Development of a Versatile Laser Ultrasonic System and Application to On-Line Measurement for Process Control of Wall Thickness and Eccentrictiy of Steel Seamless Mechanical Tubing

Researchers at the Timken Company conceived a project to develop an on-line instrument for wall thickness measurement of steel seamless mechanical tubing based on laser ultrasonic technology. The instrument, which has been installed and tested at a piercing mill, provides data on tube eccentricity and concentricity. Such measurements permit fine-tuning of manufacturing processes to eliminate excess material in the tube wall and therefore provide a more precisely dimensioned product for their customers. The resulting process energy savings are substantial, as is lowered environmental burden. The expected savings are $85.8 million per year in seamless mechanical tube piercing alone. Applied across the industry, this measurement has a potential of reducing energy consumption by 6 x 10{sup 12} BTU per year, greenhouse gas emissions by 0.3 million metric tons carbon equivalent per year, and toxic waste by 0.255 million pounds per year. The principal technical contributors to the project were the Timken Company, Industrial Materials Institute (IMI, a contractor to Timken), and Oak Ridge National Laboratory (ORNL). Timken provided mill access as well as process and metallurgical understanding. Timken researchers had previously developed fundamental ultrasonic analysis methods on which this project is based. IMI developed and fabricated the laser ultrasonic generation and receiver systems. ORNL developed Bayesian and wavelet based real-time signal processing, spread-spectrum wireless communication, and explored feature extraction and pattern recognition methods. The resulting instrument has successfully measured production tubes at one of Timken's piercing mills. This report concentrates on ORNL's contribution through the CRADA mechanism. The three components of ORNL's contribution were met with mixed success. The real-time signal-processing task accomplished its goal of improvement in detecting time of flight information with a minimum of false data. The signal processing algorithm development resulted in a combination of processing steps that can be set to generate no spoofs from noise, while simultaneously missing fewer than 10% of good trials. The algorithm leads to a 95% probability that the estimate of time of flight is good to within 4 time bins or fewer for laser excitations above 30 mJ for the first two echoes of the signal. Receiver Operating Characteristic (ROC) curves for the algorithm indicate that the algorithm is very robust against errors for excitations above at 35 mJ and above, tolerable at 30 mJ and unacceptable below 30 mJ

Genomic analyses identify hundreds of variants associated with age at menarche and support a role for puberty timing in cancer risk

The timing of puberty is a highly polygenic childhood trait that is epidemiologically associated with various adult diseases. Using 1000 Genomes Project-imputed genotype data in up to similar to 370,000 women, we identify 389 independent signals (P <5 x 10(-8)) for age at menarche, a milestone in female pubertal development. In Icelandic data, these signals explain similar to 7.4% of the population variance in age at menarche, corresponding to similar to 25% of the estimated heritability. We implicate similar to 250 genes via coding variation or associated expression, demonstrating significant enrichment in neural tissues. Rare variants near the imprinted genes MKRN3 and DLK1 were identified, exhibiting large effects when paternally inherited. Mendelian randomization analyses suggest causal inverse associations, independent of body mass index (BMI), between puberty timing and risks for breast and endometrial cancers in women and prostate cancer in men. In aggregate, our findings highlight the complexity of the genetic regulation of puberty timing and support causal links with cancer susceptibility

Genomic analyses identify hundreds of variants associated with age at menarche and support a role for puberty timing in cancer risk

The timing of puberty is a highly polygenic childhood trait that is epidemiologically associated with various adult diseases. Using 1000 Genomes Project–imputed genotype data in up to ~370,000 women, we identify 389 independent signals (P < 5 × 10$^{−8}$) for age at menarche, a milestone in female pubertal development. In Icelandic data, these signals explain ~7.4% of the population variance in age at menarche, corresponding to ~25% of the estimated heritability. We implicate ~250 genes via coding variation or associated expression, demonstrating significant enrichment in neural tissues. Rare variants near the imprinted genes MKRN3 and DLK1 were identified, exhibiting large effects when paternally inherited. Mendelian randomization analyses suggest causal inverse associations, independent of body mass index (BMI), between puberty timing and risks for breast and endometrial cancers in women and prostate cancer in men. In aggregate, our findings highlight the complexity of the genetic regulation of puberty timing and support causal links with cancer susceptibility

Measured Performance of a Dealer-Installed Residential Ground-Source Heat Pump System

Contract No. 0SX82-00083DSS No. 31155-2-4435prepared for National Research Council Canadapr\ue9par\ue9 pour le Conseil national de recherches CanadaPeer reviewed: NoNRC publication: Ye

On the adequacy of static analysis warnings with respect to code smell prediction

Code smells are poor implementation choices that developers apply while evolving source code and that affect program maintainability. Multiple automated code smell detectors have been proposed: while most of them relied on heuristics applied over software metrics, a recent trend concerns the definition of machine learning techniques. However, machine learning-based code smell detectors still suffer from low accuracy: one of the causes is the lack of adequate features to feed machine learners. In this paper, we face this issue by investigating the role of static analysis warnings generated by three state-of-the-art tools to be used as features of machine learning models for the detection of seven code smell types. We conduct a three-step study in which we (1) verify the relation between static analysis warnings and code smells and the potential predictive power of these warnings; (2) build code smell prediction models exploiting and combining the most relevant features coming from the first analysis; (3) compare and combine the performance of the best code smell prediction model with the one achieved by a state of the art approach. The results reveal the low performance of the models exploiting static analysis warnings alone, while we observe significant improvements when combining the warnings with additional code metrics. Nonetheless, we still find that the best model does not perform better than a random model, hence leaving open the challenges related to the definition of ad-hoc features for code smell prediction

Preliminary study on combined pharmacological and laser treatment in optic pit serous maculopathy after literature review.

The pit of the optic nerve head (ON) is a rare congenital defect that sometimes presents itself with a maculopathy of various neuroretinal layers for unknown reason. This study was focused, before and after pharmacological and parasurgical treatment, on the structural and functional visual assessment in a patient with optic pit maculopathy (OPM). In order to achieve this the latest generation of hi-tech diagnostic tests were used, such as Spectral-Domain Optical Coherence Tomography (SD-OCT), Visual Evoked Potentials (VEP), full-field Electroretinography (ERG), multifocal ERG (mfERG), Microperimetry (MP-1), Standard Automated Perimetry (SAP), Fluorescein Angiography (FA) and Indocyanine Green Angiography (ICG). The research was conducted through a review of past and recent literature