Preventing Youth Pregnancy: Dialogue and Deliberation in a Science Museum Exhibit

ABSTRACT

In this article, we explore how visitors engage with a science museum exhibit that displays controversial topics. Through a case study methodology, we focus on the Brazilian exhibit Preventing Youth Pregnancy that delves into teen pregnancy, sexual practices, and sexually transmitted diseases. Using the lens of science communication (an emergent field of research) we explore (a) communication patterns established between the exhibit and the public and (b) the dimensions of engagement that visitors experienced. Data were collected through observations and interviews with visitors, field notes, exit comment cards, and interviews with staffers. Our findings revealed the potential of articulating different models of science communication and exposed a visitor experience in which informed decision making, sharing of personal stories, co-production of knowledge, and learning how to care (and act) were at play while negotiating the complexities of the issues raised by the displays.     

Forecasting binary longitudinal data by a functional PC-ARIMA model

In order to forecast time evolution of a binary response variable from a related continuous time series a functional logit model is proposed. The estimation of this model from discrete time observations of the predictor is solved by using functional principal component analysis and ARIMA modelling of the associated discrete time series of principal components. The proposed model is applied to forecast the risk of drought from El Niño phenomenon.     

A review on the combination of binary classifiers in multiclass problems

Several real problems involve the classification of data into categories or classes. Given a data set containing data whose classes are known, Machine Learning algorithms can be employed for the induction of a classifier able to predict the class of new data from the same domain, performing the desired discrimination. Some learning techniques are originally conceived for the solution of problems with only two classes, also named binary classification problems. However, many problems require the discrimination of examples into more than two categories or classes. This paper presents a survey on the main strategies for the generalization of binary classifiers to problems with more than two classes, known as multiclass classification problems. The focus is on strategies that decompose the original multiclass problem into multiple binary subtasks, whose outputs are combined to obtain the final prediction.     

Modeling and solving the rooted distance-constrained minimum spanning tree problem

In this paper we discuss models and methods for solving the rooted distance constrained minimum spanning tree problem which is defined as follows: given a graph G=(V,E)$G=(V,E)$ with node set V={0,1,…,n}$V=\{0,1,\dots ,n\}$ and edge set E$E$, two integer weights, a cost _ce$c_e$ and a delay _we$w_e$ associated with each edge e$e$ of E$E$, and a natural (time limit) number H$H$, we wish to find a spanning tree T$T$ of the graph with minimum total cost and such that the unique path from a specified root node, node 0, to any other node has total delay not greater than H$H$. This problem generalizes the well known hop-constrained spanning tree problem and arises in the design of centralized networks with quality of service constraints and also in package shipment with service guarantee constraints. We present three theoretically equivalent modeling approaches, a column generation scheme, a Lagrangian relaxation combined with subgradient optimization procedure, both based on a path formulation of the problem, and a shortest path (compact) reformulation of the problem which views the underlying subproblem as defined in a layered extended graph. We present results for complete graph instances with up to 40 nodes. Our results indicate that the layered graph path reformulation model is still quite good when the arc weights are reasonably small. Lagrangian relaxation combined with subgradient optimization procedure appears to work much better than column generation and seems to be a quite reasonable approach to the problem for large weight, and even small weight, instances.     

Thionine immobilized in crosslinked chitosan films

Redox and acid–base properties of thionine immobilized in crosslinked chitosan films are analyzed. The experimental conditions for the preparation of the films are described. Results obtained with cyclic voltammetry, spectroelectrochemistry and spectrophotometry indicate that the immobilized dye retains the redox and acid–base activity of free thionine. An acid dissociation constant (K_a) for the immobilized dye of K_a = 2.7 × 10⁻¹¹ was obtained.

Due to the stable and reversible pH dependent optical signal of the studied film, this membrane can be used as a building component of an optical pH sensor.     

A flexible semantic inference methodology to reason about user preferences in knowledge-based recommender systems

Recommender systems arose with the goal of helping users search in overloaded information domains (like e-commerce, e-learning or Digital TV). These tools automatically select items (commercial products, educational courses, TV programs, etc.) that may be appealing to each user taking into account his/her personal preferences. The personalization strategies used to compare these preferences with the available items suffer from well-known deficiencies that reduce the quality of the recommendations. Most of the limitations arise from using syntactic matching techniques because they miss a lot of useful knowledge during the recommendation process. In this paper, we propose a personalization strategy that overcomes these drawbacks by applying inference techniques borrowed from the Semantic Web. Our approach reasons about the semantics of items and user preferences to discover complex associations between them. These semantic associations provide additional knowledge about the user preferences, and permit the recommender system to compare them with the available items in a more effective way. The proposed strategy is flexible enough to be applied in many recommender systems, regardless of their application domain. Here, we illustrate its use in AVATAR, a tool that selects appealing audiovisual programs from among the myriad available in Digital TV.     

Adaptation and application of multi-objective evolutionary algorithms for rule reduction and parameter tuning of fuzzy rule-based systems

Recently, multi-objective evolutionary algorithms have been applied to improve the difficult tradeoff between interpretability and accuracy of fuzzy rule-based systems. It is known that both requirements are usually contradictory, however, these kinds of algorithms can obtain a set of solutions with different trade-offs. This contribution analyzes different application alternatives in order to attain the desired accuracy/interpr-etability balance by maintaining the improved accuracy that a tuning of membership functions could give but trying to obtain more compact models. In this way, we propose the use of multi-objective evolutionary algorithms as a tool to get almost one improved solution with respect to a classic single objective approach (a solution that could dominate the one obtained by such algorithm in terms of the system error and number of rules). To do that, this work presents and analyzes the application of six different multi-objective evolutionary algorithms to obtain simpler and still accurate linguistic fuzzy models by performing rule selection and a tuning of the membership functions. The results on two different scenarios show that the use of expert knowledge in the algorithm design process significantly improves the search ability of these algorithms and that they are able to improve both objectives together, obtaining more accurate and at the same time simpler models with respect to the single objective based approach.

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Boosting the confidence of new product development teams: The role of team boundary spanning,team size and functional diversity

In today's highly interconnected, uncertain and dynamic business environment, team boundary spanning has become an important determinant of the performance of new product development (NPD) projects. Despite the positive evidence supporting the use of boundary spanning activities by NPD teams, little is still known about how boundary spanning teams become high-performance teams. The current study advances research on this subject by examining the mediating effect of team potency on the relationship between team boundary spanning and new product performance, as well as the moderating effects of team size and functional diversity on the relationship between team boundary spanning and team potency. Data from a time-lagged survey study of 140 NPD projects found that team boundary spanning can promote team potency that, in turn, results in greater new product quality and new product creativity. The positive effect of team boundary spanning on team potency was found to be more pronounced for NPD teams of medium size and high levels of functional diversity.     

Fuzzy Adaptive Control Law for Trajectory Tracking Based on a Fuzzy Adaptive Neural PID Controller of a Multi-rotor Unmanned Aerial Vehicle

International Journal of Control, Automation and Systems - This article presents a fuzzy adaptive control law (FACL) designed for tracking the trajectory of a low-scale unmanned aerial vehicle...