A decision support system for disaster prevention in Urban Areas

This paper presents the use of Human Behavior Modeling for Disaster Relief and Emergency Management. The authors propose an innovative MS2G (Modeling, Interoperable Simulation and Serious Game) using Intelligent Agents to reproduce a complex scenario used for Verification, Validation and Accreditation of the approach. The case study is inspired to South Sudan situation and to the necessity to provide accommodations, food, health care services, security and administrative support to a large number of IDPs (Internally Displaced Persons) over a wide area. The simulator includes camp preparation and installation, air dr ops, logistics network creation while the model includes populations, entities and units as well as different equipment (e.g. cargo planes, helicopters, ground units, etc.

Does institutionalization influence perceived metamemory, psychological well-being, and working-memory efficiency in Italian elders? A preliminary study

Background/Purpose: This study was mainly aimed at investigating the impact of institutionalization on working-memory and self-referent metamemory abilities in a sample of cognitively healthy Italian elders. Methods: Fifteen participants (70-91 years old) were recruited from several nursing homes located in Ogliastra, the central eastern area of Sardinia, which is characterized by a higher longevity of its inhabitants. A further sample of 15 community-dwelling elders was recruited in the same areas. The participants were asked to complete several visuospatial and verbal working-memory tasks, and a battery of questionnaires assessing their psychological well-being, general beliefs about global and prospective-memory efficiency, and personal metamnestic abilities. Results: The results showed that, compared with the community-dwelling participants, the institutionalized elders self-rated lower metamemory efficiency, but they trust more general metamemory functions of a stereotypical adult. Furthermore, no differences were found on the well-being measures between the two groups. These outcomes are not biased by social desirability. Conclusion: These findings suggest that institutionalization selectively impacts self-assessed metamemory functions, but not psychological well-being

Predicting Failure Probability in Industry 4.0 Production Systems: A Workload-Based Prognostic Model for Maintenance Planning

Maintenance of equipment is a crucial issue in almost all industrial sectors as it impacts the quality, safety, and productivity of any manufacturing system. Additionally, frequent production rescheduling due to unplanned and unintended interruptions can be very time consuming, especially in the case of centrally controlled systems. Therefore, the ability to estimate the likelihood that a monitored machine will successfully complete a predefined workload, taking into account both historical data from the machine’s sensors and the impending workload, may be essential in supporting a new approach to scheduling activities in an Industry 4.0 production system. This study proposes a novel approach for integrating machine workload information into a well-established PHM algorithm for Industry 4.0, with the aim of improving maintenance strategies in the manufacturing process. The proposed approach utilises a logistic regression model to assess the health condition of equipment and a neural network computational model to estimate its failure probability according to the scheduled workloads. Results from a prototypal case study showed that this approach leads to an improvement in the prediction of the likelihood of completing a scheduled job, resulting in improved autonomy of CPSs in accepting or declining scheduled jobs based on their forecasted health state, and a reduction in maintenance costs while maximising the utilisation of production resources. In conclusion, this study is beneficial for the present research community as it extends the traditional condition-based maintenance diagnostic approach by introducing prognostic capabilities at the plant shop floor, fully leveraging the key enabling technologies of Industry 4.0

Multipass SAR interferometry. A tool for geologic analysis

This paper investigates how the information content of repeat pass satellite SAR interferometric (INSAR) data can be used to provide the geologist with a tool which can improve his ability and efficacy in the geologic analysis of SAR imagery. INSAR processing produces interferometric fringes, coherence and amplitude images. To produce an interferometric DEM phase unwrapping is a critical step. For phase unwrapping, we propose the WLMS (Weighted Least Mean Square) estimation of the phase, which is a generalization of the least-mean square method. The crucial step in WLMS approach is the weighting procedure. We propose a weighting algorithm based on the fusion of a priori information extracted from different interferometric products. These different information channels—DEM, amplitude and coherence—can be effectively fused to convey information to the geologic interpreter using 3D stereoscopic visualization;SAR stereo pairs were artificially generated using the interferometric DEM and the intensity image or the coherence image of the area overlaid. In order to ascertain the performance of the procedure a number of tests were carried out over various sites in Matese (Southern Italy), which has a fairly demanding topography, using ERS SAR tandem data. The results demonstrate that WLMS unwrapping method is sufficiently robust in capturing the morphology of the area and that stereoscopic visualization greatly facilitates geologic interpretation and the observation of detailed features of the terrain

Editorial: Computational Approaches for Human-Human and Human-Robot Social Interactions

Non-verbal behaviors such as gaze, facial expressions, gestures, and vocal behavior carry significant information regarding human personality, emotions, engagement, intentions, action goals, and focus of attention. A large part of human communication takes place non-verbally (and often implicitly) during an explicit exchange of thoughts, attitudes, concerns, and feelings. Analyzing the basic principles of human communication through non-verbal signals is a long-standing research focus in cognitive and social psychology. However, the automatic realization of such analyses, especially by using machine learning (ML), or, in general, computational techniques, is a relatively unexplored avenue, although these techniques can be very efficient and effective

Capacitated vehicle routing problem with time windows: A linear model and a case study of express courier

Given the importance gained by the e-commerce field in the recent years, this study investigates the issue of minimizing the delivery travel time of a real company located in the South of Italy and operating as a courier, express and parcel (CEP) service provider. The scenario under examination consists of a depot, three vehicles and several customers served by the CEP company. A Capacitated Vehicle Routing Problem with Time Windows (CVRPTW) model is formulated to optimize the deliveries to the customers for the targeted company and solved under the commercial software IBM ILOG CPLEX Optimization Studio. As outcomes, the model returns a simulated path covered by the vehicles and computes the corresponding travel time. Results show that with the proposed formulation, the time windows (TWs) of all customers are respected. Because the analysis is grounded on a real company, the results are expected to provide practical indications to logistics and supply chain managers, to maximize the performance of their delivery system

Framework for selecting manufacturing simulation software in industry 4.0 environment

Even though the use of simulation software packages is widespread in industrial and manufacturing companies, the criteria and methods proposed in the scientific literature to evaluate them do not adequately help companies in identifying a package able to enhance the efficiency of their production system. Hence, the main objective of this paper is to develop a framework to guide companies in choosing the most suitable manufacturing simulation software package. The evaluation framework developed in this study is based on two different multi-criteria methods: analytic hierarchy process (AHP) integrated with benefits, opportunities, costs, risks (BOCR) analysis and the best-worst method (BWM). The framework was developed on the basis of the suggestions from the literature and from a panel of experts, both from academia and industry, trying to capture all the facets of the software selection problem. For testing purposes, the proposed approach was applied to a mid-sized enterprise located in the south of Italy, which was facing the problem of buying an effective simulation software for Participatory Design. From a practical perspective, the application showed that the framework is effective in identifying the most suitable simulation software package according to the needs of the company. From a theoretical point of view, the multi-criteria methods suggested in the framework have never been applied to the problem of selecting simulation software; their usage in this context could bring some advantages compared to other decision-making tools

Classification of schizophrenia using feature-based morphometry

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