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Simple sequence repeats (SSRs) have become one of the most popular molecular markers and are used in numerous fields, including conservation genetics, population genetic studies, and genetic mapping. Advances in next-generation sequencing technology and the growing amount of genomic data are driving the development of bioinformatics tools for SSR marker design. These tools work with different combinations of input data, which can be raw reads or assemblies, and with one or more input datasets. We present here a new strategy and implementation of a simple standalone pipeline that utilizes more than one assembly for the in silico design of PCR primers for microsatellite loci in more than one species. Primers are tested in silico to determine if they are polymorphic, eliminating the need to test time-consuming cross-species amplification in the laboratory. The end result is a set of markers that are in silico polymorphic in all analyzed species and have great potential for the identification of interspecies hybrids. The efficiency of the tool is demonstrated using two examples at different taxonomic levels and with different numbers of input assemblies to generate promising, high-quality SSR markers.

The complexity of corrosion mechanisms in harsh offshore environments poses safety and integrity challenges to oil and gas operations. Exploring the unstable interactions and complex mechanisms required an advanced probabilistic model. The current study presents the development of a probabilistic approach for a consequence-based assessment of subsea pipelines exposed to complex corrosion mechanisms. The Bayesian Probabilistic Network (BPN) is applied to structurally learn the propagation and interactions among under-deposit corrosion and microbial corrosion for the failure state prediction of the asset. A two-step consequences analysis is inferred from the failure state to establish the failure impact on the environment, lives, and economic losses. The essence is to understand how the interactions between the under-deposit and microbial corrosion mechanisms’ nodes influence the likely number of spills on the environment. The associated cost of failure consequences is predicted using the expected utility decision theory. The proposed approach is tested on a corroding subsea pipeline (API X60) to predict the degree of impact of the failed state on the asset’s likely consequences. At the worst degradation state, the failure consequence expected utility gives 1.0822×108 USD. The influence-based model provides a prognostic tool for proactive integrity management planning for subsea systems exposed to stochastic degradation in harsh offshore environments.

Offshore oil and gas production was conducted throughout the entire 20th century, but the industry's modern importance and vibrancy did not start until the early 1970s, when the North Sea became a major producer. Since then, the expansion of the offshore oil industry has been continuous and rapid. Pipelines, and more generally long tubular structures, are major oil and gas industry tools used in exploration, drilling, production, and transmission. Installing and operating tubular structures in deep waters places unique demands on them. Technical challenges within the field have spawned significant research and development efforts in a broad range of areas.Volume I addresses problems of buckling and collapse of long inelastic cylinders under various loads encountered in the offshore arena. Several of the solutions are also directly applicable to land pipelines. The approach of Mechanics of Offshore Pipelines is problem oriented. The background of each problem and scenario are first outlined and each discussion finishes with design recommendations. * New and classical problems addressed - investigated through a combination of experiments and analysis* Each chapter deals with a specific mechanical problem that is analyzed independently* The fundamental nature of the problems makes them also applicable to other fields, including tubular components in nuclear reactors and power plants, aerospace structures, automotive and civil engineering structures, naval vehicles and structures

The identification of leakage sources in underwater natural gas pipelines (UNGPs) remains a critical challenge due to complex environmental conditions. In this study, we propose a novel simulation–optimization method, integrating numerical bubble plume dynamics models with surrogate models to enable accurate leakage parameter inversion. First, a bubble plume underwater motion simulation model was developed based on the actual conditions of the study area to predict the future spatial and temporal variation characteristics of the bubble plumes in certain wave fields. Then, the simulation–optimization method was applied to determine the leakage velocity and offset distance of the underwater gas pipeline leakage source via inversion. To reduce the computational load of the optimization model by repeatedly invoking the simulation model, the Kriging method and a backpropagation (BP) neural network were used to build surrogate models for the numerical model. Finally, the optimized surrogate model was solved using the simulated annealing method, and the inverse identification results were obtained. The experimental results show that both methods can achieve a high inversion accuracy. The relative error of the Kriging model is no more than 12%, and the running time is 13 min. Meanwhile, based on the BP neural network surrogate model, the relative error of the BP neural network model is about 14%, and the running time is 2.5 min.

