Hall Effect of Spin Waves in Frustrated Magnets

We examine a possible spin Hall effect for localized spin systems with no charge degrees of freedom. In this scenario, a longitudinal magnetic field gradient induces a transverse spin current carried by spin wave excitations with an anomalous velocity which is associated with the Berry curvature raised by spin chirality, in analogy with anomalous Hall effects in itinerant electron systems. Our argument is based on a semiclassical equations of motion applicable to general spin systems. Also, a microscopic model of frustrated magnets which exhibits the anamalous spin Hall effect is presented.Comment: 5 pages, title and presentation style are changed, accepted for publication in Phys. Rev. Let

The role of the dentate gyrus and adult neurogenesis in hippocampal-basal ganglia associated behaviour

The ability of the brain to continually generate new neurons throughout life is one of the most intensely researched areas of modern neuroscience. While great advancements in understanding the biochemical mechanisms of adult neurogenesis have been made, there remain significant obstacles and gaps in connecting neurogenesis with behavioural and cognitive processes such as learning and memory. The purpose of the thesis was to examine by review and laboratory experimentation the role of the dentate gyrus and of adult neurogenesis within the hippocampus in the performance of cognitive tasks dependent on the hippocampal formation and hippocampal-basal ganglia interactions. Advancement in understanding the role of neurogenesis in these processes may assist in improving treatments for common brain injury and cognitive diseases that affect this region of the brain. Mild chronic stress reduced the acquisition rate of a stimulus-response task (p=0.043), but facilitated the acquisition of a discrimination between a small and a large reward (p=0.027). In locomotor activity assays, chronic stress did not shift the dose-response to methamphetamine. Analysis of 2,5-bromodeoxyuridine incorporation showed that, overall, chronic mild stress did not effect survival of neuronal progenitors . However, learning of the tasks had a positive influence on cell survival in stressed animals (p=0.038). Microinjections of colchicine produced significant lesions of the dentate gyrus and surrounding CA1-CA3 and neocortex. Damage to these regions impaired hippocampal-dependent reference memory (p=0.054) while preserving hippocampal independent simple discrimination learning. In a delay discounting procedure, the lesions did not induce impulsive-like behaviour when delay associated with a large reward was introduced. The experiments uphold a current theory that learning acts as a buffer to mitigate the negative effects of stress on neurogenesis

Challenges of the application of data-driven models for the real-time optimization of an industrial air separation plant

The optimization of the operation of chemical plants may require the development of mathematical models of the process units of a plant. These mathematical models can be either first-principles or data-driven models. The former type of modeling may be complex for the use in optimization and especially for online applications such as real time optimization. Available measured process data can be used to develop the latter type of modeling. Although data-driven models offer several benefits for online applications, there are some very significant challenges related to their development in a practical industrial implementation. This paper discusses the important aspects of the building of data-driven models and demonstrates the effects of these types of models on the optimization results. The current work demonstrates the application of a real time optimization framework applied to an industrial air compressor station of an air separation plant when the models are based on operating data

SISO-Closed-Loop Identifikation: eine Toolbox für den Einsatz in der industriellen Praxis

In process control, retuning of controllers is necessary from time to time due to changes of plant dynamics. Thereby, opening of the control loop is often not desired. In order to proceed in a model-based manner, a toolbox for closed-loop identification is developed in the present contribution in view of application in industrial practice. First, the theoretical foundations of closed-loop identification are reviewed, second the structure of a ready-to-use toolbox is described, and third the succesful application of the method to a real process is shown. Finally, open questions for further development are presented

Optimization of process operation strategies by combining process models with plant operating data

Competition, cost pressure, and market fluctuations lead to a persistent increase in complexity and degree of integration of chemical plants. The utilization of accurate process models facilitate plant efficiency optimization in real-time and improve process transparency for the plant personnel. To reduce development time and costs, existing models from the process development phase of the chemical plant can be used. Next to the general availability of a plant model and plant operating data, a systematic strategy is helpful to successfully implement a validated process model into the process control system of a chemical plant. This contribution presents a strategy to develop a technology platform with a validated process model containing the following steps: analysis of the economic potential, software selection, process analysis, steady-state detection, parameter identification, and process optimization. The presented strategy is applied to an industrial plant of BASF SE in Ludwigshafen, Germany. The application of this platform exposes great economic potential. Firstly, a significant cost reduction can be achieved by reusing existing models during the development phase. Secondly, with the help of the technology platform soft-sensors are created, bottlenecks identified, and an optimization of process operating strategies is undertaken

Optimization of process operation strategies by combining process models with plant operating data

Competition, cost pressure, and market fluctuations lead to a persistent increase in complexity and degree of integration of chemical plants. The utilization of accurate process models facilitate plant efficiency optimization in real-time and improve process transparency for the plant personnel. To reduce development time and costs, existing models from the process development phase of the chemical plant can be used. Next to the general availability of a plant model and plant operating data, a systematic strategy is helpful to successfully implement a validated process model into the process control system of a chemical plant. This contribution presents a strategy to develop a technology platform with a validated process model containing the following steps: analysis of the economic potential, software selection, process analysis, steady-state detection, parameter identification, and process optimization. The presented strategy is applied to an industrial plant of BASF SE in Ludwigshafen, Germany. The application of this platform exposes great economic potential. Firstly, a significant cost reduction can be achieved by reusing existing models during the development phase. Secondly, with the help of the technology platform soft-sensors are created, bottlenecks identified, and an optimization of process operating strategies is undertaken