Towards the Discovery of Influencers to Follow in Micro-Blogs (Twitter) by Detecting Topics in Posted Messages (Tweets)

Micro-blogs, such as Twitter, have become important tools to share opinions and information among users. Messages concerning any topic are daily posted. A message posted by a given user reaches all the users that decided to follow her/him. Some users post many messages, because they aim at being recognized as influencers, typically on specific topics. How a user can discover influencers concerned with her/his interest? Micro-blog apps and web sites lack a functionality to recommend users with influencers, on the basis of the content of posted messages. In this paper, we envision such a scenario and we identify the problem that constitutes the basic brick for developing a recommender of (possibly influencer) users: training a classification model by exploiting messages labeled with topical classes, so as this model can be used to classify unlabeled messages, to let the hidden topic they talk about emerge. Specifically, the paper reports the investigation activity we performed to demonstrate the suitability of our idea. To perform the investigation, we developed an investigation framework that exploits various patterns for extracting features from within messages (labeled with topical classes) in conjunction with the mostly-used classifiers for text classification problems. By means of the investigation framework, we were able to perform a large pool of experiments, that allowed us to evaluate all the combinations of feature patterns with classifiers. By means of a cost-benefit function called “Suitability”, that combines accuracy with execution time, we were able to demonstrate that a technique for discovering topics from within messages suitable for the application context is available.

Design of refractory linings for balanced energy efficiency, uptime, and capacity in lime kilns

In this work a computer model is used to examine how refractory linings with both high alumina and basic refractory bricks affect kiln operations. Recommendations are made based on the results to aid mill personnel in designing optimized refractory linings for specific situations. Kilns used to regenerate lime in the kraft process are highly energy intensive. Throughout the 1990s, in response to increasing fuel prices, the pulp and paper industry primarily used backup insulation in conjunction with high alumina brick to line calcining zones of their kilns. The dramatic decline in price of natural gas over the past decade, in combination with mounting pressures to increase production of existing assets, has led many mills to focus more on increasing uptime and capacity rather than on energy savings. To this end, a growing number of mills are using basic (magnesia based) brick instead of high alumina brick to line calcining zones. While the use of basic brick can increase the uptime and reduce the cost to maintain the refractory lining, it can dramatically increase the shell temperatures and heat losses. Tradeoffs, therefore, are created among energy efficiency, capacity, and uptime.