Resilient sustainable investment in digital education technology: A stakeholder-centric decision support model under uncertainty

Investment in education technology (EdTech) is a complex decision problem for universities during the post-Covid era. With the objective to assess the quality and adoptability of education supply chain, a novel analytical evaluation model approach is proposed, based on quality function deployment and combinative distance-based assessment. To deal with uncertainty in the evaluation process, fuzzy theory is integrated into the model. To establish the house of quality matrix, technology-based stakeholders' requirements were identified and classified in four dimensions: economic and financial, technology adoption, sustainability, competencies. Moreover, nine supplier criteria were assumed. Based on expert evaluations, the results suggest that financial credit and supplier collaboration are the most prominent attributes to evaluate suppliers, while environmental commitment is sorted as the least important criterion. The results reveal that the three dominant suppliers, which provide the best response to the identified criteria, are providers of cloud service technology. © 2022

A New Dimension of Health Sustainability Model after Pandemic Crisis Using Structural Equation Model

Since the coronavirus (COVID-19) pandemic, it has been clear that the health dimension (HEDm) has a severe impact on sustainability, which was originally considered from the pillars of society, environment and economy. Hence, the integration of the health dimension into the other three pillars is plausible to define guidelines and criteria for progress monitoring and policy assessment towards a health-sustainable city. The objective of this study aims to present The Health Sustainability Model (HSM), a four-dimensional model for health sustainability (health, economy, environment, and society), using the Del-phi method to determine potential indicators agreed by eighteen experts, including physicians who deeply understand issues on health sustainability, and assess complex dimensions of health in the context of sustainability. The researchers have found that 45 indicators, later grouped into 15 elements and 4 dimensions, have a high level of agreement with Kendall's W (KW) at 0.36. The HSM was then examined by the structural equation model (SEM) with reliability and validity shown as follows: the absolute fit with CMIN/DF = 1.44, RMSEA = 0.033, GFI = 0.96, AGFI = 0.94, RMR = 0.025, and the incremental fit with NFI = 0.94, CFI = 0.98, TLI = 0.97, and IFI = 0.98. Based on the results, the model is valid, in line with the empirical data. For further application, the HSM is expected to support city planners and decision makers by identifying room for improvement in each dimension through the indicators employed in the model. In contrast to existing studies that mainly use qualitative data, by conducting quantitative assessment, the model enables policy makers to objectively evaluate conditions and appropriately design policies to improve residents' well-being.

Identification of Critical Regions to Guide Public Policies in the Fight Against Covid-19: Decision Support Through Artificial Intelligence and the Thor2 Multi-Criteria Method

When the World Health Organization declared world pandemic in 2020 due to high propagation rate of the COVID-19, several government levels (municipalities, states and countries) initiated actions to try to fight the spread of the disease. Most of these actions where some sort of quarantine or urban mobility restrictions. To guide the governments' actions and decisions, several factors have to be considered, such as propagation and lethalness of the disease for each administrative region. However, die to the differences between urban regions, in Brazil, the final decision as to which actions to take were left to the municipalities. For this reason, o goal of this study is to, based on the official data available for the city of Rio de Janeiro, use Artificial Intelligence tools and Multicriteria Methods to identify the administrative regions that will require a higher level of attention in the event of a second wave of COVID-19. (English) [ FROM AUTHOR]

Disparities in the level of regional technical infrastructure development in Poland: multicriteria analysis

Research background: The development policy currently promoted by the European Union is focused on the use of the territory's internal resources. Among the factors affecting regional development, by building its potential, infrastructure, being a basic necessity for developing activity in a given area, is of significant importance. Hence, investment in infrastructure is critical to stimulating economic dynamism, as it is the basis for supporting a variety of measures aimed at economic growth. Purpose of the article: This paper aims to evaluate the level of development of technical infrastructure and changes taking place in this field in Polish voivodeships in 2008 and 2020. Methods: The study was carried out using the Hellwig development pattern method and a comparative analysis of the technical infrastructure of Polish regions. The above approach makes it possible to measure the diversity of the state and availability of infrastructure for the communities of the regions. Findings & value added: While implementing the study aim, particular attention was paid to the spatial differences in the level of development of the technical infrastructure of Polish voivodeships. The analysis enabled to distinguish groups of voivodeships with the highest, high, low, and very low level of technical infrastructure development. From a long-term perspective, the conducted research can be seen as a contribution to existing research and serve to further compare the impact of technical infrastructure on the economic development of countries. The strength of the study is the adequately long time span of the analysis (2008 - the period of the financial crisis and 2020 - the COVID 2019 pandemic), which provides a basis for the formation of the infrastructure in question. The added value of the article is also a regional perspective on the level of development of technical infrastructure using multidimensional methods of statistical analysis. The results of the study can be used to make decisions at the national level regarding the retrofitting of infrastructure in regions with a low level of infrastructure development. For the European Union's decision-makers they can be a source of knowledge of where to direct EU funds the purpose of which is the infrastructural development of regions.

