Optimal operation of distillation processes under uncertain inflows accumulated in a feed tank

A new method of optimizing the operation of distillation processes with uncertain inflow streams from upstream plants is discussed. The case considered is the streams that will be first accumulated in a tank before feeding to the column. To minimize the total amount of operating energy while keeping a stable column operation under these inflows, a novel decomposed optimization strategy consisting of two steps was used. For an optimal planning of the dynamic operation, a smooth feed flow policy to the column is developed in the first step by stochastic optimization under chance constraints by ensuring a predefined probability of holding the tank level inside the desired range. An easy-to-use method developed computes the maximum reachable probability of holding the constraints so that a feasible solution of the chance constrained problem can be guaranteed. Since the uncertainty in the inflow stream variability is absorbed in the stochastic optimization over the tank, the operation of the dowstream distillation column is deterministic. Therefore, in the second step, the reflux and reboiler duty policies of the column are developed by deterministic dynamic optimization. The optimal overall strategy is obtained by the maximized smoothness of the feed flow to the distillation column. The approach is applied to a pilot column, and the developed operating policies are implemented on the real plant by experiment.     

Analyzing settings for social identity management on Social Networking Sites: Classification,current state,and proposed developments

The rising prevalence of Social Networking Sites (SNS) and their usage in multiple contexts poses new privacy challenges and increasingly prompts users to manage their online identity. To address privacy threats stemming from interacting with other users on SNS, effective Social Identity Management (SIdM) is a key requirement. It refers to the deliberate and targeted disclosure of personal attribute values to a subset of one's contacts or other users on the SNS. Protection against other entities such as the site operator itself or advertisers and application programmers is not covered by SIdM, but could be incorporated in further refinement steps. Features and settings to perform SIdM have been proposed and subsequently implemented partly by some SNS. Yet, these are often isolated solutions that lack integration into a reference framework that states the requirements for successfully managing one's identity. In this article, such a reference framework of existing and desired SIdM settings is derived from identity theory, literature analysis, and existing SNS. Based thereupon, we examine the SIdM capabilities of prevalent SNS and highlight possible improvements. Lastly, we reason about developing a metric to objectively compare the capability of SNS in regards to their support for SIdM.     

Automated processing for map generalization using web services

In map generalization various operators are applied to the features of a map in order to maintain and improve the legibility of the map after the scale has been changed. These operators must be applied in the proper sequence and the quality of the results must be continuously evaluated. Cartographic constraints can be used to define the conditions that have to be met in order to make a map legible and compliant to the user needs. The combinatorial optimization approaches shown in this paper use cartographic constraints to control and restrict the selection and application of a variety of different independent generalization operators into an optimal sequence. Different optimization techniques including hill climbing, simulated annealing and genetic deep search are presented and evaluated experimentally by the example of the generalization of buildings in blocks. All algorithms used in this paper have been implemented in a web services framework. This allows the use of distributed and parallel processing in order to speed up the search for optimized generalization operator sequences.

Secretome-based proteomics reveals sulindac-modulated proteins released from colon cancer cells

Although experiments in rodents and human population-based studies have demonstrated the efficacy of nonsteroidal anti-inflammatory drugs (NSAIDs) such as sulindac in colorectal cancer (CRC) prevention, a detailed knowledge of the underlying mechanism of action of this drug is limited. To better understand the chemopreventitive effects of sulindac, especially early sulindac-induced apoptotic events, we used the CRC cell line LIM1215 as an experimental model, focusing on proteins secreted into the LIM1215 culture medium - i.e., the secretome. This subproteome comprises both soluble-secreted proteins and exosomes (30-100?nm diameter membrane vesicles released by several cell types). Selected secretome proteins whose expression levels were dysregulated by 1?mM sulindac treatment over 16?h were analyzed using 2-D DIGE, cytokine array, Western blotting, and MS. Overall, 150 secreted proteins were identified, many of which are implicated in molecular and cellular functions such as cell proliferation, differentiation, adhesion, invasion, angiogenesis, metastasis, and apoptosis. Our secretome-based proteomic studies have identified several secreted modulators of sulindac-induced apoptosis action (e.g., Mac-2 binding protein, Alix, 14-3-3 isoforms, profilin-1, calumenin/Cab45 precursors, and the angiogenic/tumor growth factors interleukin 8 (IL-8) and growth related oncogene (GRO-α)) that are likely to improve our understanding of the chemopreventitive action of this NSAID in CRC.     

3D Knitting for Pneumatic Soft Robotics

Soft robots adapt passively to complex environments due to their inherent compliance, allowing them to interact safely with fragile or irregular objects and traverse uneven terrain. The vast tunability and ubiquity of textiles has enabled new soft robotic capabilities, especially in the field of wearable robots, but existing textile processing techniques (e.g., cut-and-sew, thermal bonding) are limited in terms of rapid, additive, accessible, and waste-free manufacturing. While 3D knitting has the potential to address these limitations, an incomplete understanding of the impact of structure and material on knit-scale mechanical properties and macro-scale device performance has precluded the widespread adoption of knitted robots. In this work, the roles of knit structure and yarn material properties on textile mechanics spanning three regimes–unfolding, geometric rearrangement, and yarn stretching–are elucidated and shown to be tailorable across unique knit architectures and yarn materials. Based on this understanding, 3D knit soft actuators for extension, contraction, and bending are constructed. Combining these actuation primitives enables the monolithic fabrication of entire soft grippers and robots in a single-step additive manufacturing procedure suitable for a variety of applications. This approach represents a first step in seamlessly “printing” conformal, low-cost, customizable textile-based soft robots on-demand.     

Homopolymer Nanolithography

Future progress in nanoscience and nanotechnology necessitates further development of versatile, labor‐, and cost‐efficient surface patterning strategies. A new approach to nanopatterning is reported, which utilizes surface segregation of a smooth layer of an end‐grafted homopolymer in a poor solvent. The variation in polymer grafting density yields a range of surface nanostructures, including randomly organized pinned spherical micelles, worm‐like structures, networks, and porous films. The capability to use the polymer patterns for site‐specific deposition of small molecules, polymers, or nanoparticles is shown. This versatile strategy enables patterning of curved surfaces with direct access to the substrate and no need in changing polymer composition to realize different surface patterns.     

Nested multigrid methods for time-periodic,parabolic optimal control problems

We present a nested multigrid method to optimize time-periodic, parabolic, partial differential equations (PDE). We consider a quadratic tracking objective with a linear parabolic PDE constraint. The first order optimality conditions, given by a coupled system of boundary value problems can be rewritten as an Fredholm integral equation of the second kind, which is solved by a multigrid of the second kind. The evaluation of the integral operator consists of solving sequentially a boundary value problem for respectively the state and the adjoints. Both problems are solved efficiently by a time-periodic space-time multigrid method.     

Evaluating and improving the content access in KAD

We analyze in detail the content retrieval process in kad. kad implements content search (publish and retrieval) functions that use the Kademlia Distributed Hash Table for content routing. Node churn is quite common in peer-to-peer systems and results in information loss and stale routing table entries. To deal with node churn, kad issues parallel route requests and publishes multiple redundant copies of each piece of information. We identify the key design parameters in kad and present an analytical model to evaluate the impact of changes in the values of these parameters on the overall lookup latency and message overhead. Extensive measurements of the lookup performance using an instrumented client allow us to validate the model. The overall lookup latency is in most cases 5 s or larger. We elucidate the cause for such high lookup latencies and propose an improved scheme that significantly decreases the overall lookup latency without increasing the overhead.