Computational Science and Its Applications – ICCSA 2017

A theoretical study of some acetone -water solutions is performed considering a total number of molecules equal to 1000. A force field for the water-acetone interaction is presented. To this purpose we have considered four interaction centers on the acetone molecule and only one on the small water molecule. Then, the non electrostatic intermolecular interaction between pairs of interaction centers placed on different molecules has been modeled adopting Improved Lennard-Jones (ILJ) functions, which sum has been combined with the electrostatic interaction contribution (derived from the charge distributions on the molecules). The potential model has been used to perform some preliminary Molecular Dynamic simulations of the density at 298.15 K of temperature and 1 atm of pressure for two different values of the acetone molar fractions, x$$_{\mathrm {acet}}$$ = 0.745 and 0.986. The systems, formed by 1000 molecules, were previously thermalized at the selected temperature using the NVT ensemble. Production runs have been performed from the equilibrated systems using the NpT ensemble.

The paper deals with the synchronization mechanism among the servers of a federation of distributed repositories for the constant updating of the didactic-scientific material, its properties and its locations. A shared metadata database is the synchronization point of reference and it allows to improve performance in terms of searching and downloading. The proposed federation is meant to deal with a large variety of different contents though the discussed prototype implementation is concerned with scientific and educational subjects in particular. Additional elements of evaluation are the capability of enhancing collaboration and fault tolerance.

The Open Science Cloud project is getting off the ground within EINFRA 12 (a) under the joint action of EGI.eu, INDICO and EUDAT. To this end several pilots have been selected to provide its core services. In this paper, the characteristics of the (not selected for funding) Open Molecular Science pilot SUMO-Chem are described and further referring to two particularly innovative services: the distributed ab initio collaborative simulator of molecular processes GEMS and the prosumer based self evaluation handler of molecular knowledge e-tests.

This work reports the detection and identification of volatile chemical compounds in fruit kernel of “Lodoicea maldivica” coco nucifera palm by gas chromatographic method. The analysis was performed by HS-SPME and GC/MS techniques to determine volatile aroma composition profiles in internal and external pulp. No qualitative differences in flavor composition were observed between two pulp parts, but there were notable variations in the abundance levels of the prominent compounds. Computational method was used to extract individual component mass spectra from GC/MS data files by using the AMDIS version 2.65 software. With such a procedure we are able to construct our own mass spectra library determining retention indices for chemical compounds of our interest in the used specific experimental conditions.

The Grid Empowered Molecular Simulator GEMS enabling fully ab initio virtual experiments through rigorous theoretical and computational procedures has been upgraded with a novel scheme for automated generation of three-atom potential energy surfaces. The scheme is based on a space-reduced formulation of the so-called bond-order variables allowing for a balanced representation of the attractive and repulsive regions of a diatom configuration space. The deployment and use of the resulting upgraded machinery on distributed and cloud computing infrastructures is also discussed.

Radiation therapy is one of the main treatment modalities for cancer. The objective of radiation therapy is to eliminate all cancer cells by delivering a prescribed dose of radiation to the tumor volume while sparing at the same time the surrounding tissues. Intensity-modulated radiation therapy (IMRT) is a sophisticated technologically-driven type of radiation therapy where non-uniform radiation fields are used to irradiate the patient from different beam angle directions. Appropriate selection of beam irradiation directions – beam angle optimization (BAO) problem – enhance the quality of the treatment plan. The BAO problem is a very difficult global non-convex optimization problem for which there are few or none commercial solutions. Typically, the BAO procedure is driven by the outcome of the fluence map optimization (FMO) problem – the problem of calculating the most adequate radiation intensities. However, functions used for modeling the FMO problem have little clinical meaning. Typically, selection/validation of treatment plans is done considering a set of dosimetric measures. In this study, we propose a treatment plan global score, based on dosimetric criteria and its relative importance, as alternative plan’s quality measure to drive the BAO procedure. For the clinical case of nasopharyngeal tumor, the use of a global score to drive the BAO procedure lead to higher quality treatment plans. For similar target coverage, an improved organ sparing was obtained.

Radiotherapy is one of the treatments available for cancer patients, aiming to irradiate the tumor while preserving healthy structures. The planning of a treatment is a lengthy trial and error procedure, where treatment parameters are iteratively changed and the delivered dose is calculated to see whether it complies with the desired medical prescription. In this paper, a procedure based on fuzzy inference systems (FIS) for automated treatment planning is developed, allowing the calculation of high quality treatment plans without requiring human intervention. The procedure is structured in two different phases, incorporating the automatic selection of the best set of equidistant beam irradiation directions by an enumeration procedure. The developed method is extensively tested using ten head-and-neck cancer cases.

