Beneficiation of ilmenite from lumar analog

The results reported were obtained on a meteoric eucrite sample called Millbillillie Sample no. 173. Optical microscopy studies of the sample showed it to consist of ilmenite, troilite, and transparent gangue. The transparent gangue consisted of feldspar (anorthite), pyroxenes, olivines, and opaques. Troilite was present in minor quantities. Screen assay analyses of the 30, 100, 200, and 400 US mesh screen fractions showed that minor concentration of titanium occurred in the 200 x 400 and -400 mesh screen fractions. Scanning electron microscopy (SEM) studies of the bulk sample showed the presence of a variety of ilmenite grains, ranging from 50 microns down to less than 1 micron without any evidence of liberation. Electron Diffraction Scans (EDS) confirmed the ratio of Fe to Ti in the ilmenite grains. Dry magnetic separation in a Frantz Isodynamic Separator was found to be effective only at sizes finer than 150 microns (100 US mesh) and more so at 200 mesh (74 microns). In each case, dedusting of the sample to remove -400 mesh (-0.037 microns) fines was required. Liberation size was determined to be 200 mesh and finer. The highest grade concentrate assaying 3.45 percent Ti was produced by magnetic separation of the -200 + 400 mesh screen fraction assaying 0.44 Ti (from a -30 mesh sample) at a current setting of 0.35 AMP. This concentrate contained 21.2 percent of the Ti values in the screen fraction with 2.72 weight percent of feed to test. The results can be projected to a sample stage ground to -200 mesh. Magnetic separation of the 200 + 400 mesh (-0.074 + 0.037 microns) should produce a concentrate accounting for 1.41 weight percent of the feed. This concentrate will analyze 3.45 percent Ti and contain 10.3 percent of the Ti values in the feed. By changing the Frantz Magnetic Separator settings, a lower grade concentrate analyzing 0.98 percent Ti can be produced at an increased recovery of 25.4 percent. The concentrate weight will be 11.7 percent of the feed. It must be emphasized that improved grades and recoveries can be obtained with the -400 mesh fines. However, beneficiation of these extremely fine materials is not possible in a practical process scheme

Ethical implications of AI in robotic surgical training: A Delphi consensus statement

CONTEXT: As the role of AI in healthcare continues to expand there is increasing awareness of the potential pitfalls of AI and the need for guidance to avoid them. OBJECTIVES: To provide ethical guidance on developing narrow AI applications for surgical training curricula. We define standardised approaches to developing AI driven applications in surgical training that address current recognised ethical implications of utilising AI on surgical data. We aim to describe an ethical approach based on the current evidence, understanding of AI and available technologies, by seeking consensus from an expert committee. EVIDENCE ACQUISITION: The project was carried out in 3 phases: (1) A steering group was formed to review the literature and summarize current evidence. (2) A larger expert panel convened and discussed the ethical implications of AI application based on the current evidence. A survey was created, with input from panel members. (3) Thirdly, panel-based consensus findings were determined using an online Delphi process to formulate guidance. 30 experts in AI implementation and/or training including clinicians, academics and industry contributed. The Delphi process underwent 3 rounds. Additions to the second and third-round surveys were formulated based on the answers and comments from previous rounds. Consensus opinion was defined as ≥ 80% agreement. EVIDENCE SYNTHESIS: There was 100% response from all 3 rounds. The resulting formulated guidance showed good internal consistency, with a Cronbach alpha of >0.8. There was 100% consensus that there is currently a lack of guidance on the utilisation of AI in the setting of robotic surgical training. Consensus was reached in multiple areas, including: 1. Data protection and privacy; 2. Reproducibility and transparency; 3. Predictive analytics; 4. Inherent biases; 5. Areas of training most likely to benefit from AI. CONCLUSIONS: Using the Delphi methodology, we achieved international consensus among experts to develop and reach content validation for guidance on ethical implications of AI in surgical training. Providing an ethical foundation for launching narrow AI applications in surgical training. This guidance will require further validation. PATIENT SUMMARY: As the role of AI in healthcare continues to expand there is increasing awareness of the potential pitfalls of AI and the need for guidance to avoid them.In this paper we provide guidance on ethical implications of AI in surgical training

