Recent Submissions

  • COVID-19 pandemic decision support system for a population defense strategy and vaccination effectiveness

    Varotsos, Costas A; Krapivin, Vladimir F; Xue, Yong; Soldatov, Vladimir; Voronova, Tatiana; National and Kapodistrian University of Athens, Athens, Greece; Kotelnikov’s Institute of Radioengineering and Electronics, Fryazino Branch, Russian Academy of Sciences, Vvedensky 1, Fryazino, Moscow Region 141190, Russian Federation; University of Mining and Technology, Xuzhou, Jiangsu 221116, PR China; University of Derby (Elsevier BV, 2021-06-05)
    The year 2020 ended with a significant COVID-19 pandemic, which traumatized almost many countries where the lockdowns were restored, and numerous emotional social protests erupted. According to the World Health Organization, the global epidemiological situation in the first months of 2021 deteriorated. In this paper, the decision-making supporting system (DMSS) is proposed to be an epidemiological prediction tool. COVID-19 trends in several countries and regions, take into account the big data clouds for important geophysical and socio-ecological characteristics and the expected potentials of the medical service, including vaccination and restrictions on population migration both within the country and international traffic. These parameters for numerical simulations are estimated from officially delivered data that allows the verification of theoretical results. The numerical simulations of the transition and the results of COVID-19 are mainly based on the deterministic approach and the algorithm for processing statistical data based on the instability indicator. DMSS has been shown to help predict the effects of COVID-19 depending on the protection strategies against COVID-19 including vaccination. Numerical simulations have shown that DMSS provides results using accompanying information in the appropriate scenario.
  • Regulating Product Sustainability

    Takhar, Raj; Takhar, Sukhraj; University of Derby (The Parliamentary Office of Science and Technology, 2021-06-10)
    Interview, written review and feedback on UK government proposals on the future of regulating product for sustainability.
  • A collaborative approach for national cybersecurity incident management

    Oriola, Oluwafemi; Adeyemo, Adesesan Barnabas; Papadaki, Maria; Kotzé, Eduan; university of Plymouth; University of Ibadan, Ibadan, Nigeria; University of the Free State, Bloemfontein, South Africa (Emerald, 2021-06-28)
    Collaborative-based national cybersecurity incident management benefits from the huge size of incident information, large-scale information security devices and aggregation of security skills. However, no existing collaborative approach has been able to cater for multiple regulators, divergent incident views and incident reputation trust issues that national cybersecurity incident management presents. This paper aims to propose a collaborative approach to handle these issues cost-effectively. A collaborative-based national cybersecurity incident management architecture based on ITU-T X.1056 security incident management framework is proposed. It is composed of the cooperative regulatory unit with cooperative and third-party management strategies and an execution unit, with incident handling and response strategies. Novel collaborative incident prioritization and mitigation planning models that are fit for incident handling in national cybersecurity incident management are proposed. Use case depicting how the collaborative-based national cybersecurity incident management would function within a typical information and communication technology ecosystem is illustrated. The proposed collaborative approach is evaluated based on the performances of an experimental cyber-incident management system against two multistage attack scenarios. The results show that the proposed approach is more reliable compared to the existing ones based on descriptive statistics. The approach produces better incident impact scores and rankings than standard tools. The approach reduces the total response costs by 8.33% and false positive rate by 97.20% for the first attack scenario, while it reduces the total response costs by 26.67% and false positive rate by 78.83% for the second attack scenario.
  • An empirical analysis of the information security culture key factors framework

