Investigation of erosion-corrosion failure of API X52 carbon steel pipeline.

A failure analysis of API X52 steel pipeline was conducted. The investigation included complete material characterizations using tensile and hardness testing, optical microscope, SEM, and EDS. The main failure occurred in the downstream pipe located near the welded joint at the elbow outlet instead of elbow which was interesting. The main mechanism of failure was found to be erosion-corrosion mechanism that caused breakdown of the protective FeCO3 film, thinning of the downstream pipe, and finally failure. It is believed that the erosion-corrosion was induced by sand impingement due to turbulent flow that was promoted by sudden change in the flow cross section between the elbow inlet and upstream pipe and poor welding quality of joint at the elbow outlet.

Metallurgical investigation of early failure of heater pipes in a gas complex.

An early corrosion failure of the piping system of a gas heater was reported by a gas complex company. Local corrosion rates of 0.90 and 0.66 mm/year were observed in the heater piping system. An investigation including visual examination, macrostructure, microstructure (SEM, EDS and XRD), thickness gauging, chemical analysis, and mechanical testing, was performed. The results showed that corrosion damage occurred on the external surface of the pipes. Corrosion occurred at the cold sides of the pipes and elbows. The corrosion pattern is broad and shallow pitting. Some elbows showed an early stage of carbide spheroidization and pearlite decomposition. The EDS microanalysis revealed that the level of sulfur, chlorine, and nitrogen was substantially high in the rust samples. The XRD of the corrosion products showed that the main oxyhydroxide was Akaganeite. The analysis of results showed that the flue gas dew point corrosion was the mechanism of damage, and the root cause was the operation of the heater at low temperatures and the frequent outings of service, combined with evident material drawbacks including low levels of Si, Cu, Ni, Cr, and Mo. These elements should be at their maximum allowable limits of the SA 105 and SA 106 to improve the corrosion resistance of the steel piping components.

Effects of microstructure and alloy composition on hydroxyapatite precipitation on alkaline treated α/ß titanium alloys.

The current work aims at exploring the effects of the microstructure and alloy composition on enhancing the bone osseointegration in Ti-6Al-4V (Ti64) and Ti-6Al-7Nb (Ti67) alloys. This was revealed by investigating the alloy susceptibility to grow hydroxyapatite (HA) on their surfaces after immersion in simulated body fluid (SBF). The specimens were produced by two methods: forging and casting in order to study the influence of the microstructure on the precipitation process. The surface conditions investigated were the polished, alkaline and the hydrothermally treated. It was found that precipitation on both of Ti64 and Ti67 occurs after about 4 weeks and considerably dissolve back with further immersion. Precipitation process is enhanced at some pH levels lower than the neutral level. Forged Ti67 has less reactivity with Hank solution than Ti64 specimens; the reverse is true for cast specimens. In case of the alkaline treated specimens, precipitations on cast specimens were denser than on the forged ones. For the hydrothermally treated specimens, high amounts of Ca and P were observed on cast Ti67 indicating that hydrothermal treatment is considered the best surface modification treatment for alloy Ti67.

MicroRNA categorization using sequence motifs and k-mers.

BACKGROUND: Post-transcriptional gene dysregulation can be a hallmark of diseases like cancer and microRNAs (miRNAs) play a key role in the modulation of translation efficiency. Known pre-miRNAs are listed in miRBase, and they have been discovered in a variety of organisms ranging from viruses and microbes to eukaryotic organisms. The computational detection of pre-miRNAs is of great interest, and such approaches usually employ machine learning to discriminate between miRNAs and other sequences. Many features have been proposed describing pre-miRNAs, and we have previously introduced the use of sequence motifs and k-mers as useful ones. There have been reports of xeno-miRNAs detected via next generation sequencing. However, they may be contaminations and to aid that important decision-making process, we aimed to establish a means to differentiate pre-miRNAs from different species. RESULTS: To achieve distinction into species, we used one species' pre-miRNAs as the positive and another species' pre-miRNAs as the negative training and test data for the establishment of machine learned models based on sequence motifs and k-mers as features. This approach resulted in higher accuracy values between distantly related species while species with closer relation produced lower accuracy values. CONCLUSIONS: We were able to differentiate among species with increasing success when the evolutionary distance increases. This conclusion is supported by previous reports of fast evolutionary changes in miRNAs since even in relatively closely related species a fairly good discrimination was possible.

