Deep learning in environmental remote sensing: Achievements and challenges

Temperature warnings are important forecasts because they are used to protect life and property. Temperature forecasting is the application of science and technology to predict the state of the temperature for a future time and a given location. Temperature forecasts are made by collecting quantitative data about the current state of the atmosphere. In this paper, a neural network-based algorithm for predicting the temperature is presented. The Neural Networks package supports different types of training or learning algorithms. One such algorithm is Back Propagation Neural Network (BPN) technique. The main advantage of the BPN neural network method is that it can fairly approximate a large class of functions. This method is more efficient than numerical differentiation. The simple meaning of this term is that our model has potential to capture the complex relationships between many factors that contribute to certain temperature. The proposed idea is tested using the real time dataset. The results are compared with practical working of meteorological department and these results confirm that our model have the potential for successful application to temperature forecasting. Real time processing of weather data indicate that the BPN based weather forecast have shown improvement not only over guidance forecasts from numerical models, but over official local weather service forecasts as well. Index Terms—Multi layer perception, Temperature forecasting, Back propagation, Artificial Neural Network

/pdf/an-efficient-weather-forecasting-system-using-artificial-2wtutit9cr.pdf

An Efficient Weather Forecasting System using Artificial Neural Network

Obesity is among the leading causes of elevated cardiovascular disease (CVD) mortality and morbidity. In the present study, the associations between the increase in body mass index (BMI) and the increase rates of CVD and high blood pressure (HBP) in the states of Mississippi, Alabama, Louisiana, Tennessee, and Colorado are examined using regression analysis and by means of neural network models for obesity and HBP. Data from Behavioral Risk Factor Surveillance System were obtained and analyzed for obesity rates, percent of myocardial infarction, stroke, and HBP from 2005-2009. Results of this study showed a low association between obesity and myocardial infarction rates (R2=0.067); a moderate association with stroke rates ((R2=0.462); and a strong association with HBP rates ((R2=0.811). The highest rates of obesity, CVD, and HBP were found in Mississippi, while Colorado had the lowest rates. Maintaining healthy weight helps reduce the risks of developing CVD.

/pdf/relationships-between-obesity-and-cardiovascular-diseases-in-245i9svhq4.pdf

Relationships between Obesity and Cardiovascular Diseases in Four Southern States and Colorado

Towards a smart energy network: The roles of fuel/electrolysis cells and technological perspectives

Spatially high resolution climate information is required for a variety of applications in but not limited to functional biodiversity research. In order to scale the generally plot-based research findings to a landscape level, spatial interpolation methods of meteorological variables are required. Based on a network of temperature observation plots across the southern slopes of Mt. Kilimanjaro, the skill of 14 machine learning algorithms in predicting spatial temperature patterns is tested and evaluated against the heavily utilized kriging approach. Based on a 10-fold cross-validation testing design, regression trees generally perform better than linear and non-linear regression models. The best individual performance has been observed by the stochastic gradient boosting model followed by Cubist, random forest and model averaged neural networks which except for the latter are all regression tree-based algorithms. While these machine learning algorithms perform better than kriging in a quantitative evaluation, the overall visual interpretation of the resulting air temperature maps is ambiguous. Here, a combined Cubist and residual kriging approach can be considered the best solution.

Evaluating machine learning approaches for the interpolation of monthly air temperature at Mt. Kilimanjaro, Tanzania

Artificial neural networks for automated year-round temperature prediction

The mitigation of crop loss due to damaging freezes requires accurate air temperature prediction models. Previous work established that the Ward-style artificial neural network (ANN) is a suitable tool for developing such models. The current research focused on developing ANN models with reduced average prediction error by increasing the number of distinct observations used in training, adding additional input terms that describe the date of an observation, increasing the duration of prior weather data included in each observation, and reexamining the number of hidden nodes used in the network. Models were created to predict air temperature at hourly intervals from one to 12 hours ahead. Each ANN model, consisting of a network architecture and set of associated parameters, was evaluated by instantiating and training 30 networks and calculating the mean absolute error (MAE) of the resulting networks for some set of input patterns. The inclusion of seasonal input terms, up to 24 hours of prior weather information, and a larger number of processing nodes were some of the improvements that reduced average prediction error compared to previous research across all horizons. For example, the four-hour MAE of 1.40°C was 0.20°C, or 12.5%, less than the previous model. Prediction MAEs eight and 12 hours ahead improved by 0.17°C and 0.16°C, respectively, improvements of 7.4% and 5.9% over the existing model at these horizons. Networks instantiating the same model but with different initial random weights often led to different prediction errors. These results strongly suggest that ANN model developers should consider instantiating and training multiple networks with different initial weights to establish preferred model parameters. Keywords—Decision support systems, frost protection, fruit, time-series prediction, weather modeling