Environmental risks due to the presence of microplastics in coastal and marine environments of the Colombian Caribbean.

Microplastics (MPs) are plastic sediments that are released into the environment by various sources, their abundance and distribution increase as their size decreases, they represent a risk to ecological processes and their abundance is related to their proximity to human activities and The Anthropocene era, in addition to the Covid-19 pandemic, has exacerbated the emitting sources of plastics such as face masks, disinfectant container bottles, among others, all due to all the biosafety measures required globally. Over time, the transformation of plastics into microplastics generates particles transported by atmospheric and water dynamics, being accumulated in soils, bodies of water and incorporated into ecosystems and the food chains of organisms, including humans. Marine-coastal environments such as coastal lagoons, which in addition to hosting strategic ecosystems, being areas of convergence of different ecological flows and with important ecosystem services, have also become sinks for MP particles, putting their productivity and value at risk. Socio-ecological that they have. The purpose of this research is to evaluate and zone the environmental risks derived from contamination by microplastics in a coastal lagoon system, since once the MPs enter the environment they can cause harmful effects, in this case in the Caribbean Sea and in the lagoon complex. To this end, a comprehensive study of planetary systems was carried out to better understand their disturbances due to the presence of microplastics.

Novel Federated Decision Making for Distribution of Anti-SARS-CoV-2 Monoclonal Antibody to Eligible High-Risk Patients

Context: When the epidemic first broke out, no specific treatment was available for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The urgent need to end this unusual situation has resulted in many attempts to deal with SARS-CoV-2. In addition to several types of vaccinations that have been created, anti-SARS-CoV-2 monoclonal antibodies (mAbs) have added a new dimension to preventative and treatment efforts. This therapy also helps prevent severe symptoms for those at a high risk. Therefore, this is one of the most promising treatments for mild to moderate SARS-CoV-2 cases. However, the availability of anti-SARS-CoV-2 mAb therapy is limited and leads to two main challenges. The first is the privacy challenge of selecting eligible patients from the distribution hospital networking, which requires data sharing, and the second is the prioritization of all eligible patients amongst the distribution hospitals according to dose availability. To our knowledge, no research combined the federated fundamental approach with multicriteria decision-making methods for the treatment of SARS-COV-2, indicating a research gap. Objective: This paper presents a unique sequence processing methodology that distributes anti-SARS-CoV-2 mAbs to eligible high-risk patients with SARS-CoV-2 based on medical requirements by using a novel federated decision-making distributor. Method: This paper proposes a novel federated decision-making distributor (FDMD) of anti-SARS-CoV-2 mAbs for eligible high-risk patients. FDMD is implemented on augmented data of 49,152 cases of patients with SARS-CoV-2 with mild and moderate symptoms. For proof of concept, three hospitals with 16 patients each are enrolled. The proposed FDMD is constructed from the two sides of claim sequencing: central federated server (CFS) and local machine (LM). The CFS includes five sequential phases synchronised with the LMs, namely, the preliminary criteria setting phase that determines the high-risk criteria, calculates their weights using the newly formulated interval-valued spherical fuzzy and hesitant 2-tuple fuzzy-weighted zero-inconsistency (IVSH2-FWZIC), and allocates their values. The subsequent phases are federation, dose availability confirmation, global prioritization of eligible patients and alerting the hospitals with the patients most eligible for receiving the anti-SARS-CoV-2 mAbs according to dose availability. The LM independently performs all local prioritization processes without sharing patients' data using the provided criteria settings and federated parameters from the CFS via the proposed Federated TOPSIS (F-TOPSIS). The sequential processing steps are coherently performed at both sides. Results and Discussion: (1) The proposed FDMD efficiently and independently identifies the high-risk patients most eligible for receiving anti-SARS-CoV-2 mAbs at each local distribution hospital. The final decision at the CFS relies on the indexed patients' score and dose availability without sharing the patients' data. (2) The IVSH2-FWZIC effectively weighs the high-risk criteria of patients with SARS-CoV-2. (3) The local and global prioritization ranks of the F-TOPSIS for eligible patients are subjected to a systematic ranking validated by high correlation results across nine scenarios by altering the weights of the criteria. (4) A comparative analysis of the experimental results with a prior study confirms the effectiveness of the proposed FDMD. Conclusion: The proposed FDMD has the benefits of centrally distributing anti-SARS-CoV-2 mAbs to high-risk patients prioritized based on their eligibility and dose availability, and simultaneously protecting their privacy and offering an effective cure to prevent progression to severe SARS-CoV-2 hospitalization or death.