Investor heterogeneity and the cross-section of U.K. investment trust performance

We use the upper and lower bounds derived by Ferson and Lin (2010) to examine the impact of investor heterogeneity on the performance of U.K. investment trusts relative to alternative linear factor models. We find using the upper bounds that investor heterogeneity has an important impact for nearly all investment trusts. The upper bounds are large in economic terms and significantly different from zero. We find no evidence of any trusts where all investors agree on the sign of performance beyond what we expect by chance. Using the lower bound, we find that trusts with a larger disagreement about trust performance have a weaker relation between the trust premium and past Net Asset Value (NAV) performance

SAGES consensus recommendations on an annotation framework for surgical video

Background: The growing interest in analysis of surgical video through machine learning has led to increased research efforts; however, common methods of annotating video data are lacking. There is a need to establish recommendations on the annotation of surgical video data to enable assessment of algorithms and multi-institutional collaboration. Methods: Four working groups were formed from a pool of participants that included clinicians, engineers, and data scientists. The working groups were focused on four themes: (1) temporal models, (2) actions and tasks, (3) tissue characteristics and general anatomy, and (4) software and data structure. A modified Delphi process was utilized to create a consensus survey based on suggested recommendations from each of the working groups. Results: After three Delphi rounds, consensus was reached on recommendations for annotation within each of these domains. A hierarchy for annotation of temporal events in surgery was established. Conclusions: While additional work remains to achieve accepted standards for video annotation in surgery, the consensus recommendations on a general framework for annotation presented here lay the foundation for standardization. This type of framework is critical to enabling diverse datasets, performance benchmarks, and collaboration

Ethical implications of AI in robotic surgical training: A Delphi consensus statement

Context: As the role of AI in healthcare continues to expand there is increasing awareness of the potential pitfalls of AI and the need for guidance to avoid them. Objectives: To provide ethical guidance on developing narrow AI applications for surgical training curricula. We define standardised approaches to developing AI driven applications in surgical training that address current recognised ethical implications of utilising AI on surgical data. We aim to describe an ethical approach based on the current evidence, understanding of AI and available technologies, by seeking consensus from an expert committee. Evidence acquisition: The project was carried out in 3 phases: (1) A steering group was formed to review the literature and summarize current evidence. (2) A larger expert panel convened and discussed the ethical implications of AI application based on the current evidence. A survey was created, with input from panel members. (3) Thirdly, panel-based consensus findings were determined using an online Delphi process to formulate guidance. 30 experts in AI implementation and/or training including clinicians, academics and industry contributed. The Delphi process underwent 3 rounds. Additions to the second and third-round surveys were formulated based on the answers and comments from previous rounds. Consensus opinion was defined as ≥ 80% agreement. Evidence synthesis: There was 100% response from all 3 rounds. The resulting formulated guidance showed good internal consistency, with a Cronbach alpha of >0.8. There was 100% consensus that there is currently a lack of guidance on the utilisation of AI in the setting of robotic surgical training. Consensus was reached in multiple areas, including: 1. Data protection and privacy; 2. Reproducibility and transparency; 3. Predictive analytics; 4. Inherent biases; 5. Areas of training most likely to benefit from AI. Conclusions: Using the Delphi methodology, we achieved international consensus among experts to develop and reach content validation for guidance on ethical implications of AI in surgical training. Providing an ethical foundation for launching narrow AI applications in surgical training. This guidance will require further validation. Patient summary: As the role of AI in healthcare continues to expand there is increasing awareness of the potential pitfalls of AI and the need for guidance to avoid them.In this paper we provide guidance on ethical implications of AI in surgical training