/pdf/improving-air-temperature-prediction-with-artificial-neural-2qoo19perh.pdf

Improving Air Temperature Prediction with Artificial Neural Networks

Finding synchronizing sequences for the finite automata is a very important problem in many practical applications (part orienters in industry, reset problem in biocomputing theory, network issues etc). Problem of finding the shortest synchronizing sequence is NP-hard, so polynomial algorithms probably can work only as heuristic ones. In this paper we propose two versions of polynomial algorithms which work better than well-known Eppstein's Greedy and Cycle algorithms. Keywords—Synchronizing words, reset sequences, Cerný Conjecture.

An Enhanced Artificial Neural Network for Air Temperature Prediction

An Enhanced Artificial Neural Network for Air Temperature Prediction.

Interactions between microbes and their hosts have important outcomes for host and environmental health. Foliar fungal endophytes that infect healthy plants can harbor facultative endosymbionts called endohyphal bacteria, which can influence the outcome of plant-fungus interactions. ABSTRACT Symbiosis with bacteria is widespread among eukaryotes, including fungi. Bacteria that live within fungal mycelia (endohyphal bacteria) occur in many plant-associated fungi, including diverse Mucoromycota and Dikarya. Pestalotiopsis sp. strain 9143 is a filamentous ascomycete isolated originally as a foliar endophyte of Platycladus orientalis (Cupressaceae). It is infected naturally with the endohyphal bacterium Luteibacter sp. strain 9143, which influences auxin and enzyme production by its fungal host. Previous studies have used transcriptomics to examine similar symbioses between endohyphal bacteria and root-associated fungi such as arbuscular mycorrhizal fungi and plant pathogens. However, currently there are no gene expression studies of endohyphal bacteria of Ascomycota, the most species-rich fungal phylum. To begin to understand such symbioses, we developed methods for assessing gene expression by Pestalotiopsis sp. and Luteibacter sp. when grown in coculture and when each was grown axenically. Our assays showed that the density of Luteibacter sp. in coculture was greater than in axenic culture, but the opposite was true for Pestalotiopsis sp. Dual-transcriptome sequencing (RNA-seq) data demonstrate that growing in coculture modulates developmental and metabolic processes in both the fungus and bacterium, potentially through changes in the balance of organic sulfur via methionine acquisition. Our analyses also suggest an unexpected, potential role of the bacterial type VI secretion system in symbiosis establishment, expanding current understanding of the scope and dynamics of fungal-bacterial symbioses. IMPORTANCE Interactions between microbes and their hosts have important outcomes for host and environmental health. Foliar fungal endophytes that infect healthy plants can harbor facultative endosymbionts called endohyphal bacteria, which can influence the outcome of plant-fungus interactions. These bacterial-fungal interactions can be influential but are poorly understood, particularly from a transcriptome perspective. Here, we report on a comparative, dual-RNA-seq study examining the gene expression patterns of a foliar fungal endophyte and a facultative endohyphal bacterium when cultured together versus separately. Our findings support a role for the fungus in providing organic sulfur to the bacterium, potentially through methionine acquisition, and the potential involvement of a bacterial type VI secretion system in symbiosis establishment. This work adds to the growing body of literature characterizing endohyphal bacterial-fungal interactions, with a focus on a model facultative bacterial-fungal symbiosis in two species-rich lineages, the Ascomycota and Proteobacteria.

Transcriptional Profiles of a Foliar Fungal Endophyte (Pestalotiopsis, Ascomycota) and Its Bacterial Symbiont (Luteibacter, Gammaproteobacteria) Reveal Sulfur Exchange and Growth Regulation during Early Phases of Symbiotic Interaction

Brian A. Smith

Papers

Artificial neural networks for automated year-round temperature prediction

Improving Air Temperature Prediction with Artificial Neural Networks

An Enhanced Artificial Neural Network for Air Temperature Prediction

An Enhanced Artificial Neural Network for Air Temperature Prediction.

Transcriptional Profiles of a Foliar Fungal Endophyte (Pestalotiopsis, Ascomycota) and Its Bacterial Symbiont (Luteibacter, Gammaproteobacteria) Reveal Sulfur Exchange and Growth Regulation during Early Phases of Symbiotic Interaction