Abstract
The choice of a suitable method for object manipulation is one of the most critical aspects of virtual environment design. It has been shown that different environments or applications might benefit from direct manipulation approaches, while others might be more usable with indirect ones, exploiting, for example, three dimensional virtual widgets. When it comes to mid-Air interactions, the success of a manipulation technique is not only defined by the kind of application but also by the hardware setup, especially when specific restrictions exist. In this paper we present an experimental evaluation of different techniques and hardware for mid-Air object manipulation in immersive virtual environments (IVE). We compared task performances using both deviceless and device-based tracking solutions, combined with direct and widget-based approaches. We also tested, in the case of freehand manipulation, the effects of different visual feedback, comparing the use of a realistic virtual hand rendering with a simple cursor-like visualization.

Abstract
Current immersive modeling environments use non-natural tools and interfaces to support traditional shape manipulation operations. In the future, we expect the availability of natural methods of interaction with 3D models in immersive environments to become increasingly important in several industrial applications. In this paper, we present a study conducted on a group of potential users with the aim of verifying if there is a common strategy in gestural and vocal interaction in immersive environments when the objective is modifying a 3D shape model. The results indicate that users adopt different strategies to perform the different tasks but in the execution of a specific activity it is possible to identify a set of similar and recurrent gestures. In general, the gestures made are physically plausible. During the experiment, the vocal interaction was used quite rarely and never to express a command to the system but rather to better specify what the user was doing with gestures.

Abstract
Production scheduling in the presence of real-time events is of great importance for the successful implementation of real-world scheduling systems. Most manufacturing systems operate in dynamic environments vulnerable to various stochastic real-time events which continuously forces reconsideration and revision of pre-established schedules. In an uncertain environment, efficient ways to adapt current solutions to unexpected events, are preferable to solutions that soon become obsolete. This reality motivated us to develop a tool that attempts to start filling the gap between scheduling theory and practice. The developed prototype is connected to the MRP software and uses meta heuristics to generate a predictive schedule. Then, whenever disruptions happen, like arrival of new tasks or cancelation of others, the tool starts rescheduling through a dynamic-event module that combines dispatching rules that best fit the performance measures pre-classified by Kano’s model. The proposed tool was tested in an in-depth computational study with dynamic task releases and stochastic execution time. The results demonstrate the effectiveness of the model.

Abstract

Abstract
With the recent development of weblogs and social networks, many supplier industries share their data on different websites and weblogs. Even the Small-to-Medium sized enterprises (SMEs) in the manufacturing sector (as well as non-manufacturing sector) are rapidly strengthening their web presence in order to improve their visibility, customer reachability and remain competitive in the global market. Our study aims to classify data into various groups so that users can identify the most appropriate content based on their choice at any given time. To classify and characterize manufacturing suppliers in supply chain through their capability narratives and textual portfolios obtained from websites of such suppliers online source portals for testing and Naive Bayes and support vector machine (SVM) Classification method at term-level for classification has been used. The performance of the proposed classifier was tested experimentally based on the standard metrics such as precision, recall, and F-measure.

Abstract
Recent studies have shown that the early detection of neurodegenerative diseases (such as Parkinson) can significantly improve the effectiveness of treatments that increase quality of life, reducing the costs associated with the disease. In this paper, the proposed methodology consists in detecting early signs of Parkinson's disease through speech, with the presence of background noise. The approach uses machine learning algorithms and signal processing techniques to correctly distinguish between healthy controls and Parkinson's disease patients. In order to detect early signs of the disease, a database with patients at different stages of the Parkinson's disease is used. The learning algorithms were optimized for generalization and accuracy. An analysis of the results obtained from the proposed methodology show potential uses of machine learning algorithms in biomedical applications to detect early signs of Parkinson's disease.