    Tolah, Alaa; Furnell, Steven; Papadaki, Maria; University of Plymouth; Saudi Electronic University, Riyadh, Saudi Arabia; University of Nottingham; University of Derby; Nelson Mandela University, Gqeberha, South Africa (Elsevier, 2021-06-05)
    Information security is a challenge facing organisations, as security breaches pose a serious threat to sensitive information. Organisations face security risks in relation to their information assets, which may also stem from their own employees. Organisations need to focus on employee behaviour to limit security failures, as if they wish to establish effective security culture with employees acting as a natural safeguard for information assets. This study was conducted to respond to a need for more empirical studies that focus on a development of security culture to provide a comprehensive framework. The Information Security Culture and Key Factors Framework has been developed, incorporating two types of factors: those that influence security culture and those that reflect it. This paper validates the applicability of the framework and tests related hypotheses through an empirical study. An exploratory survey was conducted, and 266 valid responses were obtained. Phase two of the study demonstrates the framework levels of validity and reliability through the use of factor analysis. Different hypothetical correlations were analysed through the use of structural equation modelling, with indirect exploratory effect of the moderators achieved through a multi-group analysis. The findings show that the framework has validity and achieved an acceptable fit with the data. This study fills an important gap in the significant relationship between personality traits and security culture. It also contributes to the improvement of information security management through the introduction of a comprehensive framework in practice, which functions in the establishment of security culture. The factors are vital in justifying security culture acceptance, and the framework provides an important tool that can be used to assess and improve an organisational security culture.
  • Research on Action Strategies and Simulations of DRL and MCTS-based Intelligent Round Game

    Sun, Yuxiang; Yuan, Bo; Zhang, Yongliang; Zheng, Wanwen; Xia, Qingfeng; Tang, Bojian; Zhou, Xianzhong; Nanjing University, China; University of Derby; Army Engineering University, Nanjing, China (Springer Science and Business Media LLC, 2021-06-16)
    The reinforcement learning problem of complex action control in multiplayer online battlefield games has brought considerable interest in the deep learning field. This problem involves more complex states and action spaces than traditional confrontation games, making it difficult to search for any strategy with human-level performance. This paper presents a deep reinforcement learning model to solve this problem from the perspective of game simulations and algorithm implementation. A reverse reinforcement-learning model based on high-level player training data is established to support downstream algorithms. With less training data, the proposed model is converged quicker, and more consistent with the action strategies of high-level players’ decision-making. Then an intelligent deduction algorithm based on DDQN is developed to achieve a better generalization ability under the guidance of a given reward function. At the game simulation level, this paper constructs Monte Carlo Tree Search Intelligent Decision Model for turn-based antagonistic deduction games to generate next-step actions. Furthermore, a prototype game simulator that combines offline with online functions is implemented to verify the performance of proposed model and algorithm. The experiments show that our proposed approach not only has a better reference value to the antagonistic environment using incomplete information, but also accurate and effective in predicting the return value. Moreover, our work provides a theoretical validation platform and testbed for related research on game AI for deductive games.
  • Electro-Thermal Coupled Modeling of Induction Motor Using 2D Finite Element Method

    Bousbaine, Amar; Bouheraoua, Mustapha; Atig, M.; Benamrouche, N; University of Derby; Université Mouloud Mammeri de Tizi Ouzou (Ştefan cel Mare University of Suceava, 2021-05-31)
    The paper evaluates the thermal behavior of an induction machine based on a coupled electromagnetic-thermal model using 2D non-linear complex finite element method. The currents and the temperature distribution in a squirrel cage induction motor in transient state are investigated and presented. The convection heat transfer coefficient between the frame and ambient and the windings are treated with particular attention. The developed method can be applied to other electric machines having negligible axial heat flow. The corroboration of the theoretical/simulated results have been investigated, experimentally using a 2.2 kW totally enclosed fan-cooled induction motor. The simulated results and those obtained from measurements have been critically evaluated and showed good agreements.
  • Nowcasting of air pollution episodes in megacities: A case study for Athens, Greece

    Varotsos, Costas A.; Mazei, Yuri; Saldaev, Damir; Efstathiou, Maria; Voronova, Tatiana; Xue, Yong; University of Athens, Athens, Greece; Lomonosov Moscow State University, Leninskiye Gory, 1, Moscow, Russia; Severtsov Institute of Ecology and Evolution, Russian Academy of Sciences, Moscow, Russia; Shenzhen MSU-BIT University, Shenzhen, China; et al. (Elsevier BV, 2021-06-02)
    The main objective of the present study is to develop a model for the prediction of the extreme events of air pollution in megacities using the concept of so-called "natural time" instead of the "conventional clock time". In particular, we develop a new nowcasting technique based on a statistically significant fit to the law of Gutenberg-Richter of the surface concentration of ozone (O3), particles of the size fraction less than 10 μm (PM-10) and nitrogen dioxide (NO2). Studying the air pollution over Athens, Greece during the period 2000–2018, we found that the average waiting time between successive extreme concentrations values varied between different atmospheric parameters accounted as 17 days in case of O3, 29 days in case of PM-10 and 28 days in case of NO2. This average waiting time depends on the upper threshold of the maximum extreme concentrations of air pollutants considered. For instance, considering the NO2 concentrations over Athens it was found that the average waiting time is 13 days for 130 μg/m3 and 2.4 years for 200 μg/m3. Remarkably, the same behaviour of obedience to the Guttenberg-Richter law characterizing the extreme values of the air pollution of a megacity was found earlier in other long-term ecological and paleoclimatic variables. It is a sign of self-similarity that is often observed in nature, which can be used in the development of more reliable nowcasting models of extreme events.
  • Recommender Systems Evaluator: A Framework for Evaluating the Performance of Recommender Systems

    dos Santos, Paulo V.G.; Tardiole Kuehne, Bruno; Batista, Bruno G.; Leite, Dionisio M.; Peixoto, Maycon L.M.; Moreira, Edmilson Marmo; Reiff-Marganiec, Stephan; University of Derby; Federal University of Itajubá, Itajubá, Brazil; Federal University of Mato Grosso do Sul (UFMS), Ponta Porã, Brazil; et al. (Springer, 2021-06-05)
    Recommender systems are filters that suggest products of interest to customers, which may positively impact sales. Nowadays, there is a multitude of algorithms for recommender systems, and their performance varies widely. So it is crucial to choose the most suitable option given a situation, but it is not a trivial task. In this context, we propose the Recommender Systems Evaluator (RSE): a framework aimed to accomplish an offline performance evaluation of recommender systems. We argue that the usage of a proper methodology is crucial when evaluating the available options. However, it is frequently overlooked, leading to inconsistent results. To help appraisers draw reliable conclusions, RSE is based on statistical concepts and displays results intuitively. A comparative study of classical recommendation algorithms is presented as an evaluation, highlighting RSE’s critical features.
  • Transforming product labels using digital technologies to enable enhanced traceability and management of hazardous chemicals

    Takhar, Sukhraj; Liyanage, Kapila; University of Derby (Inderscience, 2021-06-08)
    Manufacturers that produce, distribute or market physical products are likely to be impacted by numerous chemical and product regulations. Manufacturers must identify chemical substances which appear within mixtures, materials, formulations, raw materials, components, assemblies and finished products. This results in a very manual and resource intensive process of collection of chemical substances in products data, where definitions arise from internal, industry standards, supplier and customer requirements and often sourced from multiple supply chain actors. This paper contributes to existing literature by identifying a research gap in transforming current manual state data collection tasks via the utilisation of digital technologies, leveraging real-time data collection using smart labels to identify chemicals contained within products. The proposed design enables manufacturers to identify the use of chemicals consumed in a automated manner and enabling appropriate risks to be identified and managed accordingly. The design can be further expanded in the proposed collaborative data sharing network.
  • Realignment of Product Stewardship towards Chemical Regulations, the Circular Economy and Corporate Social Responsibility – a Delphi Study

    Liyanage, Kapila; Takhar, Sukhraj; University of Derby (Sepuluh Nopember Institute of Technology (ITS), 2021-07)
    Chemical regulations exist to limit and control the amount of hazardous chemical substances being used by industry. Increasing awareness of diminishing natural resources, increasing pollution, and reducing the amounts of harmful waste, has led towards increasing societal and regulatory pressure on industry to change from the traditional closed-loop manufacturing towards the adoption of sustainable materials and open-loop manufacturing systems as part of the Circular Economy. Corporate Social Responsibility (CSR) extends the relationship between industry and society. Product Stewardship (PS) provides a platform for organizations to assess impacts to manufacturing systems ensuring adequate measures are in place to understand, control or limit any impact(s) from manufacturing and using products. The research question answered in this paper relates to understanding the impacts on PS. This paper has been written based on a literature review and Delphi study. The outcomes from this paper will attempt to outline a framework for PS to align with Chemical Regulations, the Circular Economy and CSR.
  • On Generalized Lucas Pseudoprimality of Level k

    Andrica, Dorin; Bagdasar, