Stochastic modeling of flows in membrane pore networks

Membrane filters provide immediate solutions to many urgent problems such as water purification, and effective remedies to pressing environmental concerns such as waste and air treatment. The ubiquity of applications gives rise to a significant amount of research in membrane material selection and structural design to optimize filter efficiency. As physical experiments tend to be costly, numerical simulation and analysis of fluid flow, foulant transport and geometric evolution due to foulant deposition in complex geometries become particularly relevant. In this dissertation, several mathematical modeling and analytical aspects of the industrial membrane filtration process are investigated. A first-principles mathematical model for fluid flow and contaminant advection/deposition through a network of cylindrical pores, and time evolution of membrane pore geometry, is proposed, formulated as a system of ordinary and partial differential equations. Membrane filter performance metrics, including total throughput (total volume of filtered fluid) and foulant concentration at membrane pore outlets, among others, are thoroughly studied against membrane geometric features such as porosity and tortuosity (average normalized distance traveled by fluid through pores between membrane top and bottom surfaces). The influence of the underlying, often complex, pore geometries on the performance of the membrane filters is explored in the following setups: (1) layered planar membrane structures with intra-layer pore connections; (2) general pore networks generated by a random graph generation protocol; (3) pore size variations in a pore network and (4) pore size gradient in a banded membrane network. Future work should include studying pore size variations on porosity graded networks and stochastic modeling of large-particle sieving in pore networks. In Chapter 1, an overview of the experimental, computational and theoretical literature on membrane filtration is given to motivate the following Chapters. In Chapter 2, a mathematical model is proposed for multilayered membrane filters with interconnected pores in the junction between layers. A side-by-side comparison is carried out between three simple geometries that have various degrees of pore connectivity and the same initial pore radius in each layer. Pore size heterogeneities, modeled as a random perturbation on initial pore size, are also studied in detail. Via variations in the strength of the pore-size perturbation, the statistical and physical influence on key properties of membrane filters, such as initial resistance, total throughput and foulant concentration at pore outlets, are analyzed and discussed. This work appeared in Journal of Fluid Mechanics. In Chapters 3 and 4, a random graph generation protocol is devised to generate pore networks that generalize the structures considered in Chapter 2. A membrane filter is modeled as a graph with vertices and edges representing pore junctions and pore throats respectively. Local fluid and foulant transport equations are posed on each edge, coupled with conservation laws to produce global equations that capture the connectivity of the network. When a uniform initial pore radius is assumed (Chapter 3), initial membrane porosity is found to be a strong predictor for total throughput via a power law; and accumulated foulant concentration at membrane pore outlets satisfies a negative exponential relationship to membrane tortuosity. When pore size variations are imposed as pore-wise noise perturbation, however (Chapter 4), it is observed that network variations induced from the random graph generation have a stronger influence on membrane performance, unless noise strength is large. Membrane initial porosity is again found to be a crucial geometric feature. The work of these Chapters appeared in SIAM Journal on Applied Mathematics and Journal of Membrane Science, respectively. In Chapter 5, a variant of the protocol described in Chapter 3 is developed to generate banded pore networks in which the pore radius decreases from one band to the next, creating a pore-size gradient. Under specific assumptions, an optimal radius gradient in the depth of the banded membrane that maximizes either total throughput of filtrate or the particle retention capability of the membrane, is found. Finally, in Chapter 6, conclusions from the previous chapters are discussed, along with two open questions for future work

Slow Lawyering: Representing Seniors in Light of Cognitive Changes Accompanying Aging

As an increasing number of lawyers represent clients who are elderly, it is imperative that lawyers become more knowledgeable about the aging process and how it impacts our clients. Although it is difficult to generalize, many seniors experience numerous and diverse cognitive changes that accompany the aging process. Existing literature offers various frameworks for addressing capacity issues and techniques for assessing diminished capacity. However, current legal scholarship provides little guidance for lawyers on how to accommodate these changes when they do not rise to the level of diminished capacity or dementia, and when the changes may, in fact, result in increased wisdom and developmental intelligence. This article seeks to fill that void. It summarizes selected cognitive developments that impact memory, outlining various types of memory and how they evolve during the aging process. This article also discusses current literature on decision-making capacity and different decision-making models and strategies that seniors may rely upon. The article concludes with recommendations on methods for enhancing communications with aging clients, while simultaneously acknowledging and accommodating cognitive changes and enabling seniors to play a prominent role in the representational process