A practical comparison of penalty functions for globally solving mixed-integer nonlinear programming (MINLP) problems is presented. The penalty approach relies on the continuous relaxation of the MINLP problem by adding a specific penalty term to the objective function. A new penalty algorithm that addresses simultaneously the reduction of the error tolerances for optimality and feasibility, as well as the reduction of the penalty parameter, is designed. Several penalty terms are tested and different penalty parameter update schemes are analyzed. The continuous nonlinear optimization problem is solved by the deterministic DIRECT optimizer. The numerical experiments show that the quality of the produced solutions are satisfactory and that the selected penalties have different performances in terms of efficiency and robustness.

In this work we present an algorithm that combines the filter technique and the dynamically dimensioned search (DDS) for solving nonlinear and nonconvex constrained global optimization problems. The DDS is a stochastic global algorithm for solving bound constrained problems that in each iteration generates a randomly trial point perturbing some coordinates of the current best point. The filter technique controls the progress related to optimality and feasibility defining a forbidden region of points refused by the algorithm. This region can be given by the flat or slanting filter rule. The proposed algorithm does not compute or approximate any derivatives of the objective and constraint functions. Preliminary experiments show that the proposed algorithm gives competitive results when compared with other methods.

The provision of home health care services is becoming an important research area, mainly because in Portugal the population is ageing. Home care visits are organized taking into account the medical treatments and general support that elder/sick people need at home. This health service can be provided by nurse teams from Health Care Centers. Usually, the visits are manually planned and without computer support. The main goal of this work is to carry out the automatic schedule of home care visits, of one Portuguese Health Care Center, in order to minimize the time spent in all home care visits and, consequently, reduce the costs involved. The developed algorithms were coded in MatLab Software and the problem was efficiently solved, obtaining several schedule solutions of home care visits for the presented data. Solutions found by genetic and particle swarm algorithms lead to significant time reductions for both nurse teams and patients.

Metaheuristics define and explore a set of different neighborhoods, in general, adapted to specific characteristics of a problem. The quality of the solution found relies on the efficiency of the neighborhood used on the local search phase, therefore it is very important to research about the movements or combination of them which compose the neighborhood structure. This paper is based on a recent work reported on literature that deals with four standard movements for the university timetabling problem. This work complements the analysis already done so far, adding five new movements widely known in the literature. Two of then are specific for the restrictions adopted by the curriculum-based formulation proposed on the Second International Timetabling Competition (ITC-2007). The Steepest Descent (SD) algorithm was implemented to study each movement separately and combined. This analysis shows that the quality of the solutions is highly affected by the movements chosen, since the ratio between the worst and best solution (in terms of objective function value), can be up to 13.5.

Branch and bound (BnB) Global Optimization algorithms can be used to find the global optimum (minimum) of a multiextremal function over the unit hypercube and unit simplex with a guaranteed accuracy. Subdivision strategies can take the information of the evaluated points into account leading to irregular shaped subsets. This study focuses on the passive generation of spatial subdivisions aiming at evaluating points on a predefined grid. The efficiency measure is in terms of the complete tree size, or worst case BnB scenario, with a termination criterion on the subset size. Longest edge bisection is used as a benchmark. It is shown that taking the grid for a given termination tolerance into account, other general partitions exist that improve the BnB upper bound on the number of evaluated points and subsets.

Identification of a key indicators capturing essential patterns in a region can be a cost-effective solution for neighborhood classification and targeted policy making. Yet, such a “core” set of indicators can vary from region to region. Here, we define set of indicators measuring education, housing, accessibility, and employment which can be used to classify neighborhoods. We test these indicators in two study regions: the Baltimore Metropolitan Area and the Greater Dublin Region. We apply factor analysis to distill indicators to smaller sets that capture differences in neighborhood types in terms of social, economic, and environmental dimensions. We use factors loadings in cluster analyzes to identify unique neighborhood types spatially. Comparison of the core set of indicators and clustering patterns for case study regions sheds new lights on the important factors for both regions. The proposed approach will help compare variations in neighborhood types between and within different regions internationally.

The appraisal objectivity depends on the possibility to quickly and easily access to reliable real estate data in order to apply appropriate appraisal approaches. In order to ensure the objectivity of the real estate appraisals, in recent years Automated Valuation Methods (AVM) have been developed, integrating computerized real estate databases and programming languages. The Automated Valuation Methods proposed at international level usually recur to regression models, aimed to return appraisal equations based on reliable real estate databases. This approach is not applicable in some markets where lack of data does not allow the implementation of regression models. This paper proposes to implement a valuation automatic method in order to appraise properties located in atypical markets, structuring a procedural algorithm based on the mono-parametric approach and able to return punctual values related to the subject’s specifics and to the market peculiarities in a very limited area. The paper proposes also the application of similarity degree coefficients in order to take into account the differences between the amounts of the real estate features, leading to the possibility to use the mono-parametric approach also when lack of data would not recommend it.

