The simulation of flight trajectory based on quasi-Newton and mesh simplification

2019 ◽  
Vol 33 (26) ◽  
pp. 1950311
Author(s):  
Yangyong Guo ◽  
Xiaoling Gao ◽  
Juan Wei ◽  
Jierui Wang ◽  
Mei Li ◽  
...  
Keyword(s):  
Prediction Accuracy ◽  
Simulation Experiment ◽  
Prediction Method ◽  
Mesh Simplification ◽  
Flight Trajectory ◽  
Key Factors ◽  
Aircraft Flight ◽  
Factors Influencing ◽  
Calculation Process ◽  
Quasi Newton

To address the problem of low accuracy of aircraft flight trajectory, a new prediction method for flight trajectory is proposed based on the quasi-Newton and mesh simplification algorithm. This method firstly proposes the calculation method of 3D aircraft flight trajectory, and simplifies the calculation process with mesh simplification algorithm. At the same time, the prediction accuracy is further improved with the quasi-Newton algorithm. At last, through the simulation experiment, we have made a deep study of the key factors influencing the prediction method. The result has shown that the method has good adaptability.

scholarly journals A Prediction Method of Network Security Situation based on QPSO-SVM

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Network Security ◽  
Situation Awareness ◽  
Prediction Accuracy ◽  
Simulation Experiment ◽  
Prediction Method ◽  
Support Vector ◽  
Original Sequence ◽  
Vector Machines ◽  
The Face ◽  
Awareness System

In network security situation awareness system, situation prediction is the key point. The traditional intrusion detection method lacks scalability in the face of the changing network structure and lacks adaptability in the face of unknown attack types. In order to ensure and improve the accuracy of situation prediction, a QPSO-SVM prediction model is proposed by combining the optimization performance of quantum particle swarm optimization and the prediction accuracy of support vector machines. By adding the original sequence to the original sequence, this model weakens the irregular disturbance in the original sequence and enhances the regularity of the sequence. Compared with the traditional SVM and PSOSVM, the superiority of the prediction precision is better, the prediction accuracy can be ensured, and the validity of the model is tested by the simulation experiment.

Consumers‟ Acceptance of „Halal‟ Credit Card Services: An Empirical Analysis

2015 ◽  
Vol 3 (1) ◽  
pp. 51 ◽  
Author(s):  
Zaimy Johana Johan ◽  
Lennora Putit
Keyword(s):  
Perceived Control ◽  
Credit Card ◽  
Credit Cards ◽  
Product Knowledge ◽  
Key Factors ◽  
Developing Nation ◽  
Business Transactions ◽  
Factors Influencing ◽  
Financial Credit ◽  
Survey Questionnaires

Many past researches have been carried out in an attempt to continuously understand individuals‟ consumption behaviour. This study was conducted to investigate key factors influencing consumers‟ potential acceptance of halal (or permissible) financial credit card services. Specifically, it anticipated the influence of attitude, social influences and perceived control on consumers‟ behavioural intention to accept such services. In addition, factors such as religiosity and product knowledge were also postulated to affect consumers‟ attitude towards the act of using halal credit cards for any retail or business transactions. Using non-probability sampling approach, a total of 500 survey questionnaires was distributed to targeted respondents in a developing nation but only 220 usable feedbacks were received for subsequent data analysis. Regression results revealed that religiosity and product knowledge significantly influence consumers‟ attitude toward using halal credit card services.  Attitude in turn, subsequently has a significant impact on consumers‟ intention to accept halal financial credit card services. Several theoretical and managerial contributions were observed in this study.   

scholarly journals Optimal breeding-value prediction using a Sparse Selection Index

Genetics ◽  
2021 ◽  
Author(s):  
Marco Lopez-Cruz ◽  
Gustavo de los Campos
Keyword(s):  
Sample Size ◽  
Dna Sequences ◽  
Genomic Prediction ◽  
Prediction Accuracy ◽  
Regularization Parameter ◽  
Selection Index ◽  
Prediction Method ◽  
Training Data ◽  
Breeding Value ◽  
Data Set

Abstract Genomic prediction uses DNA sequences and phenotypes to predict genetic values. In homogeneous populations, theory indicates that the accuracy of genomic prediction increases with sample size. However, differences in allele frequencies and in linkage disequilibrium patterns can lead to heterogeneity in SNP effects. In this context, calibrating genomic predictions using a large, potentially heterogeneous, training data set may not lead to optimal prediction accuracy. Some studies tried to address this sample size/homogeneity trade-off using training set optimization algorithms; however, this approach assumes that a single training data set is optimum for all individuals in the prediction set. Here, we propose an approach that identifies, for each individual in the prediction set, a subset from the training data (i.e., a set of support points) from which predictions are derived. The methodology that we propose is a Sparse Selection Index (SSI) that integrates Selection Index methodology with sparsity-inducing techniques commonly used for high-dimensional regression. The sparsity of the resulting index is controlled by a regularization parameter (λ); the G-BLUP (the prediction method most commonly used in plant and animal breeding) appears as a special case which happens when λ = 0. In this study, we present the methodology and demonstrate (using two wheat data sets with phenotypes collected in ten different environments) that the SSI can achieve significant (anywhere between 5-10%) gains in prediction accuracy relative to the G-BLUP.

