Mariana Curado Malta

Abstract
Identifying the influential spreaders in complex networks has emerged as an important research challenge to control the spread of (mis)information or infectious diseases. Researchers have proposed many centrality measures to identify the influential nodes (spreaders) in the past few years. Still, most of them have not considered the importance of the edges in unweighted networks. To address this issue, we propose a novel centrality measure to identify the spreading ability of the Influential Spreaders using the Potential Edge Weight method (IS-PEW). Considering the connectivity structure, the ability of information exchange, and the importance of neighbouring nodes, we measure the potential edge weight. The ranking similarity of spreaders identified by IS-PEW and the baseline centrality methods are compared with the Susceptible-Infectious-Recovered (SIR) epidemic simulator using Kendall's rank correlation. The spreading ability of the top-ranking spreaders is also compared for five different percentages of top-ranking node sets using six different real networks.

Abstract
Archaeological discoveries can benefit enormously from linked open data (LOD) technologies since, as new objects are discovered, data about them can be placed in the LOD cloud and instantly accessible to third parties. This article presents a framework developed to publish LOD on arrowheads from the Chalcolithic and Early/Middle Bronze Age chronologies (2800/2900 BC to 1500 BC) found in the last 25 years of excavations on an archaeological site in Portugal. These arrowheads were kept in boxes, hidden from the possibility of being studied and viewed by interested parties. The framework encompasses a metadata application profile (MAP) and tools to be used with this MAP, such as a namespace, two metadata schemas and eight vocabulary coding schemes. The MAP domain model was developed with the support of the scientific literature about this type of arrowheads, and the team integrated two archaeologists. This framework was created with the design philosophy of maximising data interoperability, so terms from the CIDOC CRM conceptual models and other vocabularies widely used in the LOD cloud were used. The MAP was tested using a set of seven arrowheads, which proved, in the first instance, the viability of the developed MAP. The team plans to test the model in future work with arrowheads of other excavations. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

Abstract
Influential spreaders contribute substantially to managing and optimizing any spreading process in a network. Influential spreaders are nodes that hold importance within the network. Identifying them is a challenging task. Some encysting methods for such identification include local-structure-based, global-structure-based, semi-global-structure-based, and hybrid-structure-based methods. Semi-global structure-based methods show significant potential in identifying influential nodes in different network structures. However, existing semi-global structure-based methods often identify nodes from the network's periphery, where nodes are loosely connected, and their collective influence in spreading processes is minimal. This paper presents a novel method called Semi-global triangular and degree centrality (STC + K) to overcome this limitation by considering a node's degree, the number of triangles, and the third hop of neighbourhood connectivity information. The proposed novel method outperforms the existing noteworthy indexing methods regarding ranking performance. The experimental results show better performance, as indicated by two performance metrics: recognition rate and improvement percentage. By virtue of the fact that the empirically set free parameters are absent, our method eliminates the need for time-consuming preprocessing to select optimal parameter values for ranking nodes in large networks.

Abstract
Digital Transformation (DT) has become an important issue for organisations. It is proven that DT fuels Digital Innovation in organisations. It is well-known that technologies and practices such as distributed ledger technologies, open source, analytics, big data, and artificial intelligence (AI) enhance DT. Among those technologies, AI provides tools to support decision-making and automatically decide. Cooperatives are organisations with a mutualistic scope and are characterised by having participatory cooperative governance due to the principle of democratic control by the members. In a context where DT is here to stay, where the dematerialisation of processes can bring significant advantages to any organisation, this article presents a critical reflection on the dangers of using AI technologies in cooperatives. We base this reflection on the Portuguese cooperative code. We emphasise that this code is not very different from the ones of other countries worldwide as they are all based on the Statement of Cooperative Identity defined by the International Cooperative Alliance. We understand that we cannot stop the entry of AI technologies into the cooperatives. Therefore, we present a framework for using AI technologies in cooperatives to avoid damaging the principles and values of this type of organisations.

Abstract
Agricultural cooperatives remain a significant component of the food and agriculture industry to help the stakeholders to provide services and have opportunities for themselves. One of the aims of an agricultural cooperative is to answer to the needs within the communities of the farmers. Agricultural cooperatives enable individual farmers to increase productivity and maximise their social welfare. Together the farmer members of an agricultural cooperative can buy input supplies cheaper and sell more of their products in larger markets at higher prices, which is not possible for an individual smallholder farmer otherwise. Some studies have shown that farmers who were members of cooperatives have gained higher revenue for their products and spent less on input. However, organising the hundreds of farmers into smaller groups to perform collective farming and marketing is crucial to strengthening their position in the food and agriculture industry. Thereby, in our work, we consider an agricultural cooperative of smallholder farmers as a multi-agent based coalitional model, where coalitions are formed based on the similarity among the smallholder farmers. In this paper, we propose a model and implement a heuristic-based algorithm to find the disjoint partition of the agents set. We evaluate the model and the algorithm based on the following criteria: (i) individual gain, (ii) runtime analysis, (iii) solution quality, and (iv) scalability. We theoretically prove that our coalitional model of an agricultural cooperative has conciseness, expressiveness and efficiency properties. Experimental results confirm that our algorithm is time efficient and scalable. We show, both empirically and theoretically, that our algorithm generates a solution within a bound of the optimal solution. We also show that our coalition model generates positive revenue for the smallholder farmers and the payoff division rule is individual rational. In addition, we generate a new dataset in the context of an agricultural cooperative to show the effectiveness and efficiency of the proposed coalitional model of the cooperative.