Redes Neurais artificiais e Sistema Adaptativo de Inferência Neuro Fuzzy para análise e previsão da produtividade do trigo

The current study evaluated the prediction of the yield of wheat crops in the Bagalkot district of Karnataka State, India. The study aimed to provide crop yield predictions to help farmers optimize their cultivation and marketing strategies. The model used various independent variables, such as temperature, humidity of air, and water resources, to predict growth in the yield of wheat crops. The correlation analysis helps determine the strength and direction of the relationship between the variables based on the results. The statistical analysis identifies the variables that have a significant impact on crop yield growth. The work developed and tested two different models (the Artificial Neural Network (ANN) model and the Adaptive Neuro-fuzzy Interference System (ANFIS) to predict crop yield growth based on the selected independent variables. The ANFIS model was particularly interesting as it can predict a mapping between the input and output parameters, which can be useful for understanding the relationships between different variables. ANFIS was considered a better predictor than ANN as the error percentage ranged from 0-3%. Overall, the work highlighted the importance of crop yield predictions and the potential benefits that simulations can generate for farmers and the agriculture sector in general.O presente estudo avaliou a previsão do rendimento das culturas de trigo no distrito de Bagalkot, do Estado de Karnataka, India. O estudo teve como objetivo fornecer previsões de rendimento das colheitas para ajudar os agricultores a otimizar suas estratégias de cultivo e comercialização. O modelo usou várias variáveis independentes tais como temperatura, humidade do ar e recursos hídricos para prever o crescimento no rendimento das culturas de trigo. O trabalho se desenvolveu e testou dois modelos diferentes: Modelo de Rede Neural Artificial (Artificial Neural Network – ANN) e Sistema de Interferência Neuro-fuzzy Adaptativo (Adaptive Neuro-fuzzy Interference System - ANFIS) a fim prever o crescimento do rendimento das culturas com base nas variáveis independentes selecionadas. O modelo ANFIS foi particularmente interessante, pois pôde prever um mapeamento entre os parâmetros de entrada e saída, os quais podem ser úteis para compreender a relação entre diferentes variáveis. ANFIS foi considerado um modelo de predição melhor que o modelo ANN, com uma porcentagem de erro variando de 0-3%. De maneira geral, o trabaho destacou a importância das previsões do rendimento das culturas e os potenciais benefícios que as simulações podem gerar para os agricultores e para o setor agrícola em geral

ARTIFICIAL NEURAL NETWORKS AND ADAPTIVE NEURO FUZZY INFERENCE SYSTEM FOR WHEAT YIELD ANALYSIS AND PREDICTION

The current study evaluated the prediction of the yield of wheat crops in the Bagalkot district of Karnataka State, India. The study aimed to provide crop yield predictions to help farmers optimize their cultivation and marketing strategies. The model used various independent variables, such as temperature, humidity of air, and water resources, to predict growth in the yield of wheat crops. The correlation analysis helps determine the strength and direction of the relationship between the variables based on the results. The statistical analysis identifies the variables that have a significant impact on crop yield growth. The work developed and tested two different models (the Artificial Neural Network (ANN) model and the Adaptive Neuro-fuzzy Interference System (ANFIS) to predict crop yield growth based on the selected independent variables. The ANFIS model was particularly interesting as it can predict a mapping between the input and output parameters, which can be useful for understanding the relationships between different variables. ANFIS was considered a better predictor than ANN as the error percentage ranged from 0-3%. Overall, the work highlighted the importance of crop yield predictions and the potential benefits that simulations can generate for farmers and the agriculture sector in general

Nations within a nation: variations in epidemiological transition across the states of India, 1990–2016 in the Global Burden of Disease Study

18% of the world's population lives in India, and many states of India have populations similar to those of large countries. Action to effectively improve population health in India requires availability of reliable and comprehensive state-level estimates of disease burden and risk factors over time. Such comprehensive estimates have not been available so far for all major diseases and risk factors. Thus, we aimed to estimate the disease burden and risk factors in every state of India as part of the Global Burden of Disease (GBD) Study 2016

Validation of FCOM profiles for aircraft engine flight data using neural networks

This paper explores the application of artificial neural network approach for aircraft engine health monitoring. The Digital Flight Data Recorder (DFDR) has volumes of data which if mined appropriately can provide valuable information about the aircraft health. The Flight Crew Operating Manual (FCOM) lays down operational profiles, which are recommended to be followed for efficient fuel usage and for minimizing maintenance effort. In the proposed system, the information from FCOM profiles and ‘known’ flight data has been fused to train a back propagation feed-forward neural network. The predictions made by the neural network regarding the expected data of required engine parameters have been used to monitor the flight data and diagnose the health of the aircraft engine in relevance to the FCOM profiles. A Matlab GUI has been developed to simulate the ‘unknown’ flight data through a Simulink model for the neural network. Data from the A320 family of aircrafts has been used for training and simulating the model and preliminary results are detailed in the paper. The simulation results exhibit that the data used is fairly healthy and show a very low level of severity of degradation with respect to the profiles studied

Purification and Functional Characterization of a Biologically Active Full-Length Feline Immunodeficiency Virus (FIV) Pr50Gag