Over the years, city and territory have been transformed disproportionately and the planning appears to have lost control over the dynamics of the events that, unfortunately, have caused indelible changes on urban and environmental systems. The reworking of alphanumeric and geographic data through the territorial information system can be useful as technical support and methodological knowledge capable of intervening and redeveloping the residential centers. In fact, cities and their dynamics can be visualized with tools and machinery capable of representing them in a graphic or schematic way. This study presents some Italian experiences of urban renewal made possible by territorial information systems. The achieved results attest that technological innovation represents a great opportunity for planning increasingly smart cities, in addition to having a greater influence on redevelopment processes in the government of the territory and improving services distribution and the decision making process.

The current paper presents the experience of mapping pre-war Jewish communities in pre-war central Poland. While going through a period of intensive social and cultural transformations these communities may be considered a prototype of contemporary complex societies. The initial analytical framework, provided thanks to the GIS database and concatenation of attributes coming from various sources, makes some initial observations and conclusions possible. It confirms the thesis that Jewish communities in pre-war Poland may be considered an example of a self-organising society, one which could be considered a prototype of contemporary postmodern cultural complexity. The current study provides the initial framework to map the morphology and spatial distribution of the complexity of everyday culture of use of space proper to this extremely diverse group.

The digital revolution is changing the space and the concept of cultural heritage. Furthermore, mobile devices – thanks to geolocalization, augmented/virtual reality, ubiquitous and multimodal interactions – transform the cultural storytelling in a pervasive and ongoing experience crossing seamlessly the boundaries between places of preservation and the historical remains spread in a territory. The paper proposes a design experience which explores the historical layering and evolution of the city of Milano through an interactive time machine. The cultural key chosen to read the historical development is the Manzoni’s novel The Betrothed. According to the literary interpretation, three key periods have been explored along with the evolution of the urban representation. The historical period of the novel (XVII century) corresponding to the map drawn by Cartaro in 1581. Milan at the time of the author (1785–1873) before the Beruto’s master plan that brought down the Spanish walls. And the contemporary city, where the novel itineraries are still recognizable. The time machine – the core features of the app PS 3.0 – is a dynamic way to visualize and experience the geo-referenced point of interests of the cultural paths that allow people to discover past in the present in a spatially-situated interaction.

In the age of Big Data, the lack of relevant data, information and knowledge and the limits of the instruments and legislation for Risk Prevention and Management (RPM) do not allow decision makers to act efficiently and in a participatory way in territorial management. The task of this paper is to find a way to foster a proactive coordination between RPM instruments, practices and stakeholders, in order to identify consistent policy choices. A territorial organization model is defined, the Observatory for Territorial Participation (OTP), based on the following tasks: involvement of local actors; use of interactive computerized techniques and tools for decision support; access and sharing of big data and information in real time. The use of a “strategic planning” software, tested within a real setting, not only helps to focus the discussion and the process of definition of RPM policies, but it also leads at possible strategic organizational paths in the short, medium and long run.

The current digital revolution has a strong impact on the people life and on the research process in most of the scientific areas. Geography and cartography have been invested by the innovation connected with GIS. Moreover, they reacted to the new opportunities given by the new web applications founded on the dynamic paradigm of Web 2.0. In the geographic framework the possibility to self-supply the online contents brought to the Volunteered Geographic Information phenomenon. This study presents a number of preliminary results of a project aimed at reformulating the theoretical framework of Photographic Tourism. The research focuses on the integration of social media and web mapping as an application of neogeography. The estimation (based on a sample) of the proportion of geotagged photos loaded on Flickr during 2016 and the analysis of the spatial distribution of the considered sample are the main objectives of the paper.

The paper introduce the rapid development of the “smart city” concept and its different meanings through a critical review of main bibliographical references: from the original concept of intelligent, digital and creative city, to the recent one of human smart city and SENSEable city, until to the integrated approach, who reads the “smartness” as condition for a continuous and ongoing process of growth and innovation [35]. In Europe, and also in Italy, the application of this concept has been mainly restricted to the urban scale, lacking a broader strategic and territorial vision. Starting with some reflections already developed by the authors on the subject [36], the article proposes a change of scale in the approach to the topic of the “smart city” (from urban scale to wide scale), which implies not only a conceptual transition (Smart city to Smart city-region), but the definition of completely different models and strategies.The analysis considers the key role that the Europe’s Growth Agenda recognizes to Regions in the development of new growth dynamic for Europe based on the definition of smart specialization strategies, and focuses on importance of identifying an Italian approach - an Italian road map - to smart cities, based on local potentialities. Finally, in the perspective of a new mutual balance between City and Region is presented the experience (to be defined) of the Northern global city-region Milano-Torino.

Volunteered geographic information (VGI), which pertains to geographic information voluntarily collected and shared, represents a paradigm shift in the way geographic information is created and shared. However, there are still hurdles in properly using such information. One alternative to improve the use of VGI is to use semantic enrichment. Semantic enrichment is a potential way of mitigating several issues that plague VGI such as low data quality, unreliability, and difficulty in use and recovery, among others. The present study discusses the possibility of semantically enriching volunteered geographic data using Linked Data and presents a simplified algorithm to automate this process.