Adaptive control of compliant robots with Reservoir Computing

In modern society, robots are increasingly used to handle dangerous, repetitive and/or heavy tasks with high precision. Because of the nature of the tasks, either being dangerous, high precision or simply repetitive, robots are usually constructed with high torque motors and sturdy materials, that makes them dangerous for humans to handle. In a car-manufacturing company, for example, a large cage is placed around the robot’s workspace that prevents humans from entering its vicinity. In the last few decades, efforts have been made to improve human-robot interaction. Often the movement of robots is characterized as not being smooth and clearly dividable into sub-movements. This makes their movement rather unpredictable for humans. So, there exists an opportunity to improve the motion generation of robots to enhance human-robot interaction. One interesting research direction is that of imitation learning. Here, human motions are recorded and demonstrated to the robot. Although the robot is able to reproduce such movements, it cannot be generalized to other situations. Therefore, a dynamical system approach is proposed where the recorded motions are embedded into the dynamics of the system. Shaping these nonlinear dynamics, according to recorded motions, allows for dynamical system to generalize beyond demonstration. As a result, the robot can generate motions of other situations not included in the recorded human demonstrations. In this dissertation, a Reservoir Computing approach is used to create a dynamical system in which such demonstrations are embedded. Reservoir Computing systems are Recurrent Neural Network-based approaches that are efficiently trained by considering only the training of the readout connections and retaining all other connections of such a network unchanged given their initial randomly chosen values. Although they have been used to embed periodic motions before, they were extended to embed discrete motions, or both. This work describes how such a motion pattern-generating system is built, investigates the nature of the underlying dynamics and evaluates their robustness in the face of perturbations. Additionally, a dynamical system approach to obstacle avoidance is proposed that is based on vector fields in the presence of repellers. This technique can be used to extend the motion abilities of the robot without need for changing the trained Motion Pattern Generator (MPG). Therefore, this approach can be applied in real-time on any system that generates a certain movement trajectory. Assume that the MPG system is implemented on an industrial robotic arm, similar to the ones used in a car factory. Even though the obstacle avoidance strategy presented is able to modify the generated motion of the robot’s gripper in such a way that it avoids obstacles, it does not guarantee that other parts of the robot cannot collide with a human. To prevent this, engineers have started to use advanced control algorithms that measure the amount of torque that is applied on the robot. This allows the robot to be aware of external perturbations. However, it turns out that, even with fast control loops, the adaptation to compensate for a sudden perturbation, is too slow to prevent high interaction forces. To reduce such forces, researchers started to use mechanical elements that are passively compliant (e.g., springs) and light-weight flexible materials to construct robots. Although such compliant robots are much safer and inherently energy efficient to use, their control becomes much harder. Most control approaches use model information about the robot (e.g., weight distribution and shape). However, when constructing a compliant robot it is hard to determine the dynamics of these materials. Therefore, a model-free adaptive control framework is proposed that assumes no prior knowledge about the robot. By interacting with the robot it learns an inverse robot model that is used as controller. The more it interacts, the better the control be- comes. Appropriately, this framework is called Inverse Modeling Adaptive (IMA) control framework. I have evaluated the IMA controller’s tracking ability on sev- eral tasks, investigating its model independence and stability. Furthermore, I have shown its fast learning ability and comparable performance to taskspecific designed controllers. Given both the MPG and IMA controllers, it is possible to improve the inter- actability of a compliant robot in a human-friendly environment. When the robot is to perform human-like motions for a large set of tasks, we need to demonstrate motion examples of all these tasks. However, biological research concerning the motion generation of animals and humans revealed that a limited set of motion patterns, called motion primitives, are modulated and combined to generate advanced motor/motion skills that humans and animals exhibit. Inspired by these interesting findings, I investigate if a single motion primitive indeed can be modulated to achieve a desired motion behavior. By some elementary experiments, where an MPG is controlled by an IMA controller, a proof of concept is presented. Furthermore, a general hierarchy is introduced that describes how a robot can be controlled in a biology-inspired manner. I also investigated how motion primitives can be combined to produce a desired motion. However, I was unable to get more advanced implementations to work. The results of some simple experiments are presented in the appendix. Another approach I investigated assumes that the primitives themselves are undefined. Instead, only a high-level description is given, which describes that every primitive on average should contribute equally, while still allowing for a single primitive to specialize in a part of the motion generation. Without defining the behavior of a primitive, only a set of untrained IMA controllers is used of which each will represent a single primitive. As a result of the high-level heuristic description, the task space is tiled into sub-regions in an unsupervised manner. Resulting in controllers that indeed represent a part of the motion generation. I have applied this Modular Architecture with Control Primitives (MACOP) on an inverse kinematic learning task and investigated the emerged primitives. Thanks to the tiling of the task space, it becomes possible to control redundant systems, because redundant solutions can be spread over several control primitives. Within each sub region of the task space, a specific control primitive is more accurate than in other regions allowing for the task complexity to be distributed over several less complex tasks. Finally, I extend the use of an IMA-controller, which is tracking controller, to the control of under-actuated systems. By using a sample-based planning algorithm it becomes possible to explore the system dynamics in which a path to a desired state can be planned. Afterwards, MACOP is used to incorporate feedback and to learn the necessary control commands corresponding to the planned state space trajectory, even if it contains errors. As a result, the under-actuated control of a cart pole system was achieved. Furthermore, I presented the concept of a simulation based control framework that allows the learning of the system dynamics, planning and feedback control iteratively and simultaneously

