Multi-objective optimization technique: A novel approach in tourism sustainability planning.

Strategic management of complex decisional problems in the tourism sector requires the implementation of proper planning procedures for sustainable tourism activities, due to either the number of actors involved, the presence of conflicting goals and the need of resource rationalization. Thus, the aim of the research is to implement a new approach for the selection of projects to be funded by public administration, in order to maximize the efficiency of public resource allocation. Furthermore, the proposed technique enhances the inclusion of the three dimensions of sustainability in tourism policy planning and implementation phases. The Multi-objective Optimization model accounts for environmental, social and economic impacts, to select tourism activities to be realized in order to maximize stakeholder utility. In order to test the model, we selected a case study: a call for tender drafted by an Italian Public administration, whose aim was to support the attractiveness of the urban areas. Findings illustrate that, in comparison with multicriteria techniques, the proposed model allows to achieve a better allocation of public funds, in both quantitative (i.e., amount of resources allocated) and qualitative (selection of projects with positive social and environmental implication) terms.

Best-compromise solutions for waste management: Decision support system for policymaking.

Proper management of urban waste might support sustainable and circular development, while mismanagement increases both costs and socio-environmental negative outcomes. In particular, the organic fraction constitutes the largest share of urban waste. In the circular economy framework, it is described as a valuable resource, to be converted into soil improver, biogas and energy. The aim of the paper is to propose a Decision Support System (DSS) for policymakers, based on linear programming techniques. This model is expected to improve the current methodologies for planning and managing organic fraction of municipal solid waste and provide useful insights about public resources allocation. The proposed optimization model is tested on Campania Region (Italy), which is a clear example of the negative implications of improper waste management. Based on the goals recently set by Campania regional government, the model allows to select the most cost-effective and sustainable solutions for treating organic waste. Results show three different scenarios associated to the impacts that each possible outcome has on the stated objectives. The "Ideal Solution" is not achievable, but it is used as a benchmark; the "Max NPV Solution" is feasible, but it reports several major drawbacks. Finally, the "Best Compromise Solution" allows to increase regional composting capability by six time and biogas availability by seven times, with environmental implications that are very similar to the ideal ones.

Reliability of IOTA score and ADNEX model in the screening of ovarian malignancy in postmenopausal women.

BACKGROUND: The IOTA (International Ovarian Tumor Analysis) group has developed the ADNEX (Assessment of Different NEoplasias in the adneXa) model to predict the risk that an ovarian mass is benign, borderline or malignant. This study aimed to test reliability of these risks prediction models to improve the performance of pelvic ultrasound and discriminate between benign and malignant cysts. MATERIAL AND METHODS: Postmenopausal women with an adnexal mass (including ovarian, para-ovarian and tubal) and who underwent a standardized ultrasound examination before surgery were included. Prospectively and retrospectively collected data and ultrasound appearances of the tumors were described using the terms and definitions of the IOTA group and tested in accordance with the ADNEX model and were compared to the final histological diagnosis. RESULTS: Of the 107 menopausal patients recruited between 2011 and 2016, 14 were excluded (incomplete inclusion criteria). Thus, 93 patients constituted a cohort in whom 89 had benign cysts (83 ovarian and 6 tubal or para-ovarian cysts), 1 had border line tumor and 3 had invasive ovarian cancers (1 at first stage, 1 at advanced stage and 1 metastatic tumor in the ovary). The overall prevalence of malignancy was 4.3%. Every benign ovarian cyst was classified as probably benign by IOTA score which showed also a high specificity with the totality of probably malignant lesion proved malignant by histological exam. The limit of this score was the important rate of not classified or undetermined cysts. However, the malignancy risks calculated by ADNEX model allowed identifying the totality of malignancy. Thus, the combination of the two methods of analysis showed a sensitivity and specificity rates of respectively 100% and 98%. Evaluation of malignancy risks by these 2 tests highlighted a negative predictive value of 100% (there was no case of false negative) and a positive predictive value of 80%. DISCUSSION AND CONCLUSION: On the basis of our findings, the IOTA classification and the ADNEX multimodal algorithm used as risks prediction models can improve the performance of pelvic ultrasound and discriminate between benign and malignant cysts in postmenopausal women, especially for undetermined lesions.