Batch Tests for Optimisation of Solvent Composition and Process Flexibility of the CHALMEX FS-13 Process

ABSTRACT

Studies have been performed with the purpose of determining the optimal solvent composition of a Chalmers grouped actinide extraction (CHALMEX) solvent for the selective co-extraction of transuranic elements in a novel Grouped ActiNide EXtraction (GANEX) process. The solvent is composed of 6,6’-bis(5,5,8,8-tetramethyl-5,6,7,8-tetrahydro-benzo-[1,2,4]-triazin-3-yl)-[2,2’]-bipyridine (CyMe₄-BTBP) and tri-n-butyl phosphate (TBP) in phenyl trifluoromethyl sulfone (FS-13). The performance of the system has been shown to significantly depend on the ratios of the two extracting agents and the diluent to one another. Furthermore, the performance of the determined optimal solvent (10 mM CyMe₄-BTBP in 30% v/v TBP and 70% v/v FS-13) on various simulated PUREX raffinate solutions was tested. It was found that the solvent extracts all transuranic elements with high efficiency and good selectivity with regard to most other elements (fission products/activation products) present in the simulated PUREX raffinate solutions. Moreover, the solvent was found to extract a significant amount of acid. Palladium, silver, and cadmium were co-extracted along with the TRU-radionuclides, which has also been observed in other similar CHALMEX systems. The extraction of plutonium and uranium was preserved for all tested simulated PUREX raffinate solutions compared to experiments using trace amounts.     

In Vivo Silencing of Genes Coding for dTip60 Chromatin Remodeling Complex Subunits Affects Polytene Chromosome Organization and Proper Development in Drosophila melanogaster

Chromatin organization is developmentally regulated by epigenetic changes mediated by histone-modifying enzymes and chromatin remodeling complexes. In Drosophila melanogaster, the Tip60 chromatin remodeling complex (dTip60) play roles in chromatin regulation, which are shared by evolutionarily-related complexes identified in animal and plants. Recently, it was found that most subunits previously assigned to the dTip60 complex are shared by two related complexes, DOM-A.C and DOM-B.C, defined by DOM-A and DOM-B isoforms, respectively. In this work, we combined classical genetics, cell biology, and reverse genetics approaches to further investigate the biological roles played during Drosophila melanogaster development by a number of subunits originally assigned to the dTip60 complex.     

Effect of damping model on pre-yielding earthquake response of structures

The present paper examines differences between responses of single-degree-of-freedom (SDOF) systems with viscous and hysteretic damping to earthquake ground motions. Only responses within yield limits are considered. The structural behavior of SDOF systems with hysteretic damping is modeled by a generalized elasto-slip model of the Masing type. The model leads to a force–displacement relationship similar to that proposed by Ramberg and Osgood. The responses of the two systems are compared for a number of earthquake records. To achieve consistent comparison, parameters of the linear system with viscous damping are determined by the secant stiffness method. In many cases significant differences in the peak displacement responses and energy values have been observed between the systems. This indicates that the prediction of the response of structures to earthquake ground motions before yielding based on linear models with viscous damping may not be as accurate as used to be believed. Tentative estimates of error associated with such predictions are presented.     

The effect of sodium polymethacrylate on the rheology of the positive paste and performance of the lead-acid battery

Due to its rheological properties, positive lead-acid battery paste can be difficult to spread on lead current collectors accurately and efficiently under industry machinery and setting. Sodium polymethacrylate dispersant was studied as an effective positive paste additive that could lower the yield stress of the paste without affecting paste density and battery performance. Under a four-blade vane rheometer setup, stress growth and oscillatory amplitude strain sweep experiments evaluated the rheological properties of positive paste with the addition of varying amounts of sodium polymethacrylate. Further, the electrochemical effects of sodium polymethacrylate were also evaluated in 2V batteries by testing positive active material utilization and cycle life.     