In the last decades Ho Chi Minh City (HCMC), Vietnam, has experienced a rapid urbanization. This town grows from 2.3 millions of inhabitants in the 1975 until 8 millions in the 2014. Therefore, understanding the land-cover changes is one of the main topics in order to monitor the process of urban development. In this paper we study the urban dynamics of HCMC using two Landsat scenes acquired on June of 1988 (sensor TM) and on February 2017 (sensor OLI/TIRS). We relate our main results with other sources, like demographic data and the participative maps of OpenStreetMap. So, we identify main land cover changes in this period, such as the most important demographic variations within the urban districts of HCMC.

Sustainable urban planning has to maintain an adequate relationship between demographic dynamics and the development of physical urban structures. The discrepancy between potential urban land capacities and actual population data indicates demographic growth or shrinkage, which needs to be considered as an essential issue of urban strategic planning. In order to maintain proper population densities, one of the key challenges is the establishment of reliable and quick methods to quantify both potential and real densities. The current paper reviews available methods of density calculations with a focus on possibilities to integrate GIS tools. It uses an assessment matrix to evaluate the advantages and disadvantages of each method. A fresh look is given to the usage of urban morphology, which is particularly efficient at the small-scale urban areas and in situations of data scarcity.

In the context of cites, a very innovative approach refers to the theory of urban resilience, which is represented by the ability of an urban system to absorb, adapt and respond to stresses and strains, including issues related to sustainability, governance and economic development. The paper aims at exploring the problems related to urban resilience, with specific attention to the use of Fuzzy Cognitive Maps (FCMs) which constitute a recent approach for representing complex systems and for supporting scenario planning and strategic decision making. Starting from a real case related to the regeneration program of the city of Collegno (Italy), the paper illustrates the application of the FCM method for modeling urban resilience dynamics and for exploring future scenarios of transformation.

In this work, seismic risk assessment of hospitals in Lima city is presented. The aim of this paper is to evaluate the seismic risk of the Lima’s health system at Metropolitan scale. Seismic Risk Analysis has been carried out utilizing the Comprehensive Approach for Probabilistic Risk Assessment methodology (CAPRA). Geographic Information System (GIS) tools have been used to model the risk components: hazard, exposure and vulnerability. A total of 41 hospitals have been evaluated in order to obtain risk indicators in terms of economic losses. The results show that after an 8.2 magnitude earthquake with an epicenter in the coast of Lima, 85% of hospital buildings have more than 10% of structural damage. Furthermore, the expected annual loss (EAL) is 2% of the replacement value, which is high and uncompetitive compared with the values of the insurance sector market. This study seeks to understand and evaluate the hospitals seismic risk at metropolitan level in order to define measures, priorities and actions for the future development of Lima’s health system.

The purpose of this paper is to identify environmental issues related to the geo-resources demand, which arises by the new context of the metropolitan city of Cagliari, in the framework of the existing environmental and place-based policies. This is achieved through the correlation of the main planning tools (the Regional Plan of Extractive Activities [RPEA] for geo-resources planning sector, and the urban masterplan [UMP] for urban planning sector), in order to identify environmental indicators, useful for monitoring and for decision support systems. This comparison defined a new integrated methodological approach between urban, place-based and environmental policies, referring to geo-resources planning, in line with the newest paradigm of smart region and of the panarchy process. This approach allowed to evaluate the delayed impacts of UMPs, and its environmental impacts, resulting from the quarry activities of geo-resources (such as natural aggregates). In fact, quarry activity is the leading effect in UMPs (because the materials of construction are obtained principally by it), and it simultaneously decreases the environmental sustainability, increasing the environmental debt.

According to several studies about touristic practices, we propose an adaptive method to measure the Quality of Experience (QoE) of a personalized sightseeing tour which compares a proposed tour (sequence of points of interest and activities) to a reference tour and gives a correspondence score based on two complementary measures: the cultural interest and the journey rhythm (related to the profile of the tourist group). The method is applied to provide tours for the largest theater festival in Europe, which is held every year in Avignon (France).

Recent seismic event worldwide proved how fragile the electric power system can be to seismic events. Decision Support Systems (DSSs) could have a critical role in assessing the seismic risk of electric power networks and in enabling asset managers to test the effectiveness of alternative mitigation strategies and investments on resilience. This paper exemplifies the potentialities of CIPCast, a DSS recently created in the framework of the EU-funded project CIPRNet, to perform such tasks. CIPCast enables to perform risk assessment for Critical Infrastructures (CI) when subjected to different natural hazards, including earthquakes. An ad-hoc customization of CIPCast for the seismic risk analysis and management of electric power networks is featured in this paper. The international literature describes effective and sound efforts towards the creation of software platforms and frameworks for the assessment of seismic risk of electric power networks. None of them, unfortunately, achieved the goal of creating a user-friendly and ready available DDS to be used by asset managers, local authorities and civil protection departments. Towards that and building on the international literature, the paper describes metrics and methods to be integrated within CIPCast for assessing the earthquake-induced physical and functional impacts of the electric power network at component and system level. The paper describes also how CIPCast can inform the service restoration process.

Smart City projects are moving from trials to complete Smart City realizations. Smart Cities must work as complex ecosystems of interoperable and composable services yet there is currently a proliferation of less than interoperable and portable vertical services. To diminish the barriers among these silos different approaches have been attempted but no single one of them has garnered general acceptance and adoption. The international initiative Internet of Things Enabled Smart City Framework (IES-City) convenes a broad set of stakeholders to build a consensus foundation of architectural principles for interoperable Smart Cities. IES-City evaluates global existing frameworks, tools and applications to distil a common set of Pivotal Points of Interoperability (PPI). PPI have the potential to enable both interoperability and suitable variation and reduce barriers to composable Smart City deployments. The IES-City concept is that such PPI exist in practice and need only be discovered. This paper describes the IES-City methodology.

The modern cities are addressing their innovation efforts for facing not just the common stresses cities accumulate daily, but also the sudden shocks can occur such as urban floods. Networked gauge stations are instrumental to robust floods alerts though they suffer from error and fault. For capturing the anomalous behavior of networked rain gauges, the use of an online anomaly detection methodology, based on the Support Vector Regression (SVR) technique, has here been investigated and developed. The specific anomaly case of incorrectly zero sensor readings has been efficiently addressed by a centralized architecture and a prior-knowledge free approach based on SVRs that simulate the normality profile of the networked rain gauges, on the basis of the spatial-temporal correlation existing among the observed rainfall data. Real data from the pilot rain gauge network deployed in Calabria (South Italy) have been used for simulating the anomalous sensor readings. As a result, we conclude that SVR-based anomaly detection on networked rain gauges is appropriate, detecting the eventual rain gauge fault effectively during the rainfall event and by passing through increased alert states (green, yellow, orange, red).

Machine learning techniques will take an increasingly central role in the distributed sensing realm and specifically in smart cities scenarios. Pervasive air quality monitoring as one of the primary city service requires a significant effort in term of data processing for extracting the needed semantic value. In this paper, after briefly reviewing the emerging relevant literature, we compare several machine learning tools for the purpose of devising intelligent calibration components to be run on board or in cloud computing architectures for pollutant concentration estimation. Two cities field experiments provide the needed on field recorded datasets to validate the approaches. Results are discussed both in terms of performance and computational impact for the specific application.

By 2050 the world population will grow to about 9 billion contributing to deep changes in urban areas structure. This would increase the effect of water deficiency and along with projected climate changes the impact of urban floodings, urban heat islands or drought. Smart cities could be key part of the solution contributing to improve the quality life of citizen in urban areas with the adoption of smart, intelligent technologies and infrastructure for energy, water, mobility, buildings, and government. The concept of smart water refers to the ability to provide and manage this primary resource in quantitative and qualitative terms in order to satisfy the future needs of population. The green roof (GR) is a technique belonging to the sectors of smart energy and smart water. It could provide several benefits: sound and thermal insulation of the buildings, mitigation of the urban heat island effects, reduction of air pollution, additionally, GR induces important hydraulic advantages acting as an effective tool for reducing flood risk in urban area with runoff reduction, attenuation and delay of the peak flow. In this paper, the retention capacity of two green roof test beds located in the campus of University of Salerno has been investigated. The analysis has referred to measures of runoff and rainfall conducted in 2017 during the months of February and March. The two roofs substantially differ in the composition of the water storage layer made up of expanded clay in GR1 and of commercial drainage panels in GR2. The retention capacity of the two test beds has been compared. The results confirm that both green roofs, although to a different extent, are effective for the reduction of total runoff volume of rainwater falling on their area.

This work use Genetic Algorithms (GA) in order to interpret the existing relationship between real estate rental prices and geographic location of housing units for a central urban area of Naples. The choose of algorithm parameters are discussed on the basis of a real estate sample located in Santa Lucia and Chiaia neighborhoods. Main aim of this paper is verify the reliability of GA for real estate appraisals purposes and, at the same time, to show the potentiality but also the limits of GA in this field in terms of analysis of the housing markets.

Neural Networks (NNs) had wide interest due to empirical achievements on a wide range of learning issues. NNs are highly expressive models that can learn complex function approximations from input/output, with a particular ability to train them on massive data sets with stochastic optimization. The Bayesian approach to NNs can potentially avoid some of the problems of stochastic optimization. The use of Bayesian learning is well suited to the problem of real estate appraisals, in fact, Bayesian inference techniques are very interesting in order to deal with a small and noisy sample in the field of probabilistic inference carried out with neural model. For this purpose it has here been experimented a NNs model with Bayesian learning. The output distribution has been calculated operating a numerical integration on the weights space with the help of Markov Chain Hybrid Monte Carlo Method.

