Healable,Stable and Stiff Hydrogels: Combining Conflicting Properties Using Dynamic and Selective Three‐Component Recognition with Reinforcing Cellulose Nanorods

Nanocomposite hydrogels are prepared combining polymer brush‐modified ‘hard’ cellulose nanocrystals (CNC) and ‘soft’ polymeric domains, and bound together by cucurbit[8]uril (CB[8]) supramolecular crosslinks, which allow dynamic host–guest interactions as well as selective and simultaneous binding of two guests, i.e., methyl viologen (the first guest) and naphthyl units (the second guest). CNCs are mechanically strong colloidal rods with nanometer‐scale lateral dimensions, which are functionalized by surface‐initiated atom transfer radical polymerization to yield a dense set of methacrylate polymer brushes bearing naphthyl units. They can then be non‐covalently cross‐linked through simple addition of poly(vinyl alcohol) polymers containing pendant viologen units as well as CB[8]s in aqueous media. The resulting supramolecular nanocomposite hydrogels combine three important criteria: high storage modulus (G′ > 10 kPa), rapid sol–gel transition (<6 s), and rapid self‐healing even upon aging for several months, as driven by balanced colloidal reinforcement as well as the selectivity and dynamics of the CB[8] three‐component supramolecular interactions. Such a new combination of properties for stiff and self‐healing hydrogel materials suggests new approaches for advanced dynamic materials from renewable sources.     

Subsurface Biological Activity Zone Detection Using Genetic Search Algorithms

Use of generic search algorithms for detection of subsurface biological activity zones (BAZ) is investigated through a series of hypothetical numerical biostimulation experiments. Continuous injection of dissolved oxygen and methane with periodically varying concentration stimulates the cometabolism of indigenous methanotropic bacteria. The observed breakthroughs of methane are used to deduce possible BAZ in the subsurface. The numerical experiments are implemented in a parallel computing environment to make possible the large number of simultaneous transport simulations required by the algorithm. Our results show that genetic algorithms are very efficient in locating multiple activity zones, provided the observed signals adequately sample the BAZ.     

Prime Time Commemoration: An Analysis of Television Broadcasts on Israel's Memorial Day for the Holocaust and the Heroism

This study explores the ways in which commercial media perceive and manifest their public mnemonic role. It does so via an exploration of the "memory menu"—the contents and flow of programming—offered by Channel 2, Israel's leading commercial television channel, on the eve of the country's Memorial Day for the Holocaust and the Heroism (MDHH), in which the airing of commercials is banned. In order to do so, the study incorporates a multilevel analysis that probes the structure of entire broadcasting evenings as well as the narrative building blocks that constitute each item. The study investigates the ways in which commercial media outlets operate in the context of "commercial vacuums" as they substitute material capital with symbolic capital. This process is illuminated through Channel 2's inability to work MDHH into its extremely successful routine broadcasting formulas. The channel's MDHH broadcasts construct a commemorative narrative that is insulated from day-to-day Israeli public Holocaust memory discourse; hence they operate as a significant site of Israeli postmemory work. Furthermore, such a narrative not only commemorates the memory of the Holocaust itself but also the ways in which Israeli culture used to narrate the memory of the Holocaust in the past.     

Economic Consequences of Alternative Solution Methods for Centralized Unit Commitment in Day-Ahead Electricity Markets

Many wholesale electricity markets call on the independent system operator (ISO) to determine day-ahead schedules for generators based on a centralized unit commitment. Up until recently, the Lagrangian relaxation (LR) algorithm was the only practical means of solving an ISO-scale unit commitment problem, and it was the solution technique used by most ISOs. Johnson et al. [1] demonstrate, however, that equity, incentive, and efficiency issues will arise from use of LR solutions, because different commitments that are similar in terms of total system costs can result in different surpluses to individual units. Recent advances in computing capabilities and optimization algorithms now make solution of the mixed-integer programming (MIP) formulation by means of branch and bound (B&B) tractable, often with optimality gaps smaller than those of LR algorithms, which has led some ISOs to adopt B&B algorithms and others proposing to do so. With the move towards B&B, one obvious question is whether the use of MIP will eliminate or reduce the issues with LR raised by Johnson et al. Using actual market data from an ISO, we demonstrate that both LR and MIP solutions will suffer the same equity issues, unless the ISO unit commitment problems can be solved to complete optimality within the allotted timeframe-which is beyond current computational capabilities. Our results further demonstrate that the size of the payoff deviations are not monotone in the size of the optimality gap, meaning smaller optimality gaps from B&B will not necessarily mitigate the issues Johnson et al. raise. We show that the use of "make-whole" payments, which ensure units recover any startup and no-load costs not recovered by inframarginal energy rents, can help to reduce surplus volatility and differences to some extent.     

The craving to smoke in flight attendants: Relations with smoking deprivation, anticipation of smoking, and actual smoking.

