501 research outputs found
Data for: Supergene manganese ore records 75 Myr-long Campanian to Pleistocene geodynamic evolution and weathering history of the Central African Great Lakes Region - tectonics drives, climate helps
No full textThe south-eastern part of the Democratic Republic of the Congo locally hosts Proterozoic manganese deposits. The deposits of Kisenge-Kamata are the most significant, but manganese ores are also known to occur at Kasekelesa (former Katanga Province) and Mwene-Ditu (former Kasai Province). For the present study, cryptomelane-rich samples from these two localities were dated, using the 39Ar-40Ar method in step-heating using a CO2 laser probe. Obtained ages are within a range of about 80 Myr to 2 Myr. Cryptomelane formation took place at c. 76.4 Ma, c. 59.6 Ma, c. 45 Ma, c. 35 Ma, c. 23.8 Ma, c. 15.4 Ma, and c. 13.3 Ma at Kasekelesa, and it occurred at c. 35 Ma, c. 22.4 Ma, c. 15 Ma, c. 5.5-7.2 Ma, c. 3.6 Ma, and c. 2.1-2.3 Ma at Mwene-Ditu. The Campanian age (c. 76.4 Ma) recorded at Kasekelesa is the oldest 39Ar-40Ar age that has up to now been recorded for Mn ores from Africa. It documents the formation of oxidized ore along a Campanian or older erosion surface, which could be part of the ‘African Erosion Surface’. The complete age record suggests that continent-wide tectonics accounts for most of the recognized supergene ore formation episodes, controlled by vertical lithospheric movements that are ultimately responsible for alternating stages of landscape stability and erosion. Tectonics is thus regarded as the first-order control for secondary ore formation in Central Africa, over the last 80 Myr. Climate is a second-order control, because sufficient water supply is needed for supergene enrichment, whereby climatic conditions are recognized to have been favourable during some relatively cold Late Mesozoic and Paleogene periods, as well as during some humid and warm Neogene stages
Special Issue about Competing Risks and Multi-State Models
Get PDFThere is a clear growing interest, at least in the statistical literature, in competing risks and multi-state models. With the rising interest in competing risks and multi-state models a number of software packages have been developed for the analysis of such models. The present special issue of the Journal of Statistical Software introduces a selection of R packages devoted to competing risks and multi-state models. This introduction to the special issue contains some background and highlights the contents of the contributions.
mstate: An R Package for the Analysis of Competing Risks and Multi-State Models
Get PDFMulti-state models are a very useful tool to answer a wide range of questions in survival analysis that cannot, or only in a more complicated way, be answered by classical models. They are suitable for both biomedical and other applications in which time-to-event variables are analyzed. However, they are still not frequently applied. So far, an important reason for this has been the lack of available software. To overcome this problem, we have developed the mstate package in R for the analysis of multi-state models. The package covers all steps of the analysis of multi-state models, from model building and data preparation to estimation and graphical representation of the results. It can be applied to non- and semi-parametric (Cox) models. The package is also suitable for competing risks models, as they are a special category of multi-state models. This article offers guidelines for the actual use of the software by means of an elaborate multi-state analysis of data describing post-transplant events of patients with blood cancer. The data have been provided by the EBMT (the European Group for Blood and Marrow Transplantation). Special attention will be paid to the modeling of different covariate effects (the same for all transitions or transition-specific) and different baseline hazard assumptions (different for all transitions or equal for some).
Decreasing impact of relapse on death rate after allogeneic haematopoietic stem cell transplant in patients with chronic myeloid leukaemia: a multi-state modelling study
No full textDecreasing Impact of Relapse on Death Rate after Allogeneic Hematopoietic Stem Cell Transplant in Patients with Chronic Myeloid Leukemia: a multi-state modelling study.
No full textNeogene phytostratigraphy and palaeoenvironments of Entre-Sambre-et-Meuse and Condroz areas (Belgium). Palaeoclimatic shift from humid-subtropical to cold-temperate conditions
No full textAvailable palaeobotanical data from significant karstic depressions of Entre-Sambre-et-Meuse and Condroz areas (Belgium) are reviewed. Drawing up of about forty stratigraphically significant taxa results in a phytostratigraphy of the continental Neogene supporting correlations with surrounding areas. Meorover, palaeoenvironmental and palaeoclimatic evolution of the studied areas are clarified while the phytostratigraphic framework evidences two major steps in the karst formatio
Distributed Control Design of District Heating Networks
No full textIn The Netherlands, the current heat energy system accounts for 44% of the primary energy usage and relies almost entirely on fossil fuels such as natural gas. To meet the Paris Climate Agreement goals, 4th Generation District Heating (4GDH) networks are expected as sustainable heat energy system solution. The concept relies on optimally matching the heat energy supply of sources such as waste heat, combined heat and power (CHP) plants and geothermal energy, with the demand of consumers such as households or greenhouses, whilst using the flexibility of buffers such as aquifer thermal energy storage (ATES) systems. In this thesis, a cooperative multi-agent system (MAS) hierarchical model predictive control (HMPC) implementation is presented as smart controller for 4GDH networks, and as alternative approach to improving TNO’s HeatMatcher (HM) algorithm with the proposed algorithm of PowerMatcher (PM) that relies on locational marginal pricing (LMP). The model predictive control (MPC) approach is chosen mainly due to the advantage that it optimizes over a prediction horizon or time span, instead of a single time step. This allows to take into account heat energy demand predictions, time-based constraints, and the inherent dynamic characteristics of 4GDH systems such as buffer flexibility and the variable time delay present in the heat energy exchange. The centralized model predictive control (CMPC) control problem is formulated as a deterministic, MAS, mixed-integer quadratic programming (MIQP) optimization problem and is subsequently distributed based on the Optimal Exchange Problem formulation using the alternating direction method of multipliers (ADMM). Hybrid system modelling theory is applied to model the agents’ subsystems and a simplified heat energy exchange model with constant time delay is assumed. The latter was chosen as decoupled thermal and hydraulic equations proved to be non-linear in the valve positions and mass flow, iterative due to the friction factor and the Reynolds number, and dependent on a variable spatial sampling to accurately track the thermal propagation through the network. The CMPC and HMPC algorithms are applied on an academic initial design case study to test desired controller behaviour under perfect heat demand prediction, and on a more realistic case study of the WarmCO2 heat grid involving 5 greenhouses with non-perfect heat demand predictions during a summer and winter scenario. The initial case study confirms that both algorithms perform as desired with the exception of a small shortcoming of the hybrid modelling, and that they are similar in their optimal solutions as expected. The same holds for the WarmCO2 case study. However, it also showcases that the deterministic optimization can become infeasible due to the time delay modelling and non-perfect heat demand predictions, and that therefore a stochastic optimization approach is preferred. Furthermore, good quality local optimal solutions of the NP-hard problem could be found within a relatively short computing time limit, using the heuristic methods of the Gurobi solver. And lastly, the importance of developing a non-cooperative MPC algorithm to accurately represent the individual optimization goals of different stakeholders.The work in this thesis was supported by the MCS department of TNO. Their cooperation is hereby gratefully acknowledged
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