In this paper a mass appraisal methodology for the periodic reviews of the market values of special properties that constitute the asset balance of relevant real estate portfolios has been developed. Using the information published by Italian databases, a study sample of office properties of medium and large size, located in the city of Milan (Italy) and sold in the last decade, has been obtained. After having identified the main characteristics that contribute to the formation of property prices for the study sample considered, a model for the “quick” assessment of similar properties for intended use and explanatory variables of the prices and located in the same territorial context has been defined through the implementation of a genetic algorithm. The methodology developed satisfies the need to perform repetitive valuations, that should be not only contextualized to the spatial characteristics, but also timed to the different stages of evolution of the property cycles.

All over the word several rating systems have been produced to certificate the sustainability level of buildings. Experts agree on considering sustainability a typical field for multicriteria decision analysis, but buildings’ rating systems do not comply in a consistent manner this assessment approach. Proposals for using classical and hybrid ranking procedures are aimed to quantify the relative attractiveness of buildings, but do not give information on their absolute attractiveness. While several studies propose suitable application of multicriteria decision making techniques for assessing buildings’ sustainability with the purpose of choosing the best solution or obtaining a ranking on a set of alternative solutions, the present work focuses on assessment aimed at buildings’ sustainability certification, analyzing the aggregation phase of the assessment procedure. This work is intended to give some tentative reflections on construction of assessment models for buildings’ sustainability certification systems in the effective methodological frame of multicriteria decision analysis.

This paper describes an application of Break-Even Analysis (BEA) as an instrument to support public decision-makers in identifying the type of entity to be entrusted with the management of architectural heritage, in the case of absence of public resources for this purpose. The case study is localized in Gerace, a Calabrian small town, in the South of Italy. In particular, in this case, the BEA verifies the economic sustainability of the management of such assets; BEA compares the implications of the entrusting to two different types of entity, private for-profit and private not-for-profit, which have different structures of operating costs. The application of BEA allows us to understand how the expected levels of demand generate sufficient revenue to allow a balanced budget, when the management entity is a private not-for-profit; but they are not sufficient to ensure the profitability needed when the management will be entrusted to a private for profit entity. This implies the need to involve the local community in the management of cultural heritage, through a direct commitment: this role is crucial, for example, in the Inner Areas, when the tourist’s flows are insufficient to guarantee the profitability for private for profit subjects. However, if the heritage is a relevant tourist attractor, profits are not directly generated by the management of the asset, but significant impacts are still produced on the local economy. This is the reason because the local community have to guarantee anyway the enjoyment of its heritage.

How to evaluate a landscape in constant evolution and transformation? What does it constitute and determine the economic value of landscape? The present paper aims at investigating the role of evaluation tools for the assessment of the characteristics of a landscape. In line with the definition provided by the European Landscape Convention, the research deserves specific attention to the analysis of the landscape from the point of view of the values perceived by people. In particular, the paper focuses on the Choice Experiments technique that allows individual preferences and choices to be studied about several alternative scenarios. Starting from a real case concerning the Corona Verde landscape in the metropolitan area of Turin (Italy), the contribution investigates the role of Choice Experiments for supporting decision processes concerning landscape management and protection.

This paper focuses on the evaluation of the economic aspects related to urban transformations, with particular attention to the relationships among the different interests involved. Starting from the application of the Discounted Cash-Flow Analysis, the study investigates public and private perspectives in the development of the regeneration of the historic center of the city of Trieste (Italy). Different scenarios are considered and evaluated from the point of view of the public and private convenience considering the Internal Rate of Return and the Net Present Value indicators. The final results are also verified by means of specific sensitivity analyses that allow the validity of the proposed model to be tested.

The definition of a refurbishment intervention, in every step of the building process, brings to a complex decision problem; in a diachronic dimension of time, iterative and interactive must be put in relation a large variety of aspects with interrelations, concerning components, stakeholders and procedures that must be considered. The identification of the possible solutions depends on the construction of the evaluation procedures; shared goals with the stakeholders must be defined, on the basis of which can be assessed alternative intervention scenarios and make choices with regard to the matter under consideration. Thereby, in the present work a mixed method model in which are integrated Multi-Criteria Decision Analysis, Strategic Planning Tools and Participation Techniques has been proposed to be applied to refurbishment intervention in historical fabrics.