An application of the PROMETHEE II method for the comparison of energy requalification strategies to design Post-Carbon Cities

A resilient, diversified, and efficient energy system, comprising multiple energy carriers and high-efficiency infrastructure, is the way to decarbonise the European economy in line with the Paris Agreement, the UN 2030 Agenda for Sustainable Development, and the various recovery plans after the COVID-19 pandemic period. To achieve these goals, a key role is played by the private construction sector, which can reduce economic and environmental impacts and accelerate the green transition. Nevertheless, while traditionally decision-making problems in large urban transformations were supported by economic assessment based on Life Cycle Thinking and Cost-Benefit Analysis (CBA) approaches, these are now obsolete. Indeed, the sustainable neighbourhood paradigm requires the assessment of different aspects, considering both economic and extra-economic criteria, as well as different points of view, involving all stakeholders. In this context, the paper proposes a multi-stage assessment procedure that first investigates the energy performance, through a dynamic simulation model, and then the socio-economic performance of regeneration operations at the neighbourhood scale, through a Multi-Criteria Decision Analysis (MCDA). The model based on the proposed Preference Ranking Organisation Method for Enrichment Evaluations II (PROMETHEE II) aims to support local decision makers (DMs) in choosing which retrofit operations to implement and finance. The methodology was applied to a real-world case study in Turin (Italy), where various sustainable measures were ranked using multiple criteria to determine the best transformation scenario.

Sustainable Mobility Engagement and Co-planning;a Multicriteria Analysis Based Transferability Guide

Engagement in sustainable mobility planning seems to act as a starting point to unlock a new era of responsible and sustainable behaviors. After almost a two-years experience of a global crisis (COVID-19) revealing that the only way out is through jointly walking on the way into sustainability and resilience, engaging people in shifting to sustainable mobility options has become an imperative need. The current paper exploits Multi-Criteria Decision Analysis (MCDA) in building a methodological 5-step framework for evaluating the transferability potentials of good practices (GPs) in citizens’ sensibilization and engagement in sustainable mobility. 10 good practices were selected in order to cover the whole cycle of sustainable mobility planning (SUMP cycle) while representatives from different EU Regions were involved in the assessment procedure resulting in this way in a general transferability guide. The guide, tailored to each case, can be a very useful tool in the hands of single authorities while making their mobility engagement plan. © 2022, Springer Nature Switzerland AG.

Hybrid MCDM for Cloud Services: AHP(blocks) & Entropy, TOPSIS & MOORA (Case Study with QoS and QoE Criteria)

In the methodology part of this study, we introduced a combined “AHP(blocks) and Entropy” approach for improving of Multi-Criteria Decision Making (MCDM) for Cloud Services (CS) selection. On this basis, we optimize the combination of professional experts’ opinion and objectivity of entropy criteria weighting. In this second part, we test our findings on an existing universal dataset with Quality of Service (QoS) and Quality of Experience (QoE) criteria and adapting for the needs of individual users and small education organisations under conditions of COVID-19 pandemics. © 2022, Springer Nature Switzerland AG.

Combined criteria weighting in MCDM: AHP in blocks with traditional Entropy and novel Hierarchy in TOPSIS evaluation of Cloud Services

This paper presents a combined approach based on 'AHP-block and objective criteria weighting with Entropy and Hierarchy approach for improving the methodology of Multi-Criteria Decision Making (MCDM) for Cloud Services (CS) selection. A structured and comprehensive approach allows optimizing the combination of professional experts' opinion and objectivity of entropy criteria weighting. The new method and findings are tested on an recent and detailed dataset with Quality of Service (QoS) criteria with a simulation for the needs of individual users and small education organisations under conditions of COVID-19 pandemics. © 2021 IEEE.

A methodological proposal to evaluate the health hazard scenario from COVID-19 in Italy.

2019 Coronavirus disease (COVID-19) had a big impact in Italy, mainly concentrated in the northern part of the Country. All this was mainly due to similarities of this area with Wuhan in Hubei Province, according to geographical, environmental and socio-economic points of view. The basic hypothesis of this research was that the presence of atmospheric pollutants can generate stress on health conditions of the population and determine pre-conditions for the development of diseases of the respiratory system and complications related to them. In most cases the attention on environmental aspects is mainly concentrated on pollution, neglecting issues such as land management which, in some way, can contribute to reducing the impact of pollution. The reduction of land take and the decrease in the loss of ecosystem services can represent an important aspect in improving environmental quality. In order to integrate policies for environmental change and human health, the main factors analyzed in this paper can be summarized in environmental, climatic and land management. The main aim of this paper was to produce three different hazard scenarios respectively related to environmental, climatic and land management-related factors. A Spatial Analytical Hierarchy Process (AHP) method has been applied over thirteen informative layers grouped in aggregation classes of environmental, climatic and land management. The results of the health hazard maps show a disparity in the distribution of territorial responses to the pandemic in Italy. The environmental components play an extremely relevant role in the definition of the red zones of hazard, with a consequent urgent need to renew sustainable development strategies. The comparison of hazard maps related to different scenarios provides decision makers with tools to orient policy choices with a different degree of priority according to a place-based approach. In particular, the geospatial representation of risks could be a tool for legitimizing the measures chosen by decision-makers, proposing a renewed approach that highlights and takes account of the differences between the spatial contexts to be considered - Regions, Provinces, Municipalities - also in terms of climatic and environmental variables.