In the study, the authors examined the effects of smoking deprivation, anticipation of smoking, and actual smoking on the craving to smoke. Flight attendants who were light to heavy smokers rated their craving to smoke at predetermined time points during a 2-way short flight (each leg 3–5.5 hr) and a 1-way long flight (8–13 hr). In both short and long flights, craving increased gradually and peaked as landing approached. Craving levels at the end of the 1st leg of the short flights were equal to those at the end of the long flight and were much higher than those at the parallel time point in the long flight. In the short flight, craving levels at the beginning of the 2nd leg dropped relative to the end of the 1st leg, both for participants who smoked during the intermission and for those who did not, though the drop was steeper for the former. The results provide additional evidence for the role of psychological factors in determining the craving to smoke in a naturalistic setting. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Near-field effects in multilayer inductive metal meshes

The transmittance of inductive single-layer and multilayer cross-shaped metal meshes has been calculated with the Micro-Stripes software program. The effect of symmetric and asymmetric alignment of the crosses of one mesh with respect to another was studied and compared with transmission line theory, which presents the nonaligned case. Significant differences are found for small spacing at approximately 1/5 the periodicity constant, whereas the differences disappear for large spacing. A pair of coupled surface waves is used to represent the mode of a single mesh. The resulting modes corresponding to the transmittance of multilayer metal meshes are interpreted by modes composed of resonance modes of a single mesh coupled by Fabry-Perot modes depending on the separation.     

Biomimetic Supramolecular Fibers Exhibit Water‐Induced Supercontraction

Spider silk is a fascinating material, combining high strength and elasticity that outperforms most synthetic fibers. Another intriguing feature of spider silk is its ability to “supercontract,” shrinking up to 50% when exposed to water. This is likely on account of the entropy‐driven recoiling of secondary structured proteins when water penetrates the spider silk. In contrast, humidity‐driven contraction in synthetic fibers is difficult to achieve. Here, inspired by the spider silk model, a supercontractile fiber (SCF), which contracts up to 50% of its original length at high humidity, comparable to spider silk, is reported. The fiber exhibits up to 300% uptake of water by volume, confirmed via environmental scanning electron microscopy. Interestingly, the SCF exhibits tunable mechanical properties by varying humidity, which is reflected by the prolonged failure strain and the reversible damping capacity. This smart supramolecular fiber material provides a new opportunity of fabricating biomimetic muscle for diverse applications.     

Analysis of a probabilistic model of redundancy in unsupervised information extraction

Unsupervised Information Extraction (UIE) is the task of extracting knowledge from text without the use of hand-labeled training examples. Because UIE systems do not require human intervention, they can recursively discover new relations, attributes, and instances in a scalable manner. When applied to massive corpora such as the Web, UIE systems present an approach to a primary challenge in artificial intelligence: the automatic accumulation of massive bodies of knowledge.A fundamental problem for a UIE system is assessing the probability that its extracted information is correct. In massive corpora such as the Web, the same extraction is found repeatedly in different documents. How does this redundancy impact the probability of correctness?We present a combinatorial “balls-and-urns” model, called Urns, that computes the impact of sample size, redundancy, and corroboration from multiple distinct extraction rules on the probability that an extraction is correct. We describe methods for estimating Urns's parameters in practice and demonstrate experimentally that for UIE the model's log likelihoods are 15 times better, on average, than those obtained by methods used in previous work. We illustrate the generality of the redundancy model by detailing multiple applications beyond UIE in which Urns has been effective. We also provide a theoretical foundation for Urns's performance, including a theorem showing that PAC Learnability in Urns is guaranteed without hand-labeled data, under certain assumptions.     

Unsupervised named-entity extraction from the Web: An experimental study

The KnowItAll system aims to automate the tedious process of extracting large collections of facts (e.g., names of scientists or politicians) from the Web in an unsupervised, domain-independent, and scalable manner. The paper presents an overview of KnowItAll's novel architecture and design principles, emphasizing its distinctive ability to extract information without any hand-labeled training examples. In its first major run, KnowItAll extracted over 50,000 class instances, but suggested a challenge: How can we improve KnowItAll's recall and extraction rate without sacrificing precision?This paper presents three distinct ways to address this challenge and evaluates their performance. Pattern Learning learns domain-specific extraction rules, which enable additional extractions. Subclass Extraction automatically identifies sub-classes in order to boost recall (e.g., “chemist” and “biologist” are identified as sub-classes of “scientist”). List Extraction locates lists of class instances, learns a “wrapper” for each list, and extracts elements of each list. Since each method bootstraps from KnowItAll's domain-independent methods, the methods also obviate hand-labeled training examples. The paper reports on experiments, focused on building lists of named entities, that measure the relative efficacy of each method and demonstrate their synergy. In concert, our methods gave KnowItAll a 4-fold to 8-fold increase in recall at precision of 0.90, and discovered over 10,000 cities missing from the Tipster Gazetteer.