No abstract

No abstract

The development of a new steam turbine generation for use in advanced coal fired power plants with prospective operating temperatures beyond 700 °C and a projected thermodynamic efficiency of about 55 % requires, amongst other innovations, the partial substitution of ferritic steels by wrought Ni‐base superalloys. Although Ni‐base alloys are already widely used in the aerospace industry, they are faced with demands regarding component size and operation temperature, which by far exceed current aero‐engine requirements. In this article, the potential of selected alloys for 700 °C steam turbine applications is discussed with respect to their manufacturability and mechanical performance. Hereby, the focus is on the steam turbine rotor, which probably is the most critical component. It is concluded that material solutions are available for operation conditions around 600 °C but not for temperatures of 700 °C and above. Based on these results, alloy development strategies are suggested in order to close this gap and two new alloys, DT 706 and DT 750, are introduced.

Objectives To systematically review studies using machine learning (ML) algorithms to predict whether patients undergoing total knee or total hip arthroplasty achieve an improvement as high or higher than the minimal clinically important differences (MCID) in patient reported outcome measures (PROMs) (classification problem). Methods Studies were eligible to be included in the review if they collected PROMs both pre- and postintervention, reported the method of MCID calculation and applied ML. ML was defined as a family of models which automatically learn from data when selecting features, identifying nonlinear relations or interactions. Predictive performance must have been assessed using common metrics. Studies were searched on MEDLINE, PubMed Central, Web of Science Core Collection, Google Scholar and Cochrane Library. Study selection and risk of bias assessment (ROB) was conducted by two independent researchers. Results 517 studies were eligible for title and abstract screening. After screening title and abstract, 18 studies qualified for full-text screening. Finally, six studies were included. The most commonly applied ML algorithms were random forest and gradient boosting. Overall, eleven different ML algorithms have been applied in all papers. All studies reported at least fair predictive performance, with two reporting excellent performance. Sample size varied widely across studies, with 587 to 34,110 individuals observed. PROMs also varied widely across studies, with sixteen applied to TKA and six applied to THA. There was no single PROM utilized commonly in all studies. All studies calculated MCIDs for PROMs based on anchor-based or distribution-based methods or referred to literature which did so. Five studies reported variable importance for their models. Two studies were at high risk of bias. Discussion No ML model was identified to perform best at the problem stated, nor can any PROM said to be best predictable. Reporting standards must be improved to reduce risk of bias and improve comparability to other studies.

For robotic fabrication of wooden structures, the simple, quick and tight joining of elements can be solved using swelling hardwood dowels. This topic has been the focus of the present study, and the set-recovery capacity of densified wood (dW) as dowel material was investigated. European beech was compressed in the radial direction at 103°C and 10% moisture content (MC) to a compression ratio of 40%. Multiple swelling and shrinkage cycles were applied to measure swelling behavior, swelling pressure development and combined swelling and creep under compressive loading. It has been demonstrated that dW shows increased swelling and more persisting swelling pressures than native wood (nW). The set-recovery prevents significant contact-stress relaxation over multiple cycles of MC change. Application as a structural joining element for robotic fabrication was studied by shear lap joint tests on round double-dovetail swelling dowels.