Business Intelligence Acceptance: The Prominence of Organizational Factors

The specifics of business intelligence systems compared to operational IS motivate the necessity to research the business intelligence systems acceptance determinants separately. The authors followed an exploratory approach in order to conceptualize a business intelligence acceptance model. Their findings show that in the Business Intelligence Systems context, there is a significant emphasis on organizational factors, such as result demonstrability, social influence, and facilitating conditions with sufficient resources that help build an adequate information culture all substantially influencing the effective acceptance of business intelligence systems.     

Limitations of a Mapping Algorithm with Fragmentation Mimics (MAFM) when modeling statistical data sources based on measured packet network traffic

Knowing the estimation of a statistical process’s parameters for measured network traffic is very important as it can then be further used for the statistical analyses and modeling of network traffic in simulation tools. It is for this reason that different estimation methods are proposed that allow estimations of the statistical processes of network traffic. One of them is our own histograms comparison (EMHC) based method that can be used to estimate statistical data-length process parameters from measured packet traffic. The main part of EMHC method is Mapping Algorithm with Fragmentation Mimics (MAFM).     

Disquiet in the plasma

The metaphor of plasma is taken up to present and discuss movement and engagement by participants in mixed reality installation arts. Two works involving full body video portraits exhibited through large plasma screens in a variety of public settings are covered. Machinic mediations of video realism are considered in terms of embodied interaction in which viewer-participants contribute to the ‘disquiet’ of gendered figuring. Processural, proximal and personal aspects of responsive engagement are discussed. This is extended to performativity that may lead us to critical reflection of our own actions and responses in mixed reality arts.     

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.