Rocket Performance of Red Fuming Nitric Acid with Blends of Norbornadiene, Carene and Cardanol

The fuel blends of nornornadiene and carene (50:50 by weight) and norbornadiene, carene and cardanol (40:40:20 by weight) exhibit synergistic hypergolic ignition with red fuming nitric acid (RFNA) as oxidiser. These fuel blends have been evaluated by theoretical calculations of performance parameters and subsequently verified by static firing in a 10 kg/sub f/ thruster at a chamber pressure of around 20 atm, using RFNA (with 21 per cent N/sub 2/O/sub 4/ by weight) as oxidiser. The theoretical calculations show maximum specific impulse and C*values at the O/F, 3 to be 227.8 s and 1598.7 m/s respectively for the norbornadiene-carene blend. The corresponding values for the norbornadiene, carene and cardanol blend were found to be 226.8 s and 1586.0 m/s respectively at the O/F, 4. For theoretical calculations, the chamber pressure (P/sub c) and the exit pressure (P/sub e/0 were assumed to be 20 and 1 atm, respectively. The static firing of the propellants in a 10 kg thruster exhibited smooth pressure-time curves with the experimental C* values in close agreement with those calculated and the non-deposition of carbon in the nozzle. This indicated low combustion instability and high combustion efficiency under rocket conditions (> 0.9). The fuel blends with their low cost and toxicity and relatively high density can replace G-fuel used in several Indian missiles without impairing the performance

Awake fMRI Reveals Brain Regions for Novel Word Detection in Dogs

How do dogs understand human words? At a basic level, understanding would require the discrimination of words from non-words. To determine the mechanisms of such a discrimination, we trained 12 dogs to retrieve two objects based on object names, then probed the neural basis for these auditory discriminations using awake-fMRI. We compared the neural response to these trained words relative to “oddball” pseudowords the dogs had not heard before. Consistent with novelty detection, we found greater activation for pseudowords relative to trained words bilaterally in the parietotemporal cortex. To probe the neural basis for representations of trained words, searchlight multivoxel pattern analysis (MVPA) revealed that a subset of dogs had clusters of informative voxels that discriminated between the two trained words. These clusters included the left temporal cortex and amygdala, left caudate nucleus, and thalamus. These results demonstrate that dogs’ processing of human words utilizes basic processes like novelty detection, and for some dogs, may also include auditory and hedonic representations

The Daemo crowdsourcing marketplace

The success of crowdsourcing markets is dependent on a strong foundation of trust between workers and requesters. In current marketplaces, workers and requesters are often unable to trust each other’s quality, and their mental models of tasks are misaligned due to ambiguous instructions or confusing edge cases. This breakdown of trust typically arises from (1) flawed reputation systems which do not accurately reflect worker and requester quality, and from (2) poorly designed tasks. In this demo, we present how Boomerang and Prototype Tasks, the fundamental building blocks of the Daemo crowdsourcing marketplace, help restore trust between workers and requesters. Daemo’s Boomerang reputation system incentivizes alignment between opinion and ratings by determining the likelihood that workers and requesters will work together in the future based on how they rate each other. Daemo’s Prototype tasks require that new tasks go through a feedback iteration phase with a small number of workers so that requesters can revise their instructions and task designs before launch

Prototype tasks: Improving crowdsourcing results through rapid, iterative task design

Low-quality results have been a long-standing problem on microtask crowdsourcing platforms, driving away requesters and justifying low wages for workers. To date, workers have been blamed for low-quality results: they are said to make as little effort as possible, do not pay attention to detail, and lack expertise. In this paper, we hypothesize that requesters may also be responsible for low-quality work: they launch unclear task designs that confuse even earnest workers, under-specify edge cases, and neglect to include examples. We introduce prototype tasks, a crowdsourcing strategy requiring all new task designs to launch a small number of sample tasks. Workers attempt these tasks and leave feedback, enabling the requester to iterate on the design before publishing it. We report a field experiment in which tasks that underwent prototype task iteration produced higher-quality work results than the original task designs. With this research, we suggest that a simple and rapid iteration cycle can improve crowd work, and we provide empirical evidence that requester “quality” directly impacts result quality

Crowd guilds: Worker-led reputation and feedback on crowdsourcing platforms

Crowd workers are distributed and decentralized. While decentralization is designed to utilize independent judgment to promote high-quality results, it paradoxically undercuts behaviors and institutions that are critical to high-quality work. Reputation is one central example: crowdsourcing systems depend on reputation scores from decentralized workers and requesters, but these scores are notoriously inflated and uninformative. In this paper, we draw inspiration from historical worker guilds (e.g., in the silk trade) to design and implement crowd guilds: centralized groups of crowd workers who collectively certify each other’s quality through double-blind peer assessment. A two-week field experiment compared crowd guilds to a traditional decentralized crowd work model. Crowd guilds produced reputation signals more strongly correlated with ground-truth worker quality than signals available on current crowd working platforms, and more accurate than in the traditional model

Co-opting the state: how weak parties can make stable party systems

Conventional understandings of party system institutionalisation assume that institutionalised parties are necessary for interparty competition to stabilise. However, this approach neglects the role of the state in shaping party competition. Using survey data from Bangladesh, India and Pakistan, it is shown that weakly institutionalised parties can lead to institutionalised party systems if parties are able to successfully co-opt the state and use state resources to supplement party deficiencies. By developing a relationship that intertwines parties with the state, parties in young democracies do not need to institutionalise for stable party systems to form

Boomerang: Rebounding the consequences of reputation feedback on crowdsourcing platforms

Paid crowdsourcing platforms suffer from low-quality workand unfair rejections, but paradoxically, most workers and requesters have high reputation scores. These inflated scores, which make high-quality work and workers difficult to find,stem from social pressure to avoid giving negative feedback. We introduce Boomerang, a reputation system for crowdsourcing that elicits more accurate feedback by rebounding the consequences of feedback directly back onto the person who gave it. With Boomerang, requesters find that their highly rated workers gain earliest access to their future tasks, and workers find tasks from their highly-rated requesters at the top of their task feed. Field experiments verify that Boomerang causes both workers and requesters to provide feedback that is more closely aligned with their private opinions. Inspired by a game-theoretic notion of incentive-compatibility, Boomerang opens opportunities for interaction design to incentivize honest reporting over strategic dishonesty