Water in the Green Economy: Capacity Development Aspects

This book discusses needs related to capacity development for water resources management, including water supply and sanitation, in the context of the green economy. It showcases theoretical and practical approaches with proven success. Most contributions come from members and partners within the interagency mechanism, UN-Water. The 11 case studies in this book range from innovative design and delivery of capacity development programs related to water in the green economy, market mechanisms, and quality control procedures supporting capacity development success towards the practical implementation of programs to enhance individual and institutional capacity

Greater Than the Sum: Systems Thinking in Tobacco control

Tobacco control and public health have evolved into a complex set of interconnected and largely self-organizing systems. Their components include international, national, and local governmental agencies; individual advocacy groups; policy makers; health care professionals; nonprofit foundations; and the general population itself. The issues require the exploration of approaches and methodologies that speak to the evolving, dynamic nature of this systems environment. This monograph focuses on the first two years of the Initiative on the Study and Implementation of Systems (ISIS), which was funded by the National Cancer Institute to examine the potential for systems thinking in tobacco control and public health. ISIS explored the general idea of a systems thinking rubric encompassing a great variety of systems-oriented methodologies and approaches. Four approaches have particular promise for their applicability to tobacco control and public health and thus were chosen as areas for initial investigation: (1) organizing and managing as a system, (2) system dynamics and how to model those dynamics, (3) system networks and their analysis, and (4) systems knowledge and its management and translation. As a transdisciplinary effort that linked both tobacco control stakeholders and systems experts, ISIS combined a number of exploratory projects and case studies within these four approaches with a detailed examination of the potential for systems thinking in tobacco control. Its end product was a set of expert consensus guidelines for the future implementation of systems thinking and systems perspectives for tobacco control and public health.https://cancercontrol.cancer.gov/tcrb/monographs/18/index.htm

Transforming the Education Sector into a Learning System: Perspectives from the Field and Recommendations for Action

Carnegie Corporation of New York and EducationCounsel address a critical question of how to accelerate a shift from our current educational system to a new learning system. An ambitious vision for what is possible is outlined, along with starting points for making this shift. Executing on these ideas will require comprehensive plans that, among other things, enumerate specific actions, clarify roles, and identify needed resources.

How to Make the Most Productive Intervention in a Complex Economic System

Information about supply and demand propagates through supply chains in a queueing network with people and computers as batch information processors. As each batch processor delays propagation of information whilst pursuing optimal local decisions, the effect is delay and distortion of the information that is used to commit resources to actions in the supply chain. This thesis investigates the effect of delay and imperfect information as a source of error, to establish the case for change in research focus from optimal exploitation of physical constraints to optimal exploitation of information. In the context of real world supply chains, the thesis asks "How does one make the most productive intervention in a complex economic system?" and pursues a meta-intervention which perpetually minimises the discovered error-term. Evidence from literature indicates that agent-based modelling permits real-time peer-to-peer communication and distributed optimisation. Based on the literature the research project designs and develops an agent-based model which operates in real-time without batch-processes and can perform incremental multi-objective optimisation under realistic (chronologically progressive) conditions for decision making. The agent based model is then used to investigate two real-world supply chains, as case studies, which reveals a significant improvement of profitability and order-fulfilment. The thesis concludes that agent-based modelling is a very promising direction for "making the most productive intervention" as it reduces delay to a minimum. Finally it recommends that continuous improvement of decision making methods is a role better suited for humans, rather than operational decision making where computers cope much better with the high amount of detailed information