Microwave-assisted extraction (MAE), ultrasound-assisted extraction (UAE) and conventional extraction of vanillin and its quantification by HPLC in pods of Vanilla planifolia is described. A range of nonpolar to polar solvents were used for the extraction of vanillin employing MAE, UAE and conventional methods. Various extraction parameters such as nature of the solvent, solvent volume, time of irradiation, microwave and ultrasound energy inputs were optimized. HPLC was performed on RP ODS column (4.6 mm ID x 250 mm, 5 microm, Waters), a photodiode array detector (Waters 2996) using gradient solvent system of ACN and ortho-phosphoric acid in water (0.001:99.999 v/v) at 25 degrees C. Regression equation revealed a linear relationship (r2 > 0.9998) between the mass of vanillin injected and the peak areas. The detection limit (S/N = 3) and limit of quantification (S/N = 10) were 0.65 and 1.2 microg/g, respectively. Recovery was achieved in the range 98.5-99.6% for vanillin. Maximum yield of vanilla extract (29.81, 29.068 and 14.31% by conventional extraction, MAE and UAE, respectively) was found in a mixture of ethanol/water (40:60 v/v). Dehydrated ethanolic extract showed the highest amount of vanillin (1.8, 1.25 and 0.99% by MAE, conventional extraction and UAE, respectively).
Worldwide, Air Navigation Service Providers (ANSP) are striving to exceed the desired safety levels. The Terminal Manoeuvre Area (TMA) is one of the most safetycritical areas in ATM as it encompasses the most critical phase of flight, i.e., departure and landing. An aircraft, during the final approach phase, is required to remain in a stable configuration and prevent any undesired state such as an unstable approach, which may subsequently lead to incidents/accidents such as Go-Around, Runway Excursions, etc. In this paper, we propose a data-driven framework to model the aircraft 4D trajectories in the final approach phase by adopting sparse variational Gaussian process (SVGP) model. The model is trained to learn the aircraft landing dynamics from Advanced Surface Movement Guidance and Control System (A-SMGCS) data, during the final approach phase. We experimentally demonstrate that SVGP provides an interpretable probabilistic bound of aircraft parameters that can quantify deviation and perform real-time anomaly detection. The findings of this work can increase situational awareness of the air traffic controller and has implications for the design of a new approach procedure in complex runway configurations such as parallel approach.
Approach and landing accidents have resulted in a significant number of hull losses worldwide. Technologies (e.g., instrument landing system) and procedures (e.g., stabilized approach criteria) have been developed to reduce the risks. In this paper, we propose a data-driven method to learn and interpret flight's approach and landing parameters to facilitate comprehensible and actionable insights into flight dynamics. Specifically, we develop two variants of tunnel Gaussian process (TGP) models to elucidate aircraft's approach and landing dynamics using advanced surface movement guidance and control system (A-SMGCS) data, which then indicates the stability of flight. TGP hybridizes the strengths of sparse variational Gaussian process and polar Gaussian process to learn from a large amount of data in cylindrical coordinates. We examine TGP qualitatively and quantitatively by synthesizing three complex trajectory datasets and compared TGP against existing methods on trajectory learning. Empirically, TGP demonstrates superior modeling performance. When applied to operational A-SMGCS data, TGP provides the generative probabilistic description of landing dynamics and interpretable tunnel views of approach and landing parameters. These probabilistic tunnel models can facilitate the analysis of procedure adherence and augment existing aircrew and air traffic controllers' displays during the approach and landing procedures, enabling necessary corrective actions. Nomenclature 𝐺𝑃 = Gaussian process models 𝐾 = kernel function defined by 𝐺𝑃
Background: Mental health is a big problem throughout the world, and India is not far behind. When we look at progress in the field of mental health, it appears to be sluggish. Despite the fact that a newly created mental health literacy (MHL) scale revealed substantial score disparities between the general public and mental health professionals, there is currently no published scale to measure MHL among healthcare students. Aims and Objectives: The major part was comparing the knowledge, attitude and perception of 1st year medical students with final year medical students regarding psychiatric disorders and measuring there response on Likert scale. Materials and Methods: The participants were recruited from 1st year to final year undergraduate students, during the period April 2019 to January 2020 in Patna Medical College and Hospital, Patna. The sample consisted of 100 students (50 from 1st year MBBS Students and 50 from final year MBBS (Students) Non-random, non-stratified, and purposive sampling was done for the purpose of the study. Results: Among the groups, majority of the of the final year students (64%) agreed that the best described condition of the patient was Generalized Anxiety Disorder, but only 26% of the 1st years students agreed that the best described condition of the patient was Generalized Anxiety Disorder. There was significant difference between 1st years and final year students about the knowledge of the described condition with P<0.001. Conclusion: Result showed that the final year students had more knowledge about the cases with regard to correct diagnosis, usefulness of various treatments and interventions and best the source of help. The 1st and final yearstudent had no differences in the attitude and perception regarding various cases given in the vignettes. Stigma based attitude was almost equally common among both 1st year and final year students.
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