scholarly journals Perspectives of Healthcare Professionals Regarding Factors Associated with Antimicrobial Resistance (AMR) and Their Consequences: A Cross Sectional Study in Eastern Province of Saudi Arabia

Antibiotics ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 878
Author(s):  
Mohamed A. Baraka ◽  
Amany Alboghdadly ◽  
Samar Alshawwa ◽  
Asim Ahmed Elnour ◽  
Hassan Alsultan ◽  
...  
Keyword(s):  
Antimicrobial Resistance ◽  
Inappropriate Prescribing ◽  
Healthcare Providers ◽  
Cross Sectional Study ◽  
Sectional Study ◽  
Key Factors ◽  
Eastern Province ◽  
Cross Sectional ◽  
Factors Associated ◽  
Factors Influencing

Factors reported in the literature associated with inappropriate prescribing of antimicrobials include physicians with less experience, uncertain diagnosis, and patient caregiver influences on physicians’ decisions. Monitoring antimicrobial resistance is critical for identifying emerging resistance patterns, developing, and assessing the effectiveness of mitigation strategies. Improvement in prescribing antimicrobials would minimize the risk of resistance and, consequently, improve patients’ clinical and health outcomes. The purpose of the study is to delineate factors associated with antimicrobial resistance, describe the factors influencing prescriber’s choice during prescribing of antimicrobial, and examine factors related to consequences of inappropriate prescribing of antimicrobial. A cross-sectional study was conducted among healthcare providers (190) in six tertiary hospitals in the Eastern province of Saudi Arabia. The research panel has developed, validated, and piloted survey specific with closed-ended questions. A value of p < 0.05 was considered to be statistically significant. All data analysis was performed using the Statistical Package for Social Sciences (IBM SPSS version 23.0). 72.7% of the respondents have agreed that poor skills and knowledge are key factors that contribute to the inappropriate prescribing of antimicrobials. All of the respondents acknowledged effectiveness, previous experience with the antimicrobial, and reading scientific materials (such as books, articles, and the internet) as being key factors influencing physicians’ choice during antimicrobial prescribing. The current study has identified comprehensive education and training needs for healthcare providers about antimicrobial resistance. Using antimicrobials unnecessarily, insufficient duration of antimicrobial use, and using broad spectrum antimicrobials were reported to be common practices. Furthermore, poor skills and knowledge were a key factor that contributed to the inappropriate use and overuse of antimicrobials, and the use of antimicrobials without a physician’s prescription (i.e., self-medication) represent key factors which contribute to AMR from participants’ perspectives. Furthermore, internal policy and guidelines are needed to ensure that the antimicrobials are prescribed in accordance with standard protocols and clinical guidelines.

Small Bowel Obstruction

2010 ◽  
Vol 21 (2) ◽  
pp. 187-194
Author(s):  
Colleen Trevino
Keyword(s):  
Small Bowel ◽  
Physical Examination ◽  
Surgical Intervention ◽  
Signs And Symptoms ◽  
Bowel Ischemia ◽  
Key Factors ◽  
The Past ◽  
Factors Influencing ◽  
Imaging Response

Strategies for the management of small bowel obstructions have changed significantly over the years. Nonoperative medical management has become the mainstay of treatment of many small bowel obstructions. However, the key to the management of small bowel obstructions is identifying those patients who need surgical intervention. Identification of those at risk for bowel ischemia and bowel death is an art as much as it is a science. Using the current literature and the past knowledge regarding small bowel obstructions, the clinician must carefully identify the signs and symptoms that suggest the need for operative intervention. Classification of the obstruction, history and physical examination, imaging, response to decompression and resuscitation, and resolution or progression of symptoms are the key factors influencing the management of small bowel obstructions.

Study on How to Identify the Key Factors Influencing Driving Behavior

ICCTP 2011 ◽  
2011 ◽  
Author(s):  
Ping-fan Li ◽  
Dian-hai Wang ◽  
Feng Sun ◽  
Chang-jun Wang
Keyword(s):  
Driving Behavior ◽  
Key Factors ◽  
Factors Influencing
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