Quantifying Impact of Droughts on Barley Yield in North Dakota, Usa Using Multiple Linear Regression and Artificial Neural Network

Abstract

This tutorial is based on modification of the professor nomination lecture presented two years ago in front of the Scientific Council of the Czech Technical University in Prague [16].It is devoted to the techniques for the models developing suitable for processes forecasting in complex systems. Because of the high sensitivity of the processes to the initial conditions and, consequently, due to our limited possibilities to forecast the processes for the long-term horizon, the attention is focused on the techniques leading to practical applications of the short term prediction models. The aim of this tutorial paper is to bring attention to possible difficulties which designers of the predicting models and their users meet and which have to be solved during the prediction model developing, validation, testing, and applications. The presented overview is not complete, it only reflects the author's experience with developing of the prediction models for practical tasks solving in banking, meteorology, air pollution and energy sector. The paper is completed by an example of the global solar radiation prediction which forms an important input for the electrical energy production forecast from renewable sources. The global solar radiation forecasting is based on numerical weather prediction models. The time-lagged ensemble technique for uncertainty quantification is demonstrated on a simple example.

Agrobacterium rhizogenes transformed soybeans with AtPAP18 gene show enhanced phosphorus uptake and biomass production

“…soybean crops overexpressing one of the relevant PAP genes would show improvement through substantial increases in their biomass, yield [6,13] and tiller numbers [27]. As reported [4,6,9,11,27], APases act as a key factor in the mineralization of organic P, remobilization, reallocation and utilization of Pi. Similar results have been reported for overexpression of AtPAP18 in tobacco [13], AtPAP15 in soybean [6], AtPAP10 in Arabidopsis [22] and OsPAP10c in rice [27], in which the biomass and the P status showed substantial increasing, particularly in Pi deficient conditions [13].…”

“…growing soils mainly suffer from a lack of phosphorus [3]. As a result, to keep the highest levels of soybean production, phosphorus fertilizers should be added to soils with phosphorous concentrations less than 10 mmol/L [4]. However, most of the annually added phosphorus fertilizers are fixed in the soil in organic forms by adsorption, sedimentation and transformation, which is challenging because the additional dissolution of phosphates is potentially harmful to the environment [5].…”

“…These enzymes catalyze the breakdown of organic phosphates and added P fertilizers into Pi. PAPs are highly induced by P starvation and can be secreted or located in the cellular organelles to utilize externally available Pi in the soil or to recycle it from intracellular P sources [4]. PAP family enzymes are distributed across different plants, for example the genomes of Arabidopsis thaliana, Oriza sativa and Glycine max include 29, 26 and 35 PAP family members, respectively [7,9,10].…”

A Neural Network Approach for Assessing the Relationship Between Grip Strength and Hand Anthropometry

“…Basically, ANN performs the function of nonlinear mapping or pattern recognition. In other words, by using ANN it is possible to model complex and non-linear systems [34]. The proper selection of neural network architecture is a crucial decision for accurate prediction [41,21].…”

Determining Mechanical and Physical Properties of Phospho-Gypsum and Perlite-Admixtured Plaster Using an Artificial Neural Network and Regression Models

“…Choosing the right ANN depends on the type of the application and data representation of a given problem [19,11,20].…”