Bayesian Approaches to Emulation for a Complex Computer Crop Yield Simulator with Mixed Inputs

Agriculture is one area where the simulation of crop growth, nutrition, soil condition and pollution could be invaluable in any land management decisions. The Environmental Policy Integrated Climate Model (EPIC) is a simulation model to investigate the behaviour of crop yield in response to changes in inputs such as fertiliser levels, soil, steepness, and other environmental covariates. We build a model for crop yield around a non-linear Mitscherlich Baule growth model to make inferences about crop yield response to changes in continuous input and factor variables. A Bayesian hierarchical approach to the modelling was taken for mixed inputs, requiring Markov Chain Monte Carlo simulations to obtain samples from the posterior distributions, to validate and illustrate the results, and to carry out model selection. The emulation of complex computer simulations has become an effective tool in exploring this high-dimensional simulated process's behaviour. Initially, we built a Bayes linear emulator to efficiently emulate crop yield as a function of the simulator's continuous inputs only. We explore emulator diagnostics and present the results from the emulation of a subset of the simulated EPIC data output. Computer models with quantitative inputs are used widely, but the challenge is incorporating the factors. We propose a framework for solving this issue considering the Bayes linear emulation approach. We explore a variety of correlation structures to represent the mixed inputs and combine this with the Bayes linear approach to construct an emulator. Finally, we developed a method to make an optimal decision for the farmers to gain maximum utility considering yield and pollutants, accounting for weather factors, land characteristics and fertiliser use

Bayes Linear Emulation of Simulated Crop Yield

The analysis of the output from a large-scale computer simulation experiment can pose a challenging problem in terms of size and computation. We consider output in the form of simulated crop yields from the Environmental Policy Integrated Climate (EPIC) model, which requires a large number of inputs—such as fertilizer levels, weather conditions, and crop rotations—inducing a high dimensional input space. In this paper, we adopt a Bayes linear approach to efficiently emulate crop yield as a function of the simulator fertilizer inputs. We explore emulator diagnostics and present the results from emulation of a subset of the simulated EPIC data output

Text to Emotion Extraction Using Supervised Machine Learning Techniques

Proliferation of internet and social media has greatly increased the popularity of text communication. People convey their sentiment and emotion through text which promotes lively communication. Consequently, a tremendous amount of emotional text is generated on different social media and blogs in every moment. This has raised the necessity of automated tool for emotion mining from text. There are various rule based approaches of emotion extraction form text based on emotion intensity lexicon. However, creating emotion intensity lexicon is a time consuming and tedious process. Moreover, there is no hard and fast rule for assigning emotion intensity to words. To solve these difficulties, we propose a machine learning based approach of emotion extraction from text which relies on annotated example rather emotion intensity lexicon. We investigated Multinomial Naïve Bayesian (MNB) Classifier, Artificial Neural Network (ANN) and Support Vector Machine (SVM) for mining emotion from text. In our setup, SVM outperformed other classifiers with promising accuracy

A review of thz technologies for rapid sensing and detection of viruses including SARS-CoV-2

Virus epidemics such as Ebola virus, Zika virus, MERS-coronavirus, and others have wreaked havoc on humanity in the last decade. In addition, a coronavirus (SARS-CoV-2) pandemic and its continuously evolving mutants have become so deadly that they have forced the entire technical advancement of healthcare into peril. Traditional ways of detecting these viruses have been successful to some extent, but they are costly, time-consuming, and require specialized human resources. Terahertz-based biosensors have the potential to lead the way for low-cost, non-invasive, and rapid virus detection. This review explores the latest progresses in terahertz technology-based biosensors for the virus, viral particle, and antigen detection, as well as upcoming research directions in the field

Defining Optimal Aerobic Exercise Parameters to Affect Complex Motor and Cognitive Outcomes after Stroke: A Systematic Review and Synthesis

Although poststroke aerobic exercise (AE) increases markers of neuroplasticity and protects perilesional tissue, the degree to which it enhances complex motor or cognitive outcomes is unknown. Previous research suggests that timing and dosage of exercise may be important. We synthesized data from clinical and animal studies in order to determine optimal AE training parameters and recovery outcomes for future research. Using predefined criteria, we included clinical trials of stroke of any type or duration and animal studies employing any established models of stroke. Of the 5,259 titles returned, 52 articles met our criteria, measuring the effects of AE on balance, lower extremity coordination, upper limb motor skills, learning, processing speed, memory, and executive function. We found that early-initiated low-to-moderate intensity AE improved locomotor coordination in rodents. In clinical trials, AE improved balance and lower limb coordination irrespective of intervention modality or parameter. In contrast, fine upper limb recovery was relatively resistant to AE. In terms of cognitive outcomes, poststroke AE in animals improved memory and learning, except when training was too intense. However, in clinical trials, combined training protocols more consistently improved cognition. We noted a paucity of studies examining the benefits of AE on recovery beyond cessation of the intervention

