Injury and damage by threecornered alfalfa hopper, Spissistilus festinus (Say), in group IV soybean

Threecornered alfalfa hopper, Spissistilus festinus (Say), is a pest of soybean during vegetative and reproductive stages. The primary damage from this pest is girdling of the main stem during vegetative stages and girdling of the petioles during reproductive stages. Previous research determined that yield losses are greater during reproductive stages than vegetative stages. I hypothesized that some reproductive stages are more vulnerable to damage than other stages. I used field cages infested with different pest densities at five reproductive stages of group IV soybean. A greenhouse study compared the injury and damage caused by the adults and nymphs. The field study showed that the threecornered alfalfa hopper did not significantly impact yields at the growth stages studied. Adults preferred to feed on leaf petioles while nymphs fed mostly on stems. Significant yield reduction was noticed at growth stage R4 in the greenhouse due to adult and nymphs compared to control

Developing a MongoDB Monitoring System using NoSQL Databases for Monitored Data Management

MongoDB is a NoSQL database, specifically used to efficiently store and access a large quantity of unstructured data over a distributed cluster of nodes. As the number of nodes in the cluster increases, it becomes difficult to manually monitor different components of the database. This poses an interesting problem of monitoring the MongoDB database to view the state of the system at any point. Although a few proprietary monitoring tools exist to monitor MongoDB clusters, they are not freely available for use in academia. Therefore, the focus of this project is to create a monitoring system that is completely built from open-source resources. To automatically monitor a MongoDB cluster, several components are to be built: monitoring agents that obtain this information from the nodes in the cluster, storage mechanisms to save this information for future use and write buffers to temporarily hold monitored records before they are written to storage. The monitoring agents have to be created to obtain only the information that a user of a monitoring system might find useful. Since monitored data is expected to be of high volume and velocity, NoSQL databases are ideal candidates for the storage component of the monitoring system. MongoDB, Cassandra, and OpenTSDB are identified as suitable candidates and their performances are compared with respect to several aspects such as read and write performance and storage requirements. In an attempt to improve the write performance of the system, the performance impact of adding a BigQueue as a write buffer to the storage is also studied

Risk based integrity modeling for the optimal maintenance strategies of offshore process components

Ageing of components is a major threat to asset integrity in offshore process facilities. A robust maintenance strategy mitigates the effects of age-based structural degradations and reduces the threat of failure. Failure caused by structural degradations is a stochastic process. For maintenance strategies to be effective, the stochastic nature of failure has to be taken into consideration. Risk based integrity modeling (RBIM) is a newly-developed approach that aims at the protection of human life, financial investment, and the environment against the consequences of failure. RBIM quantifies the risk to which individual components are subjected and uses this as a basis for the design of a maintenance strategy. Risk is a combination of the probability and the consequence of failure. The major age-based structural degradations to be addressed include corrosion; such as uniform, pitting and erosion mechanisms; and cracking; such as stress corrosion, corrosion fatigue, and hydrogen induced cracking. In this study, component degradation processes are modeled stochastically to estimate the probability of failure using Bayesian analysis methods. Bayesian analysis improves the fidelity on the likelihood of future events by relating with the prior and posterior probabilities. Prior modeling is performed using judgmental studies and analyzing historic databases from similar installations. For the assessment of ageing assets and degradation mechanisms, field non-destructive test (NDT) data is used to establish the likelihood function. The posterior modeling is performed using a simulation-based Metropolis-Hastings algorithm and Laplace approximation since the prior-likelihood combinations are non-conjugate pairs. In this study, the consequences of failure are modeled using economic analysis to estimate the costs of failure, inspection and maintenance. The cost of failure includes lost production, loss of shutdown, cost of spill cleanup, loss caused by environmental damage and liability. The inspection and maintenance costs are estimated using the inspection and maintenance tasks, access, surface preparation, gauging defects, coating and restoration costs. Maintenance may be either minimal repair or replacement of components. The annual equivalent cost (AEC) of operation and maintaining a facility is the summation of the annual equivalent costs of failure, inspection, and maintenance. The cumulative posterior failure probability is combined with AEC to produce the operational life risk curve for a component. Since the risk curve is a convex function of the maintenance interval, then the optimum interval is the global minimum point. The operational risk is thus reduced to as low as reasonably practicable level by optimal maintenance

Identification of bioactive compounds from the ethnomedicinal plant Senna alata (L.) Roxb. (fabaceae) through in vitro and molecular docking analysis against ?-glucosidase enzyme: a diabetic drug target

