| 1. |
- Tabiri, S, et al.
(author)
-
- 2021
-
swepub:Mat__t
|
|
| 2. |
- Bravo, L, et al.
(author)
-
- 2021
-
swepub:Mat__t
|
|
| 3. |
- Glasbey, JC, et al.
(author)
-
- 2021
-
swepub:Mat__t
|
|
| 4. |
|
|
| 5. |
|
|
| 6. |
|
|
| 7. |
- Khatri, C, et al.
(author)
-
Outcomes after perioperative SARS-CoV-2 infection in patients with proximal femoral fractures: an international cohort study
- 2021
-
In: BMJ open. - : BMJ. - 2044-6055. ; 11:11, s. e050830-
-
Journal article (peer-reviewed)abstract
- Studies have demonstrated high rates of mortality in people with proximal femoral fracture and SARS-CoV-2, but there is limited published data on the factors that influence mortality for clinicians to make informed treatment decisions. This study aims to report the 30-day mortality associated with perioperative infection of patients undergoing surgery for proximal femoral fractures and to examine the factors that influence mortality in a multivariate analysis.SettingProspective, international, multicentre, observational cohort study.ParticipantsPatients undergoing any operation for a proximal femoral fracture from 1 February to 30 April 2020 and with perioperative SARS-CoV-2 infection (either 7 days prior or 30-day postoperative).Primary outcome30-day mortality. Multivariate modelling was performed to identify factors associated with 30-day mortality.ResultsThis study reports included 1063 patients from 174 hospitals in 19 countries. Overall 30-day mortality was 29.4% (313/1063). In an adjusted model, 30-day mortality was associated with male gender (OR 2.29, 95% CI 1.68 to 3.13, p<0.001), age >80 years (OR 1.60, 95% CI 1.1 to 2.31, p=0.013), preoperative diagnosis of dementia (OR 1.57, 95% CI 1.15 to 2.16, p=0.005), kidney disease (OR 1.73, 95% CI 1.18 to 2.55, p=0.005) and congestive heart failure (OR 1.62, 95% CI 1.06 to 2.48, p=0.025). Mortality at 30 days was lower in patients with a preoperative diagnosis of SARS-CoV-2 (OR 0.6, 95% CI 0.6 (0.42 to 0.85), p=0.004). There was no difference in mortality in patients with an increase to delay in surgery (p=0.220) or type of anaesthetic given (p=0.787).ConclusionsPatients undergoing surgery for a proximal femoral fracture with a perioperative infection of SARS-CoV-2 have a high rate of mortality. This study would support the need for providing these patients with individualised medical and anaesthetic care, including medical optimisation before theatre. Careful preoperative counselling is needed for those with a proximal femoral fracture and SARS-CoV-2, especially those in the highest risk groups.Trial registration numberNCT04323644
|
|
| 8. |
- Kattge, Jens, et al.
(author)
-
TRY plant trait database - enhanced coverage and open access
- 2020
-
In: Global Change Biology. - : Wiley-Blackwell. - 1354-1013 .- 1365-2486. ; 26:1, s. 119-188
-
Journal article (peer-reviewed)abstract
- Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives.
|
|
| 9. |
- Blackburn, Landen D., et al.
(author)
-
Dynamic machine learning-based optimization algorithm to improve boiler efficiency
- 2022
-
In: Journal of Process Control. - : Elsevier BV. - 0959-1524. ; 120, s. 129-149
-
Research review (peer-reviewed)abstract
- With decreasing computational costs, improvement in algorithms, and the aggregation of large industrial and commercial datasets, machine learning is becoming a ubiquitous tool for process and business innovations. Machine learning is still lacking applications in the field of dynamic optimization for real-time control. This work presents a novel framework for performing constrained dynamic optimization using a recurrent neural network model combined with a metaheuristic optimizer. The framework is designed to augment an existing control system and is purely data-driven, like most industrial Model Predictive Control applications. Several recurrent neural network models are compared as well as several metaheuristic optimizers. Hyperparameters and optimizer parameters are tuned with parameter sweeps, and the resulting values are reported. The best parameters for each optimizer and model combination are demonstrated in closed-loop control of a dynamic simulation, and several recommendations are made for generalizing this framework to other systems. Up to 0.953% improvement is realized over the non-optimized case for a simulated coal-fired boiler. While this is not a large improvement in percentage, the total economic impact is $991,000 per year, and this study builds a foundation for future machine learning with dynamic optimization.
|
|
| 10. |
- Fedina, Tatiana, et al.
(author)
-
A comparative study of water and gas atomized low alloy steel powders for additive manufacturing
- 2020
-
In: Additive Manufacturing. - : Elsevier. - 2214-8604 .- 2214-7810. ; 36
-
Journal article (peer-reviewed)abstract
- This work reports a study of the differences between laser processing of water and gas atomized low alloy steel powders with a focus on powder behavior and performance in additive manufacturing. Material packing densities were measured to establish a relationship between powder packing and track formation. The results showed that the track height when using water atomized powder was 15% lower than the value achieved for the gas atomized powder. High-speed imaging was utilized to observe the material behavior and analyze the powder particle movement under laser irradiation. It was found that water atomized powder has less particle entrainment due to its tendency towards mechanical interlocking. The occurrence of powder spattering and melt pool instabilities was also studied. More frequent spatter ejection is believed to be due to the higher amount of oxygen in the water atomized powder.
|
|