The impact of feature selection on one and two-class classification performance for plant microRNAs.

MicroRNAs (miRNAs) are short nucleotide sequences that form a typical hairpin structure which is recognized by a complex enzyme machinery. It ultimately leads to the incorporation of 18-24 nt long mature miRNAs into RISC where they act as recognition keys to aid in regulation of target mRNAs. It is involved to determine miRNAs experimentally and, therefore, machine learning is used to complement such endeavors. The success of machine learning mostly depends on proper input data and appropriate features for parameterization of the data. Although, in general, two-class classification (TCC) is used in the field; because negative examples are hard to come by, one-class classification (OCC) has been tried for pre-miRNA detection. Since both positive and negative examples are currently somewhat limited, feature selection can prove to be vital for furthering the field of pre-miRNA detection. In this study, we compare the performance of OCC and TCC using eight feature selection methods and seven different plant species providing positive pre-miRNA examples. Feature selection was very successful for OCC where the best feature selection method achieved an average accuracy of 95.6%, thereby being ∼29% better than the worst method which achieved 66.9% accuracy. While the performance is comparable to TCC, which performs up to 3% better than OCC, TCC is much less affected by feature selection and its largest performance gap is ∼13% which only occurs for two of the feature selection methodologies. We conclude that feature selection is crucially important for OCC and that it can perform on par with TCC given the proper set of features.

Feature Selection Has a Large Impact on One-Class Classification Accuracy for MicroRNAs in Plants.

MicroRNAs (miRNAs) are short RNA sequences involved in posttranscriptional gene regulation. Their experimental analysis is complicated and, therefore, needs to be supplemented with computational miRNA detection. Currently computational miRNA detection is mainly performed using machine learning and in particular two-class classification. For machine learning, the miRNAs need to be parametrized and more than 700 features have been described. Positive training examples for machine learning are readily available, but negative data is hard to come by. Therefore, it seems prerogative to use one-class classification instead of two-class classification. Previously, we were able to almost reach two-class classification accuracy using one-class classifiers. In this work, we employ feature selection procedures in conjunction with one-class classification and show that there is up to 36% difference in accuracy among these feature selection methods. The best feature set allowed the training of a one-class classifier which achieved an average accuracy of ~95.6% thereby outperforming previous two-class-based plant miRNA detection approaches by about 0.5%. We believe that this can be improved upon in the future by rigorous filtering of the positive training examples and by improving current feature clustering algorithms to better target pre-miRNA feature selection.

Ensemble Clustering Classification compete SVM and One-Class classifiers applied on plant microRNAs Data.

The performance of many learning and data mining algorithms depends critically on suitable metrics to assess efficiency over the input space. Learning a suitable metric from examples may, therefore, be the key to successful application of these algorithms. We have demonstrated that the k-nearest neighbor (kNN) classification can be significantly improved by learning a distance metric from labeled examples. The clustering ensemble is used to define the distance between points in respect to how they co-cluster. This distance is then used within the framework of the kNN algorithm to define a classifier named ensemble clustering kNN classifier (EC-kNN). In many instances in our experiments we achieved highest accuracy while SVM failed to perform as well. In this study, we compare the performance of a two-class classifier using EC-kNN with different one-class and two-class classifiers. The comparison was applied to seven different plant microRNA species considering eight feature selection methods. In this study, the averaged results show that ECkNN outperforms all other methods employed here and previously published results for the same data. In conclusion, this study shows that the chosen classifier shows high performance when the distance metric is carefully chosen.

Surface modification of investment cast-316L implants: microstructure effects.

Artificial femur stem of 316L stainless steel was fabricated by investment casting using vacuum induction melting. Different surface treatments: mechanical polishing, thermal oxidation and immersion in alkaline solution were applied. Thicker hydroxyapatite (HAP) layer was formed in the furnace-oxidized samples as compared to the mechanically polished ones. The alkaline treatment enhanced the precipitation of HAP on the samples. It was also observed that the HAP precipitation responded differently to the different phases of the microstructure. The austenite phase was observed to have more homogeneous and smoother layer of HAP. In addition, the growth of HAP was sometimes favored on the austenite phase rather than on ferrite phase.