Senna alata (L.) Roxb. belongs to the family Fabaceae, is reported to have traditional use to treat diabetics and is selected for the study. Preliminary phytochemical analysis was carried out in the selected plant, indicating comparatively higher amounts of phenol, flavonoid, tannin and alkaloids in quantification. The antidiabetic activity of the plant was analyzed and the result indicated that the acetone and methanolic extract showed the lowest IC50 values in a-amylase and a-glucosidase assays respectively. The methanolic extract, which showed an IC50 (39.977 ug/ml) value similar to the standard (35.151 ug/ml), was selected for HR-LCMS analysis. HR-LCMS analysis indicated compounds that exhibit antidiabetic properties, including rutin, kaempferol, rhein and luteolin in the extract. Molecular docking analysis revealed 5 compounds showing better binding affinity namely 5-methoxyhydnocarpin-D, quercetin 3-rhamnoside-7-glucoside, marimetin, kaempferol and luteolin, than the standard drugs voglibose and acarbose. The present in vitro antidiabetic study against 5NN8 target protein was supported by molecular docking analysis. Therefore, further study of bioactive compounds identified through HR-LC MS can help develop future drug leads. Using such medicinal plants can support the improvement of the healthcare system as they do not have many side effects. S. alata is an important medicinal plant, but at the same time, it has become a weed in different parts of Kerala. Validation of medicinal properties and identification of bioactive molecules can help the sustainable utilization of the plant

Fourier neural operator for learning solutions to macroscopic traffic flow models: Application to the forward and inverse problems

Deep learning methods are emerging as popular computational tools for solving forward and inverse problems in traffic flow. In this paper, we study a neural operator framework for learning solutions to nonlinear hyperbolic partial differential equations with applications in macroscopic traffic flow models. In this framework, an operator is trained to map heterogeneous and sparse traffic input data to the complete macroscopic traffic state in a supervised learning setting. We chose a physics-informed Fourier neural operator ($\pi$-FNO) as the operator, where an additional physics loss based on a discrete conservation law regularizes the problem during training to improve the shock predictions. We also propose to use training data generated from random piecewise constant input data to systematically capture the shock and rarefied solutions. From experiments using the LWR traffic flow model, we found superior accuracy in predicting the density dynamics of a ring-road network and urban signalized road. We also found that the operator can be trained using simple traffic density dynamics, e.g., consisting of $2-3$ vehicle queues and $1-2$ traffic signal cycles, and it can predict density dynamics for heterogeneous vehicle queue distributions and multiple traffic signal cycles $(\geq 2)$ with an acceptable error. The extrapolation error grew sub-linearly with input complexity for a proper choice of the model architecture and training data. Adding a physics regularizer aided in learning long-term traffic density dynamics, especially for problems with periodic boundary data

Mutational spectrum of APC and genotype-phenotype correlations in Greek FAP patients

<p>Abstract</p> <p>Background</p> <p>Familial adenomatous polyposis, an autosomal dominant inherited disease caused by germline mutations within the <it>APC </it>gene, is characterized by early onset colorectal cancer as a consequence of the intrinsic phenotypic feature of multiple colorectal adenomatic polyps. The genetic investigation of Greek adenomatous polyposis families was performed in respects to <it>APC </it>and <it>MUTYH </it>germline mutations. Additionally, all available published mutations were considered in order to define the <it>APC </it>mutation spectrum in Greece.</p> <p>Methods</p> <p>A cohort of 25 unrelated adenomatous polyposis families of Greek origin has been selected. Genetic testing included direct sequencing of <it>APC </it>and <it>MUTYH </it>genes. <it>APC </it>gene was also checked for large genomic rearrangements by MLPA.</p> <p>Results</p> <p>Analysis of the <it>APC </it>gene performed in a Greek cohort of twenty five FAP families revealed eighteen different germline mutations in twenty families (80%), four of which novel. Mutations were scattered between exon 3 and codon 1503 of exon 15, while no large genomic rearrangements were identified.</p> <p>Conclusion</p> <p>This concise report describes the spectrum of all <it>APC </it>mutations identified in Greek FAP families, including four novel mutations. It is concluded that the Greek population is characterized by genetic heterogeneity, low incidence of genomic rearrangements in <it>APC </it>gene and lack of founder mutation in FAP syndrome.</p

Learning-based solutions to nonlinear hyperbolic PDEs: Empirical insights on generalization errors

We study learning weak solutions to nonlinear hyperbolic partial differential equations (H-PDE), which have been difficult to learn due to discontinuities in their solutions. We use a physics-informed variant of the Fourier Neural Operator ($\pi$-FNO) to learn the weak solutions. We empirically quantify the generalization/out-of-sample error of the $\pi$-FNO solver as a function of input complexity, i.e., the distributions of initial and boundary conditions. Our testing results show that $\pi$-FNO generalizes well to unseen initial and boundary conditions. We find that the generalization error grows linearly with input complexity. Further, adding a physics-informed regularizer improved the prediction of discontinuities in the solution. We use the Lighthill-Witham-Richards (LWR) traffic flow model as a guiding example to illustrate the results