Simulation And Analysis Of Indian Air Traffic

Air Traffic Density In India And The World At Large Is Growing Fast . The Airspace And Air Traffic Management Therefore Need Augmentation Of System Capability Without Compromising Safety . This Report Addresses The Judicious Use Of Simulation Facilities To Predict Present And Futuristic Problems. It Is Intended To Help Service Providers Plan And Come Out With Strategies For Air Traffic Management. "State Of The Art Simulation Facilities Are Also Useful In Training A Team Of Reliable, Safe And Efficient Air Traffic Controllers . The Report Presents Results Of Simulation Of Three International Airports : One Existing In Bangalore, The One Planned At Devanahalli And The One In Cochin . The Problems Addressed Are Those Of Delay Encountered At The Three Airports, Controller Workload And Noise Contours Around The Bangalore Aerodrome

Modelling,simulation, and analysis of HAL Bangalore13; international airport

Air traffic density in India and the world at large is growing fast and posing challenging13; problems. The problems encountered can be parameterized as flight delay, workload of air traffic13; controllers and noise levels in and around aerodromes. Prediction and quantification of these13; parameters aid in developing strategies for efficient air traffic management. In this study, the13; method used for quantifying is by simulation and analysis of the selected aerodrome and air13; space. This paper presents the results of simulation of HAL Bangalore International Airport,13; which is used by civil as well as military aircraft. With the test flying of unscheduled military13; aircraft and the increase in the civil air traffic, this airport is hitting the limit of acceptable delay.13; The workload on air traffic controllers is pushed to high during peak times. The noise contour13; prediction, especially for the test flying military aircraft is sounding a wake up call to the13; communities living in the vicinity of the Airport.13

Ethnobotanical Study of Medicinal Plants used by the Local People in Vellore District, Tamilnadu, India

An ethnobotanical survey was conducted in and around Vellore district to study the various medicinal plants used by the people for the treatment of their ailments such as fever, cold, cough, diabetes, jaundice, diarrhoea, rheumatism, snake bite, and headache. The study also covered the methods used in plant extraction, and the dose, duration and mode of application

A Study the effect of Biofertilizer Azotobacter Chroococcum on the Growth of Mulberry Cropmorus Indica L. and the Yield of Bombyx Mori L

The present study was carried out on the effect of biofertilizer Azotobacter chroococcumon the growth of mulberry plantMorusindica L. and larvalweight, cocoon weight, shell weight, shell ratio and effective rate of rearing (ERR) and length of silk filament of the Bombyxmori. Based on growth of mulberry plant, larval weight and the effect of Azotobacter biofertilizer on length of silk filament was more in treated and which was found to be statistically significant at

Ethnobotanical Survey of Folklore Plants for the Treatment of Jaundice and Snakebites in Vellore Districts of Tamilnadu, India

An ethnobotanical survey was undertaken to collect information from local people about the use of medicinal plants in Vellore district. Local people use certain folklore medicinal plants for the treatment of Jaundice and Snakebite. The Knowledge about the medicinal plants has been transmitted orally from generation. The investigations revealed that there are about 22 species of plants to treat Jaundice and Snakebite. Jaundice and Snakebite are the common problems among the local people. The study indicates that the local inhabitants rely on medicinal plants for treatment

DNA methylation at the mu-1 opioid receptor gene (OPRM1) promoter predicts preoperative, acute, and chronic postsurgical pain after spine fusion.

INTRODUCTION:The perioperative pain experience shows great interindividual variability and is difficult to predict. The mu-1 opioid receptor gene (OPRM1) is known to play an important role in opioid-pain pathways. Since deoxyribonucleic acid (DNA) methylation is a potent repressor of gene expression, DNA methylation was evaluated at the OPRM1 promoter, as a predictor of preoperative, acute, and chronic postsurgical pain (CPSP). METHODS:A prospective observational cohort study was conducted in 133 adolescents with idiopathic scoliosis undergoing spine fusion under standard protocols. Data regarding pain, opioid consumption, anxiety, and catastrophizing (using validated questionnaires) were collected before and 2-3 months postsurgery. Outcomes evaluated were preoperative pain, acute postoperative pain (area under curve [AUC] for pain scores over 48 hours), and CPSP (numerical rating scale >3/10 at 2-3 months postsurgery). Blood samples collected preoperatively were analyzed for DNA methylation by pyrosequencing of 22 CpG sites at the OPRM1 gene promoter. The association of each pain outcome with the methylation percentage of each CpG site was assessed using multivariable regression, adjusting for significant (P<0.05) nongenetic variables. RESULTS:Majority (83%) of the patients reported no pain preoperatively, while CPSP occurred in 36% of the subjects (44/121). Regression on dichotomized preoperative pain outcome showed association with methylation at six CpG sites (1, 3, 4, 9, 11, and 17) (P<0.05). Methylation at CpG sites 4, 17, and 18 was associated with higher AUC after adjusting for opioid consumption and preoperative pain score (P<0.05). After adjusting for postoperative opioid consumption and preoperative pain score, methylation at CpG sites 13 and 22 was associated with CPSP (P<0.05). DISCUSSION:Novel CPSP biomarkers were identified in an active regulatory region of the OPRM1 gene that binds multiple transcription factors. Inhibition of binding by DNA methylation potentially decreases the OPRM1 gene expression, leading to a decreased response to endogenous and exogenous opioids, and an increased pain experience