Fault tolerant network design inspired by Physarum polycephalum

Physarum polycephalum, a true slime mould, is a primitive, unicellular organism that creates networks to transport nutrients while foraging. The design of these natural networks proved to be advanced, e.g. the slime mould was able to find the shortest path through a maze. The underlying principles of this design have been mathematically modelled in literature. As in real life the slime mould can design fault tolerant networks, its principles can be applied to the design of man-made networks. In this paper, an existing model and algorithm are adapted and extended with stimulation and migration mechanisms which encourage formation of alternative paths, optimize edge positioning and allow for automated design. The extended model can then be used to better design fault tolerant networks. The extended algorithm is applied to several national and international network configurations. Results show that the extensions allow the model to capture the fault tolerance requirements more accurately. The resulting extended algorithm overcomes weaknesses in geometric graph design and can be used to design fault tolerant networks such as telecommunication networks with varying fault tolerance requirements.     

Multiagent Reinforcement Learning:Rollout and Policy Iteration

We discuss the solution of complex multistage decision problems using methods that are based on the idea of policy iteration (PI), i.e., start from some base policy and generate an improved policy. Rollout is the simplest method of this type, where just one improved policy is generated. We can view PI as repeated application of rollout, where the rollout policy at each iteration serves as the base policy for the next iteration. In contrast with PI, rollout has a robustness property: it can be applied on-line and is suitable for on-line replanning. Moreover, rollout can use as base policy one of the policies produced by PI, thereby improving on that policy. This is the type of scheme underlying the prominently successful AlphaZero chess program.In this paper we focus on rollout and PI-like methods for problems where the control consists of multiple components each selected (conceptually) by a separate agent. This is the class of multiagent problems where the agents have a shared objective function, and a shared and perfect state information. Based on a problem reformulation that trades off control space complexity with state space complexity, we develop an approach, whereby at every stage, the agents sequentially (one-at-a-time) execute a local rollout algorithm that uses a base policy, together with some coordinating information from the other agents. The amount of total computation required at every stage grows linearly with the number of agents. By contrast, in the standard rollout algorithm, the amount of total computation grows exponentially with the number of agents. Despite the dramatic reduction in required computation, we show that our multiagent rollout algorithm has the fundamental cost improvement property of standard rollout: it guarantees an improved performance relative to the base policy. We also discuss autonomous multiagent rollout schemes that allow the agents to make decisions autonomously through the use of precomputed signaling information, which is sufficient to maintain the cost improvement property, without any on-line coordination of control selection between the agents.For discounted and other infinite horizon problems, we also consider exact and approximate PI algorithms involving a new type of one-agent-at-a-time policy improvement operation. For one of our PI algorithms, we prove convergence to an agent-by-agent optimal policy, thus establishing a connection with the theory of teams. For another PI algorithm, which is executed over a more complex state space, we prove convergence to an optimal policy. Approximate forms of these algorithms are also given, based on the use of policy and value neural networks. These PI algorithms, in both their exact and their approximate form are strictly off-line methods, but they can be used to provide a base policy for use in an on-line multiagent rollout scheme.      

Brain decoding: Opportunities and challenges for pattern recognition

The neuroimaging community heavily relies on statistical inference to explain measured brain activity given the experimental paradigm. Undeniably, this method has led to many results, but it is limited by the richness of the generative models that are deployed, typically in a mass-univariate way. Such an approach is suboptimal given the high-dimensional and complex spatiotemporal correlation structure of neuroimaging data.Over the recent years, techniques from pattern recognition have brought new insights into where and how information is stored in the brain by prediction of the stimulus or state from the data. Pattern recognition is intrinsically multivariate and the underlying models are data-driven. Moreover, the predictive setting is more powerful for many applications, including clinical diagnosis and brain–computer interfacing. This special issue features a number of papers that identify and tackle remaining challenges in this field. The specific problems at hand constitute opportunities for future research in pattern recognition and neurosciences.