L’Oratorio di Santa Croce di Borutta

Nel centro storico del paese di Borutta (SS), sorge l’Oratorio intitolato alla Santa Croce. Nell’edificio, risalente all’undicesimo secolo secondo i dati degli studi archeologici, sono presenti un’abside di fattezze romaniche e pregiati innesti architettonici di influenza gotico-catalana. Un certo interesse riveste la croce di consacrazione absidale, nonché gli arredi lignei, nelle forme di un altare, di un ambone e di un bel simulacro del Cristo morto.Le fonti, che testimoniano l’antico utilizzo della struttura in qualità di parrocchiale del paese intitolata a Santa Maria Maddalena, riportano la predilezione che gli ultimi vescovi di Sorres mostrarono verso la chiesa boruttese all’indomani della decadenza della villa e della sede episcopale di SorresThe Oratorio of Santa Croce is in the old town of Borutta (Sassari, ITALY). The church dates from the eleventh century - according to archaeologist. Inside the structure there are an interesting apse, Catalan gothic elements, painted cross of consecration and painted wooden furniture, in particular a simulacrum of the dead Christ. Historic documents reveal the original use of this church as the ancient parish of Borutta, but it played some way the role of cathedral after the decline of the original episcopal seat.

The stochastic multi-path traveling salesman problem with dependent random travel costs.

The objective of the stochastic multi-path Traveling Salesman Problem is to determine the expected minimum-cost Hamiltonian tour in a network characterized by the presence of different paths between each pair of nodes, given that a random travel cost with an unknown probability distribution is associated with each of these paths. Previous works have proved that this problem can be deterministically approximated when the path travel costs are independent and identically distributed. Such an approximation has been demonstrated to be of acceptable quality in terms of the estimation of an optimal solution compared to consolidated approaches such as stochastic programming with recourse, completely overcoming the computational burden of solving enormous programs exacerbated by the number of scenarios considered. Nevertheless, the hypothesis regarding the independence among the path travel costs does not hold when considering real settings. It is well known, in fact, that traffic congestion influences travel costs and creates dependence among them. In this paper, we demonstrate that the independence assumption can be relaxed and a deterministic approximation of the stochastic multi-path Traveling Salesman Problem can be derived by assuming just asymptotically independent travel costs. We also demonstrate that this deterministic approximation has strong operational implications because it allows the consideration of realistic traffic models. Computational tests on extensive sets of random and realistic instances indicate the excellent efficiency and accuracy of the deterministic approximation

Comparative analysis of models and performance indicators for optimal service facility location

This study investigates the optimal process for locating generic service facilities by applying and comparing several well-known basic models from the literature. At a strategic level, we emphasize that selecting the right location model to use could result in a problematic and possibly misleading task if not supported by appropriate quantitative analysis. For this reason, we propose a general methodological framework to analyze and compare the solutions provided by several models to obtain a comprehensive evaluation of the location decisions from several different perspectives. Therefore, a battery of key performance indicators (KPIs) has been developed and calculated for the different models’ solutions. Additional insights into the decision process have been obtained through a comparative analysis. The indicators involve topological, coverage, equity, robustness, dispersion, and accessibility aspects. Moreover, a specific part of the analysis is devoted to progressive location interventions over time and identifying core location decisions. Results on randomly generated instances, which simulate areas characterized by realistic geographical or demographic features, are reported to analyze the models’ behavior in different settings and demonstrate the methodology’s general applicability. Our experimental campaign shows that the p-median model behaves very well against the proposed KPIs. In contrast, the maximal covering problem and some proposed back-up coverage models return very robust solutions when the location plan is implemented through several progressive interventions over time

Reinforcement Learning Algorithms for Online Single-Machine Scheduling.

Online scheduling has been an attractive field of research for over three decades. Some recent developments suggest that Reinforcement Learning (RL) techniques can effectively deal with online scheduling issues. Driven by an industrial application, in this paper we apply four of the most important RL techniques, namely Q-learning, Sarsa, Watkins’s Q(lambda), and Sarsa(lambda), to the online single-machine scheduling problem. Our main goal is to provide insights into how such techniques perform in the scheduling process. We will consider the minimization of two different and widely used objective functions: the total tardiness and the total earliness and tardiness of the jobs. The computational experiments show that Watkins’s Q(lambda) performs best in minimizing the total tardiness. At the same time, it seems that the RL approaches are not very effective in minimizing the total earliness and tardiness over large time horizons

Reinforcement Learning Algorithms for Online Single-Machine Scheduling.