Impact of nitrogen and phosphorus fertilizer on growth and yield of bambara groundnut

Bambara groundnut (Vigna subterranea) is an indigenous African crop which belongs to the family fabacea and sub-family of faboidea. It seeds contain 63% carbohydrate, 19% protein and 6.5% oil and good source of fibre, calcium, iron and potassium. Hence, this study aimed to determine the effect of nitrogen (N) and phosphorus (P) on growth and yield bambara groundnut. A pot experiments was conducted in ladang 15 at the Faculty of Agriculture; Universiti Putra Malaysia. The experiment was performed Randomized Complete Block Design (RCBD). The size of the pot was 65.94 cm2. The experiment was conducted in a factorial design with four levels of N (0, 10, 20, 30 kg/ha) and P (0, 20, 40 and 60 kg/ha). In this study, N and P fertilizer was played dominating role for vegetative growth of the plant. Plant height (20.65 cm), leaves number (262), leaf area (2140.54 cm2), number of pod (47.25) and pod weight (22.8 g) increased with the application of level of N and P. Vegetative growth and yield of the plant was better at N30P60 kg/ha than the all other treatments. It can be concluded that by using N30P60 kg/ha growth and yield of bambara groundnut is maximum

Validation of some disease-resistance molecular markers associated with multiple diseases in tomato for marker-assisted selection program

Marker-assisted selection (MAS) is a tool that is widely applied in tomato resistance breeding. To determine the robustness of some molecular markers commonly used in MAS, extensive screening of 964 tomato lines was performed under a controlled experimental condition. Initially, the application of 36 molecular markers targeting 26 resistance genes (R genes) and 14 major diseases was evaluated. Here, we employed basic molecular biology and bioinformatics techniques for analysis where polymorphism, accuracy and clearness of amplicons constituted the selection criteria of markers. Upon initial analysis, 20 of these markers designated as efficient markers, among which 8 were considered gene-based markers and referred to as perfect markers were selected for detail evaluation. Information extrapolated from PCR result revealed 18 R genes that control 12 diseases were grouped under efficient markers. On the other hand, grouping of breeding lines based on the number of R gene harbored comprehensively revealed 62% of the lines to be void of R gene, while 38% carry different types of R genes. This provides us with an avenue to better understand new sources of resistance in the breeding lines. Conclusively, these efficient markers and their limited PCR condition can be suggested as basis of a diagnostic kit for MAS applications against 12 major tomato diseases and the identified resistant breeding lines could be conserved in order to be propagated as different sources of resistance for the development of new resistant varieties. Therefore, in areas with high vulnerability to diseases, high efficiency combination of the relevant R genes and their pyramiding into commercial tomato varieties are proposed to be implemented as a pragmatic approach

Building Information Modeling and Artificial Intelligence Based Smart Construction Management: Materials and Electrical

With the development of society and technological progress, the requirements of government regulatory departments for engineering construction efficiency, quality, and safety are constantly increasing. The traditional extensive construction process can no longer meet the requirements of modern construction industry development. Based on the shortcomings of traditional construction processes, the concept of intelligent construction has been introduced. The construction of new smart and digital twin (DT) cities is entering an explosive period. The application of building rapid modeling technology based on artificial intelligence (AI) and building information modeling (BIM) integration in smart cities has gradually begun new explorations and attempts, and its application value is becoming increasingly prominent. A brand-new auto-machine learning (auto-ML) integrated algorithm technology platform for 3D building modeling is being developed and improved over time by combining AI and BIM technology in a deep way. This allows for fast and accurate modeling as well as high-value scenarios in the smart city industry, including architecture, municipal engineering, roads, and bridges.&nbsp

Performance of yield and yield contributing characteristics of BC2F3 population with addition of blast resistant gene

The study was carried out in the University Putra Malaysia (UPM) Rice Research Centre to evaluate the yield performance of newly developed selected blast resistant plants of BC2 F3 generations derived from a cross between MR263, a high yielding rice variety but blast susceptible and Pongsu Seribu 1, donor with blast resistant (Pi-7(t) and Pi-d (t)1, Pir2-3(t) genes and qLN2 QTL), Malaysian local variety. On the basis of assessed traits, the plants 12, 6, 7, 5, 21, 22, 5, 26, 11, 8, 10, 13 and 15 had the higher yield, blast resistant and good morphological traits. More than 70% heritability was found in days to maturity, plant height, tiller numbers per hill, and panicle per hill, 80% heritability was found in filled grain and yield per hill and more than 90% heritability was found in grain length, grain width and seed weight. Cluster analysis based on the traits grouped 30 plants along with MR263 into seven clusters. According to PCA, the first four principal components account for about 69.3% total variation for all measured traits and exhibited high correlation among the characteristics analyzed