Underwater natural gas pipelines constitute critical infrastructure for energy transportation. Any damage or leakage in these pipelines poses serious security risks, directly threatening marine and lake ecosystems, and potentially causing operational issues and economic losses in the energy supply chain. However, current methods for detecting deterioration and regularly inspecting these submerged pipelines remain limited, as they rely heavily on divers, which is both costly and inefficient. Due to these challenges, the use of unmanned underwater vehicles (UUVs) becomes crucial in this field, offering a more effective and reliable solution for pipeline monitoring and maintenance. In this study, we conducted an underwater pipeline tracking and damage detection experiment using a remote-controlled unmanned underwater vehicle (UUV) with autonomous features. The primary objective of this research is to demonstrate that UUV systems provide a more cost-effective, efficient, and practical alternative to traditional, more expensive methods for inspecting submerged natural gas pipelines. The experimental method included vehicle (UUV) setup, pre-test calibration, pipeline tracking mechanism, 3D navigation control, damage detection, data processing, and analysis. During the tracking of the underwater pipeline, damages were identified, and their locations were determined. The navigation information of the underwater vehicle, including orientation in the x, y, and z axes (roll, pitch, yaw) from a gyroscope integrated with a magnetic compass, speed and position information in three axes from an accelerometer, and the distance to the water surface from a pressure sensor, was integrated into the vehicle. Pre-tests determined the necessary pulse width modulation values for the vehicle’s thrusters, enabling autonomous operation by providing these values as input to the thruster motors. In this study, 3D movement was achieved by activating the vehicle’s vertical thruster to maintain a specific depth and applying equal force to the right and left thrusters for forward movement, while differential force was used to induce deviation angles. In pool experiments, the unmanned underwater vehicle autonomously tracked the pipeline as intended, identifying damages on the pipeline using images captured by the vehicle’s camera. The images for damage assessment were processed using a convolutional neural network (CNN) algorithm, a deep learning method. The position of the damage relative to the vehicle was estimated from the pixel dimensions of the identified damage. The location of the damage relative to its starting point was obtained by combining these two positional pieces of information from the vehicle’s navigation system. The damages in the underwater pipeline were successfully detected using the CNN algorithm. The training accuracy and validation accuracy of the CNN algorithm in detecting underwater pipeline damages were 94.4% and 92.87%, respectively. The autonomous underwater vehicle also followed the designated underwater pipeline route with high precision. The experiments showed that the underwater vehicle followed the pipeline path with an error of 0.072 m on the x-axis and 0.037 m on the y-axis. Object recognition and the automation of the unmanned underwater vehicle were implemented in the Python environment.

[This corrects the article DOI: 10.1371/journal.pone.0323991.].

MicroRNAs (miRNAs) are small noncoding RNAs that play important post-transcriptional regulatory roles in animals and plants. Despite the importance of plant miRNAs, the inherent complexity of miRNA biogenesis in plants hampers the application of standard miRNA prediction tools, which are often optimized for animal sequences. Therefore, computational approaches to predict putative miRNAs (merely) from genomic sequences, regardless of their expression levels or tissue specificity, are of great interest. Here, we present AmiR-P.sup.3, a novel ab initio plant miRNA prediction pipeline that leverages the strengths of various utilities for its key computational steps. Users can readily adjust the prediction criteria based on the state-of-the-art biological knowledge of plant miRNA properties. The pipeline starts with finding the potential homologs of the known plant miRNAs in the input sequence(s) and ensures that they do not overlap with protein-coding regions. Then, by computing the secondary structure of the presumed RNA sequence based on the minimum free energy, a deep learning classification model is employed to predict potential pre-miRNA structures. Finally, a set of criteria is used to select the most likely miRNAs from the set of predicted miRNAs. We show that our method yields acceptable predictions in a variety of plant species. AmiR-P.sup.3 does not (necessarily) require sequencing reads and/or assembled reference genomes, enabling it to identify conserved and novel putative miRNAs from any genomic or transcriptomic sequence. Therefore, AmiR-P.sup.3 is suitable for miRNA prediction even in less-studied plants, as it does not require any prior knowledge of the miRNA repertoire of the organism. AmiR-P.sup.3 is provided as a docker container, which is a portable and self-contained software package that can be readily installed and run on any platform and is freely available for non-commercial use from: https://hub.docker.com/r/micrornaproject/amir-p3