Online scheduling has been an attractive field of research for over three decades. Some recent developments suggest that Reinforcement Learning (RL) techniques can effectively deal with online scheduling issues. Driven by an industrial application, in this paper we apply four of the most important RL techniques, namely Q-learning, Sarsa, Watkins’s Q(lambda), and Sarsa(lambda), to the online single-machine scheduling problem. Our main goal is to provide insights into how such techniques perform in the scheduling process. We will consider the minimization of two different and widely used objective functions: the total tardiness and the total earliness and tardiness of the jobs. The computational experiments show that Watkins’s Q(lambda) performs best in minimizing the total tardiness. At the same time, it seems that the RL approaches are not very effective in minimizing the total earliness and tardiness over large time horizons

A chance-constraint approach for optimizing social engagement-based services

Social engagement is a novel business model whose goal is transforming final users of a service from passive components into active ones. In this framework, people are contacted by the decision-maker (generally a company) and they are asked to perform tasks in exchange for a reward. This paves the way to the interesting optimization problem of allocating the different types of workforce so as to minimize costs. Despite this problem has been investigated within the operations research community, there is no model that allows to solve it by explicitly and appropriately modeling the behavior of contacted candidates through consolidated concepts from utility theory. This work aims at filling this gap. We propose a stochastic optimization model including a chance constraint that puts in relation, under probabilistic terms, the candidate willingness to accept a task and the reward actually offered by the decision-maker. The proposed model aims at optimally deciding which user to contact, the amount of the reward proposed, and how many employees to use in order to minimize the total expected costs of the operations. A solution approach is proposed to address the formulated stochastic optimization problem and its computational efficiency and effectiveness are investigated through an extensive set of computational experiment

A Social Internet of Things Smart City Solution for Traffic and Pollution Monitoring in Cagliari

In the last years, the smart city (SC) paradigm has been deeply studied to support sustainable mobility and to improve human living conditions. In this context, a new SC based on the Social Internet of Things paradigm is presented in this article. Starting from the tracking of all vehicles (that is, private and public) and pedestrians, integrated with air quality measurements (that is, in real time by mobile and fixed sensors), the system aims to improve the viability of the city, both for pedestrian and vehicular users. A monitoring network based on sensors and devices hosted on board in local public transport allows real-time monitoring of the most sensitive areas both from traffic congestion and from an environmental point of view. The proposed solution is equipped with an appropriate intelligence that takes into account instantaneous speed, type of traffic, and instantaneous pollution data, allowing to evaluate the congestion and pollution condition in a specific moment. Moreover, specific tools support the decisions of public administration facilitating the identification of the most appropriate actions for the implementation of effective policies relating to mobility. All collected data are elaborated in real time to improve traffic viability suggesting new directions and information to citizens to better organize how to live in the city

Co-design of human-centered, explainable AI for clinical decision support

eXplainable AI (XAI) involves two intertwined but separate challenges: the development of techniques to extract explanations from black-box AI models, and the way such explanations are presented to users, i.e., the explanation user interface. Despite its importance, the second aspect has received limited attention so far in the literature. Effective AI explanation interfaces are fundamental for allowing human decision-makers to take advantage and oversee high-risk AI systems effectively. Following an iterative design approach, we present the first cycle of prototyping-testing-redesigning of an explainable AI technique, and its explanation user interface for clinical Decision Support Systems (DSS). We first present an XAI technique that meets the technical requirements of the healthcare domain: sequential, ontology-linked patient data, and multi-label classification tasks. We demonstrate its applicability to explain a clinical DSS, and we design a first prototype of an explanation user interface. Next, we test such a prototype with healthcare providers and collect their feedback, with a two-fold outcome: first, we obtain evidence that explanations increase users’ trust in the XAI system, and second, we obtain useful insights on the perceived deficiencies of their interaction with the system, so that we can re-design a better, more human-centered explanation interface