Abstract: Various processes for the addition or build-up of material such as base metals or alloys onto tool joints and/or threads of drill pipe typically used in, but not limited to, the gas and oil industry are disclosed. In one example, a spiral delivery of material is described, as well as, a machine that delivers the material onto the tool joints and/or threads of drill pipe. Variables can be temperature used during pre-heat, application, inter-pass and/or post process, electrical current used (AC/DC), volts, amps, wire type and size, cooling process, width and depth of bead applied, rotation, and travel speed.

Abstract: A machine learning system for ranking a collection of filtered propensity to failure metrics of like components within an electrical grid that includes a raw data assembly to provide raw data representative of the like components within the electrical grid; (b) a data processor, operatively coupled to the raw data assembly, to convert the raw data to more uniform data via one or more data processing techniques; (c) a database, operatively coupled to the data processor, to store the more uniform data; (d) a machine learning engine, operatively coupled to the database, to provide a collection of propensity to failure metrics for the like components; (e) an evaluation engine, operatively coupled to the machine learning engine, to detect and remove non-complying metrics from the collection of propensity to failure metrics and to provide the collection of filtered propensity to failure metrics; and (f) a decision support application, operatively coupled to the evaluation engine, configured to display a ranking of

Abstract: A machine learning system for ranking a collection of filtered propensity to failure metrics of like components within an electrical grid that includes a raw data assembly to provide raw data representative of the like components within the electrical grid; (b) a data processor, operatively coupled to the raw data assembly, to convert the raw data to more uniform data via one or more data processing techniques; (c) a database, operatively coupled to the data processor, to store the more uniform data; (d) a machine learning engine, operatively coupled to the database, to provide a collection of propensity to failure metrics for the like components; (e) an evaluation engine, operatively coupled to the machine learning engine, to detect and remove non-complying metrics from the collection of propensity to failure metrics and to provide the collection of filtered propensity to failure metrics; and (f) a decision support application, operatively coupled to the evaluation engine, configured to display a ranking of

Abstract: The present invention relates to diagnosing the risk of developing a need for dialysis and/or predicting a need for dialysis, particularly in patients suffering from renal disorders, comprising the steps of measuring the level of a BNP-type peptide or a variant thereof in a sample from the patient, diagnosing the risk by comparing the measured level of the BNP-type peptide or a variant thereof to at least one reference level. The BNP-type peptide may, for example, be brain natriuretic peptide (BNP), proBNP, NT-proBNP, a variant of BNP, a variant of proBNP, or a variant of NT-proBNP.

Abstract: The present invention relates to the diagnosis of the risk of experiencing a cardiovascular complication in the context of treatment with erythropoiesis stimulating agents (ESA's) such as erythropoietin and derivatives thereof particularly in the context of anemia. More particularly, the invention provides a method for diagnosing the risk of a patient of experiencing a cardiovascular complication as a consequence of future medication with an erythropoiesis stimulating agent (ESA), comprising the steps of (a) measuring the level of a BNP-type peptide in a sample of the patient, (b) diagnosing said risk by comparing the measured level of the BNP-type peptide to at least one reference level. The BNP-type peptide may for example be brain natriuretic peptide (BNP) or the N-terminal fragment of BNP, NT-proBNP. The cardiovascular complication may include complications, such as stroke, transient cerebral ischemic attack, acute-coronary syndrome, myocardial infarction, congestive heart failure etc.

Abstract: An apparatus for testing the efficacy of a sterilizing environment on a test indicator, comprising a test chamber, a loading chamber adjacent to the test chamber and an opening connecting the test chamber to the loading chamber. A movable gate mechanism has a first position closing the opening and isolating the test chamber from the loading chamber, and a second position wherein the opening connects the test chamber to the loading chamber. A system is connected to the inlet port and the outlet port of the test chamber for supplying a sterilant vapor to the test chamber to establish a sterilizing environment in the test chamber. An indicator holder is operable to move a test indicator from the loading chamber into and out of the test chamber when the gate mechanism is in the second position.