Usefulness and perceived usefulness of decision support systems (DSSs) in participatory forest planning: the final user's point of view

In recent decades, the focus of forestry Decision Support Systems (DSSs) has expanded to consider the social dimension of forestry and to support participatory decision-making. A large number of models and tools have become available to solve forest management planning problems. The Usefulness of a DSS depends on the range of tools that it incorporates, and many researches have been developed to evaluate DSSs using Usefulness as parameter. The assessment of Usefulness concerns the effectiveness of a DSS. Furthermore, most assessments take into account the degree of Perceived Usefulness, which is considered an indicator of the impact a system has on job performance. The present study focuses on the analysis of final users’ point of view on the Usefulness and Perceived Usefulness of DSSs in participatory forest planning. The research investigates how forest users’ characteristics and context influence their views on the potentialities of DSSs to enhance both the various phases of the participatory planning process (Usefulness) and job performance (Perceived Usefulness). The study is based on quantitative data collected through two questionnaires e-mailed to a sample of 150 DSSs end users. The questionnaires focused on Usefulness and on Perceived Usefulness topics, respectively. Results indicate that special attention must be given to motivating respondents with a clear explanation of the survey objectives when e-mailing questionnaires. Moreover, results show that, in general, respondents consider DSSs useful at each step of the participatory process, despite differences emerge among steps. The research also shows that respondents’ Perceived Usefulness of DSSs was higher before actually engaging with DSSs. Furthermore, the results highlight differences in Perceived Usefulness to improve job performance, suggesting that the use of DSSs may actually improve job performance more than expected. Specifically, results indicate that improving the technical descriptions of methodologies incorporated in a DSS may contribute to increasing the Perceived Usefulness. The information provided within this research contributes to the advancement of knowledge regarding the Usefulness of DSSs as perceived by forest stakeholders, which in turn supports the improvement of DSS architectures and the development of participatory processes in forest management planninginfo:eu-repo/semantics/publishedVersio

Dynamic Community Discovery Method Based on Phylogenetic Planted Partition in Temporal Networks

As most of the community discovery methods are researched by static thought, some community discovery algorithms cannot represent the whole dynamic network change process efficiently. This paper proposes a novel dynamic community discovery method (Phylogenetic Planted Partition Model, PPPM) for phylogenetic evolution. Firstly, the time dimension is introduced into the typical migration partition model, and all states are treated as variables, and the observation equation is constructed. Secondly, this paper takes the observation equation of the whole dynamic social network as the constraint between variables and the error function. Then, the quadratic form of the error function is minimized. Thirdly, the Levenberg–Marquardt (L–M) method is used to calculate the gradient of the error function, and the iteration is carried out. Finally, simulation experiments are carried out under the experimental environment of artificial networks and real net-works. The experimental results show that: compared with FaceNet, SBM + MLE, CLBM, and Pi-sCES, the proposed PPPM model improves accuracy by 5% and 3%, respectively. It is proven that the proposed PPPM method is robust, reasonable, and effective. This method can also be applied to the general social networking community discovery field

Sporotrichoid Mycobacterium marinum infection in an elderly woman

We describe the case of an elderly woman who acquired a Mycobacterium marinum infection following skin exposure to the bacteria through a small wound on her right ring finger, obtained while preparing fish. The resultant sporotrichoid nodules of the right hand and the distal forearm, refractory to the initial therapy with doxycycline and rifampicin, were successfully treated with oral regimen of clarithromycin. \ua9 2015 by the article author(s)

Magnetic resonance imaging landmarks for preoperative localization of inferior medial genicular artery: a proof of concept analysis

The joint line is a useful landmark to identify IMGAcourse during knee surgery. The IMGA course is closerto the joint line and to the border of the medial tibialplateau in females than in males. Although the interindi-vidual variability these results should be taken into ac-count when performing all surgical procedures involvingthe medial aspect of the knee. Similar interindividualdistances were observed between IMGA and semimem-branosus tendon insertion regardless of gender. How-ever, the proximity to this tendon should be consideredespecially during specific cases of ligamentous balancingin TKA procedure

Criminal networks analysis in missing data scenarios through graph distances

Data collected in criminal investigations may suffer from issues like: (i) incompleteness, due to the covert nature of criminal organizations; (ii) incorrectness, caused by either unintentional data collection errors or intentional deception by criminals; (iii) inconsistency, when the same information is collected into law enforcement databases multiple times, or in different formats. In this paper we analyze nine real criminal networks of different nature (i.e., Mafia networks, criminal street gangs and terrorist organizations) in order to quantify the impact of incomplete data, and to determine which network type is most affected by it. The networks are firstly pruned using two specific methods: (i) random edge removal, simulating the scenario in which the Law Enforcement Agencies fail to intercept some calls, or to spot sporadic meetings among suspects; (ii) node removal, modeling the situation in which some suspects cannot be intercepted or investigated. Finally we compute spectral distances (i.e., Adjacency, Laplacian and normalized Laplacian Spectral Distances) and matrix distances (i.e., Root Euclidean Distance) between the complete and pruned networks, which we compare using statistical analysis. Our investigation identifies two main features: first, the overall understanding of the criminal networks remains high even with incomplete data on criminal interactions (i.e., when 10% of edges are removed); second, removing even a small fraction of suspects not investigated (i.e., 2% of nodes are removed) may lead to significant misinterpretation of the overall network. Copyright

Disrupting resilient criminal networks through data analysis: The case of sicilian mafia

Compared to other types of social networks, criminal networks present particularly hard challenges, due to their strong resilience to disruption, which poses severe hurdles to LawEnforcement Agencies (LEAs). Herein, we borrow methods and tools from Social Network Analysis (SNA) to (i) unveil the structure and organization of Sicilian Mafia gangs, based on two real-world datasets, and (ii) gain insights as to how to efficiently reduce the Largest Connected Component (LCC) of two networks derived from them. Mafia networks have peculiar features in terms of the links distribution and strength, which makes them very different from other social networks, and extremely robust to exogenous perturbations. Analysts also face difficulties in collecting reliable datasets that accurately describe the gangs' internal structure and their relationships with the external world, which is why earlier studies are largely qualitative, elusive and incomplete. An added value of our work is the generation of two realworld datasets, based on raw data extracted from juridical acts, relating to a Mafia organization that operated in Sicily during the first decade of 2000s. We created two different networks, capturing phone calls and physical meetings, respectively. Our analysis simulated different intervention procedures: (i) arresting one criminal at a time (sequential node removal); and (ii) police raids (node block removal). In both the sequential, and the node block removal intervention procedures, the Betweenness centrality was the most effective strategy in prioritizing the nodes to be removed. For instance, when targeting the top 5% nodes with the largest Betweenness centrality, our simulations suggest a reduction of up to 70% in the size of the LCC. We also identified that, due the peculiar type of interactions in criminal networks (namely, the distribution of the interactions' frequency), no significant differences exist between weighted and unweighted network analysis. Our work has significant practical applications for perturbing the operations of criminal and terrorist networks