Competitiveness of the Selected Countries of the EU with a Focus on the Quality of the Business Environment

The business environment and entrepreneurship are important elements in the economic growth of each country. The better the business environment a given country offers, the more attractive the country is for small and large companies as well as for private entrepreneurs. A high level of business competitiveness can help a country secure economic growth, especially after overcoming a crisis such as the COVID-19 pandemic. Many institutions focus on the measurement of the business environment using indices to evaluate its quality. The main goal of the present study is to evaluate the quality of the business environment through multicriteria analysis. For the period from 2018 to 2020, the data were analysed by using seven selected indices of the weighted sum approach (WSA) and the technique for order preference by similarity to ideal solution (TOPSIS) methods. The research sample included all EU countries that joined the EU at the same time in 2004. The processing of analytical data was gradually implemented by using descriptive statistics and multicriteria evaluation methods. The methods used in the multicriteria evaluation of variants determined the rankings of the individual variants in terms of the selected criteria using entropy. We concluded that the efficiency of the business environments in Cyprus, the Czech Republic, Estonia, Hungary, Poland, Latvia, Lithuania, Slovakia and Slovenia are below the EU average. Within this group of countries, Estonia, Malta and Slovenia have seen the largest regeneration of their business environment since having joined the EU.

Evaluation of Transportation Infrastructure: A Case Study of Gold Coast Light Rail Stage 1&2

Since the COVID-19 pandemic began, there has been increased reliance on new infrastructure projects to counter economic fallout and underpin employment security. Urban and inter-urban transportation projects, such as major road, rail and port facilities, are popular choices for national and state governments in Australia as they provide broad fiscal support across all sectors of the economy. The problem with stimulus is making sure that the quality of the new infrastructure provides collective utility to a community or region. Whether the benefits will be worthwhile and represent best use of resource inputs requires financial, social, ethical and environmental consequences to be evaluated in a comparable format. The aim in this paper is to analyse the Gold Coast Light Rail (GCLR) Stage 1&2 project using a method that is capable of merging tangible and intangible criteria using an ordinal ranking algorithm. While the GCLR case study is undertaken with the benefit of hindsight, normally these types of evaluations are performed in real time as a project progresses from initiation (design) to implementation (deliver) and influence (delight). The method adopted in this study represents a modern form of multi-criteria decision-making, which enables successful projects to be distinguished from unsuccessful ones using a time period from commencement until one full year of operation has occurred. The i3d3 model, developed by a team from Bond University, has the unique benefit of ranking projects from best to worst across an organisational portfolio, geographic region or industry sector. It also supports past project performance to inform new design through application of a continuous improvement process of recording lessons learned. The GCLR case study calculated 100% of the critical success factors in the model to be positive and produced an overall success ranking of 23 (on a scale of -100 to +100). This paper presents the approach taken to evaluate GCLR's level of success and the calculations that took place to reach this finding. This is the first time i3d3 has been used on an Australian project.

A multicriteria approach for risk assessment of Covid-19 in urban district lockdown.

At the beginning of 2020, the spread of a new strand of Coronavirus named SARS-CoV-2 (COVID-19) raised the interest of the scientific community about the risk assessment related to the viral infection. The contagion became pandemic in few months forcing many Countries to declare lockdown status. In this context of quarantine, all commercial and productive activities are suspended, and many Countries are experiencing a serious crisis. To this aim, the understanding of risk of contagion in every urban district is fundamental for governments and administrations to establish reopening strategies. This paper proposes the calibration of an index able to predict the risk of contagion in urban districts in order to support the administrations in identifying the best strategies to reduce or restart the local activities during lockdown conditions. The objective regards the achievement of a useful tool to predict the risk of contagion by considering socio-economic data such as the presence of activities, companies, institutions and number of infections in urban districts. The proposed index is based on a factorial formula, simple and easy to be applied by practitioners, calibrated by using an optimization-based procedure and exploiting data of 257 urban districts of Apulian region (Italy). Moreover, a comparison with a more refined analysis, based on the training of Artificial Neural Networks, is performed in order to take into account the non-linearity of the phenomenon. The investigation quantifies the influence of each considered parameter in the risk of contagion useful to obtain risk analysis and forecast scenarios.