Square, Corrected Coefficient of Determination, Accuracy Factor, Deviation Factor, Durbin-Watson, Akaike Information Criterion, Corrected Akaike Information Criterion and Bayesian Information Criterion were taken into consideration. As a result of the study; Gamma was determined as the most suitable model for modeling cumulative egg yields and length values, McNally for modeling daily egg yields and weight values, and Gompertz and Gamma models for modeling egg width values.