The Integrated Water Service (IWS) makes use of technological infrastructures that are part of the more general process of the natural water cycle, significantly changing its eco-systemic structure. The management of the aqueducts is a strategic link in the sustainability chain; its mission is to contain the catabolic effects of dialectic between social system and environment, caused by inadequacies in the design, construction and maintenance of capillary networks in large part obsolete. Starting from some analyses of the criticalities of the infrastructure management, the contribution focuses on information as production factor, whose implementation may help resolve major problems, especially in the planning of interventions to improve efficiency of the underground pipelines network. With reference to the ATO of Caltanissetta, we estimate the positive economic impact that the implementation of ICT-based technologies have on the extension of the territory covered by the IWS, given the quality target.

This contribution deals with some economic issues of the enhancement of Tifeo power station in Augusta, facing the Ionian Sicilian coast. The assessment was conducted to verify the economic balance and landscape sustainability of some of the most significant schemes submitted by nine teams entering a design competition organized by the Architecture Department of the University of Palermo. The assessment of the nine schemes focuses on the main issues of the territorial and real estate features involved in the regeneration process and connected with the large and polluted area surrounding the Petrochemical Industrial District of Syracuse. The analysis consists of two approaches: a Discounted Cash Flow Analysis and a Multi Attribute Value Theory pattern.

Over the past two decades the Council of Europe has addressed cultural heritage preservation policies to the use of heritage as cultural capital. Given this definition, the conservation of cultural capital is crucial, for its intrinsic value and as an investment for cultural, social and economic development. Thus, principles and areas of actions have been defined with the aim of underlying the importance of cultural values for territorial identity. Especially for cultural heritage with a potential for tourism, the decisions about valorization interventions are not always consensual, given the coexistence of different instances from local inhabitants and tourists. Selecting among the potential uses the one that could ensure the preservation of physical characters as well as intangible values, fueling economic development, is still a challenging policy and design issue. In this context, this paper proposes the use of a multi-methodological approach based on Choice Experiments and Social Multicriteria Evaluation to support the adaptive reuse on real case study. The NAIADE approach has allowed the decision maker to consider both socio-economic and technical dimensions within the same evaluation framework.

Large-scale urban development projects featured over the past thirty years have shown some critical issues related to the implementation phase. Con-sequently, the current practice seems oriented toward minimal and wide-spread interventions meant as urban catalyst. This planning practice might solve the problem of limited reliability of large developments’ feasibility studies, but it rises an evaluation demand related to the selection of coali-tion of projects within a multidimensional and multi-stakeholders deci-sion-making context. This study aims to propose a framework for the generation of coalitions of elementary actions in the context of urban regeneration processes and for their evaluation using a Multi Criteria Decision Analysis approach. The proposed evaluation framework supports decision makers in exploring dif-ferent combinations of actions in the context of urban interventions taking into account synergies, i.e. positive or negative effects on the overall per-formance of an alternative linked to the joint realization of specific pairs of actions. The proposed evaluation framework has been tested on a pilot case study dealing with urban regeneration processes in the city of Milan (Italy).

The Community impact evaluation (CIE) is a multi-actor methodology of evaluation: its goal is to identify the convenience of actions/projects, as part of urban policies, according to the Social preferable expressed by different members of the community affected by the policy itself. It traces a methodological approach for the preparation of a social report distributed in the plan’s policies. His first methodological application was developed as Planning Balance Sheet in the early sixties, in order to give an answer to the need to overcome the Cost-benefit analysis limit constituted by the failure to evaluate the distributive effects of interventions. Lichfield [1] distinguishes between different social categories those involved in an active way (promoters) or passive (users) in the implementation of an intervention, in different ways. These different modes of involvement are defined by the nature of the different advantages of which each group can enjoy and disadvantages that each group can undergo. They play an active role of the producers, who participate in the implementation of the intervention. instead play a “passive” users, who do not participate in the production process. A first way to determine the distribution effects is therefore to build many budgets disaggregated as there are groups affected by the policy of recovery. If the benefits and costs identified for each social group were all liquidated the procedure would be to build a series of indicators of economic convenience. (Net Present Value and Internal Return Rate) estimated budget for the costs benefits of all the groups involved [2].To widen the scope you can start by analyzing the advantages and disadvantages not monetizable affecting every social group. The paper illustrates the result of a social supported simulation of future hypothesis of reuse of a monumental site, by the application of a matrix modelled CIE approach.

The ability of assessing the affective information content is of increasing interest in applications of computer science, e.g. in human machine interfaces, recommender systems, social robots. In this project, the architecture of a semantic system of emotions is designed and implemented, to quantify the emotional content of short sentences by evaluating and aggregating the semantic proximity of each term in the sentence from the basic emotions defined in a psychological model of emotions (e.g. Ekman, Plutchick, Lovheim). Our model is parametric with respect to the semantic proximity measures, focusing on web-based proximity measures, where data needed to evaluate the proximity can be retrieved from search engines on the Web. To test the performances of the model, a software system has been developed to both collect the statistical data and perform the emotion analysis. The system automatizes the phases of sentence preprocessing, search engine query, results parsing, semantic proximity calculation and the final phase of ranking of emotions.