Experimental realization of strange nonchaotic attractors in a quasiperiodically forced electronic circuit

We have identified the three prominent routes, namely Heagy-Hammel, fractalization and intermittency routes, and their mechanisms for the birth of strange nonchaotic attractors (SNAs) in a quasiperiodically forced electronic system constructed using a negative conductance series LCR circuit with a diode both numerically and experimentally. The birth of SNAs by these three routes is verified from both experimental and their corresponding numerical data by maximal Lyapunov exponents, and their variance, Poincar\'e maps, Fourier amplitude spectrum, spectral distribution function and finite-time Lyapunov exponents. Although these three routes have been identified numerically in different dynamical systems, the experimental observation of all these mechanisms is reported for the first time to our knowledge and that too in a single second order electronic circuit.Comment: 21 figure

Farmers Perception on the Communication Behaviour and Usefulness of Farmer Producer Organizations in Namakkal District of Tamil Nadu

Agriculture is the main occupation of the vast majority of the population of India. Producers companies can help smallholder farmers participate in emerging high-value markets, such as the export market and the unfolding modern retail sector in India. Farmer producer organizations (FPO) need to strengthen support service for small farmers developing a link between farmers and purchasers of agricultural produce. The study was conducted among four assisting FPOs with 45 respondents in Namakkal district of Tamil Nadu. Data were collected through a well-structured interview schedule among the respondents of four farmer producers companies select randomly. The data collected were coded, tabulated, ranked and the result was interpreted worked out. Overall the respondent's member perception score was ranged between “good†to “excellentâ€

Red Deer Optimization with Deep Learning based Robust White Blood Cell Detection and Classification Model

The use of deep learning techniques for White Blood Cell (WBC) classification has garnered significant attention on medical image analysis due to its potential to automate and enhance the accuracy of WBC classification, which is critical for disease diagnosis and infection detection. Convolutional neural networks (CNNs) have revolutionized image analysis tasks, including WBC classification effectively capturing intricate spatial patterns and distinguishing between different cell types. A key advantage of deep learning-based WBC classification is its capability to handle large datasets, enabling models to learn the diverse variations and characteristics of different cell types. This facilitates robust generalization and accurate classification of previously unseen samples. In this paper, a novel approach called Red Deer Optimization with Deep Learning for Robust White Blood Cell Detection and Classification was presented. The proposed model incorporates various components to improve performance and robustness. Image pre-processing involves the utilization of median filtering, while U-Net++ is employed for segmentation, facilitating accurate delineation of WBCs. Feature extraction is performed using the Xception model, which effectively captures informative representations of the WBCs. For classification, BiGRU model is employed, leveraging its ability to model temporal dependencies in the WBC sequences. To optimize the performance of the BiGRU model, the RDO is utilized for parameter tuning, resulting in enhanced accuracy and faster convergence of the deep learning models. The integration of RDO contributes to more reliable detection and classification of WBCs, further improving the overall performance and robustness of the approach. Experimental results demonstrate the superiority of our Red Deer Optimization with deep learning-based approach over traditional methods and standalone deep learning models in achieving robust WBC detection and classification. This research highlights the possibility of combining deep learning techniques with optimization algorithms for improving WBC analysis, offering valuable insights for medical professionals and medical image analysis

Influence of tuck stitch in course direction on thermal comfort characteristics of layered knitted fabrics

The thermal comfort characteristics of bi-layer knitted fabrics have been studied for shuttle badminton sportswear. Bilayerknitted fabrics are developed by changing tuck position in course direction such as 6, 10, 14 and 18 course repeat,keeping the tuck on 12th wale the same. It is observed that the greater the distance between successive tuck points, the betterwill be the air, heat and moisture transfer properties. Bi-layer knitted fabric with slack structure facilitates lower thicknessand mass per unit area, and exhibits better thermal comfort characteristics. By wear trial method, bi-layer knitted fabric withtuck on 18th course and 12th wale shows good rating compared to other bi-layer knitted fabrics. The results are discussed at95% significant level with ANOVA analysis and Friedman one-way analysis of variance