The feline immunodeficiency virus (FIV) full-length Pr50Gag precursor is a key player in the assembly of new viral particles. It is also a critical component of the efficient selection and packaging of two copies of genomic RNA (gRNA) into the newly formed virus particles from a wide pool of cellular and spliced viral RNA. To understand the molecular mechanisms involved during FIV gRNA packaging, we expressed the His6-tagged and untagged recombinant FIV Pr50Gag protein both in eukaryotic and prokaryotic cells. The recombinant Pr50Gag-His6-tag fusion protein was purified from soluble fractions of prokaryotic cultures using immobilized metal affinity chromatography (IMAC). This purified protein was able to assemble in vitro into virus-like particles (VLPs), indicating that it preserved its ability to oligomerize/multimerize. Furthermore, VLPs formed in eukaryotic cells by the FIV full-length Pr50Gag both in the presence and absence of His6-tag could package FIV sub-genomic RNA to similar levels, suggesting that the biological activity of the recombinant full-length Pr50Gag fusion protein was retained in the presence of His6-tag at the carboxy terminus. Successful expression and purification of a biologically active, recombinant full-length Pr50Gag-His6-tag fusion protein will allow study of the intricate RNA-protein interactions involved during FIV gRNA encapsidation

Statistical Analysis of Repeat Test Results for Assessment of Wind Tunnel Data Quality

A methodology for quantifying wind tunnel data quality from repeat tests is described. The methodology largely adopts the measurement uncertainty concepts and statistical analysis techniques recommended by AIAA Standards. Repeatability quality of measurements is quantified in terms of three statistical parameters viz., precision limit (or random uncertainty), tolerance interval and precision interval. A description of the methods used for determining these statistical parameters is given. A computer program developed to facilitate routine application of the above analysis to repeat test data obtained in the NAL 1.2m and 0.6m wind tunnels is described. To illustrate the application of the above methodology and use of the computer program an analysis of longitudinal aerodynamic data obtained from repeat tests on a typical fighter aircraft model in the 1.2m tunnel has been made. Results of this analysis along with a discussion of the same, and a comparison with similar results obtained on a commercial transport airplane configuration in the NASA 2.5m cryogenic tunnel are included in the report

Emerging towards zero carbon footprint via carbon dioxide capturing and sequestration

Concerns about climatic changes and global temperature enhancements have sparked efforts worldwide to curb the magnitude of atmospheric carbon dioxide. A key tactic for achieving carbon dioxide emission mitigation goals is Carbon dioxide capturing and sequestration, which is critical for the seamless changeover from the prevailing fossil-based power systems to more eco-friendly future energy systems. Among the carbon dioxide capturing techniques, post-combustion capture is the most practical method for retrofitting existing power plants although it result with 3–15 vol.% concentrated carbon dioxide gas stream. Chemical looping combustion capturing receives much attention receives much attention owing to its non-pollution nature and the yielding of highly concentrated carbon dioxide stream, up to 100%. This review also explores a variety of sequestration strategies, including geological carbon dioxide sequestration with multiple geological carbon dioxide storage sinks, mineral carbonation sequestration, as well as marine sequestration. The carbon dioxide transportation and the storage facilities comprising pressure vessels, pipelines, and cryogenic storage tanks are also discussed briefly. The Enhanced gas recovery, Enhanced water recovery, and Enhanced oil recovery, which rely on geologically stored carbon dioxide are also taken into account in this analysis as a part of the commercial-economic application of carbon dioxide capturing and sequestration. Along with the risk considerations related to the sequestration processes, the efficient exploitation of the sequestered carbon dioxide is delineated as a road map leading to future prospects. Concerns have been raised that the widespread adoption of carbon dioxide capturing and sequestration will likely be affected by the general public perceptions due to unawareness, along with potential leakage risks and the enormous capturing cost, which should be puzzled out for the effective uptake of carbon dioxide capturing and sequestration strategies

Management of psoriasis -ayurveda and allopathy-A review

Psoriasis is a chronic infl ammatory skin disease that affects 2% to 4% of the population. Infl ammatory arthritis develops in approximately 30% of patients with psoriasis and can have a major effect on activities of daily living and quality of life. Peripheral joint involvement in patients with psoriatic arthritis can be oligo articular or poly articular and can cause joint destruction.</p

Development of Hierarchical Safety Performance Functions for Urban Mid-blocks

AbstractCrash frequency and severity are influenced by a variety of variables that represent regional, site, crash and driver-vehicle unit characteristics. In the traditional methods of crash prediction, all the variables are considered at a single level and the multilevel structure inherent in the crash data is ignored. Hierarchical modelling is a statistical technique that allows multilevel data structure to be properly specified and estimated. In the present study, a hierarchical modelling approach was used to estimate the crash frequency and severity of single and dual carriageway roads. Since the crash patterns of single and dual carriageways were found to be different, separate models were developed for these facilities. A two level design was adopted for crash frequency prediction and four level design for crash severity prediction. The two levels in the crash frequency prediction are geographic region level and traffic site level. The additional levels in severity prediction are crash level and driver-vehicle unit level. The study indicates that hierarchical models performed better for crash frequency and severity prediction. Hierarchical models are strongly advocated for crash data that has correlated observations within groups