Sentiment analysis one of the important and interesting task in natural languages. A number of resources and tools have been developed for sentiment analysis of English, Turkish, Russian and other languages. Unfortunately, there was no data and tools available for sentiment analysis in Kazakh. The Dictionary of Kazakh sentiment words has been created during the study. In this work we described the rule-based method using dictionary for sentiment analysis of texts in the Kazakh language, based on the morphological rules and ontological model. Ontological model for rule extraction that determines sentiment was built. Our rule based method achieves 83% accuracy for simple sentences.

Automatic classification of drivers’ mental states is an important yet relatively unexplored topic. In this paper, we define a taxonomy of a set of complex mental states that are relevant to driving, namely: Happy, Bothered, Concentrated and Confused. We present our video segmentation and annotation methodology of a spontaneous dataset of natural driving videos from 10 different drivers. We also present our real-time annotation tool used for labelling the dataset via an emotion perception experiment and discuss the challenges faced in obtaining the ground truth labels. Finally, we present a methodology for automatic classification of drivers’ mental states. We compare SVM models trained on our dataset with an existing nearest neighbour model pre-trained on posed dataset, using facial Action Units as input features. We demonstrate that our temporal SVM approach yields better results. The dataset’s extracted features and validated emotion labels, together with the annotation tool, will be made available to the research community.

The work described in this paper represents the study and the attempt to make a contribution to one of the most stimulating and promising sectors in the field of emotion recognition, which is health care management. Multidisciplinary studies in artificial intelligence, augmented reality and psychology stressed out the importance of emotions in communication and awareness. The intent is to recognize human emotions, processing images streamed in real-time from a mobile device. The adopted techniques involve the use of open source libraries of visual recognition and machine learning approaches based on convolutional neural networks (CNN).

The present study deals with the representation and automatic detection of the multimodal communication of humble politicians. Studies in the psychology of political communication have stressed the role of dominance in the self-presentation of politicians, while implicitly excluding the very hypothesis that a political leader can be o can present himself as humble. This work presents two studies on humility and humble politicians. First a survey study has investigated how laypeople define humility, trying to extract the defining features of this notion, such as non-superiority, empathy, equality and others. Then a qualitative analysis has investigated, in the multimodal communication of four humble leaders, which postures, prosodic features, gaze and face expressions specifically convey the semantic features of humility previously hypothesized, and what emotions, detected through Ekman’s Action Units, are displayed by those leaders and how they are linked to the features of humility.

Sentiment analysis and emotion recognition are emerging research fields of research that aim to build intelligent systems able to recognize and interpret human emotions. Due to the applicability of these systems to almost all kinds of markets, also the interest of companies and industries is grown in an exponential way in the last years and a lot of frameworks for programming these systems are introduced. IBM Watson is one of the most famous and used: it offers, among others, a lot of services for Natural Language Processing. In spite of broad-scale multi-language services, most of functions are not available in a lot of “secondary” languages (like Italian). The main objective of this work is to demonstrate the feasibility of a translation-based approach to emotion recognition in texts written in “secondary” languages. We present a prototypical system using IBM Watson to extract emotions from Italian text by means of Bluemix Alchemy Language. Some preliminary results are shown and discussed in order to stress pro and cons of the approach.

The paper introduces a methodology for a learning and negotiation process that supports urban regeneration, combining management models and multi-criteria/multi-group evaluation methods. The purpose concerns the urban regeneration issue in an interdisciplinary complex decisional context where Place Branding, Community Planning, Community Impact Evaluation, and Place Marketing interplay in a decision-making process named “Community Branding (Co-Bra)”. The processing of data and information elaborated by PROMETHEE (Preference Ranking Organisation METHod for Enrichment Evaluations) is crucial for providing the decision-maker with a ranking of alternatives based on preference degrees. Starting from the analysis carried out for Matera ECoC 2019, the case study of Pisticci (MT), the third-largest town in Basilicata (Italy), tested the methodological approach. The choice of a multidimensional approach, focused on the recognition of social, economic and cultural resources, provides strategies of enhancement of cultural heritage and community network by a “community hub”, called “PLUS – Pisticci Laboratorio Urbano Sostenibile” (Pisticci Sustainable Urban Lab).

The paper introduces a methodological approach for a Deliberative Spatial Multi-Criteria Evaluation (DSM-CE) able to support cultural enhancement, combining deliberative multi-criteria evaluation methods and Geographic Information System (GIS). The purpose concerns the cultural regeneration issue in an interdisciplinary complex decisional context where an interactive decision-making process among the different stakeholders is oriented to the identification of shared cultural values. The decision-making process has been elaborated for the historic centre of Naples (Italy), in order to activate a culture-led regeneration process and to recognise in the culture the ability to influence site-specific planning actions.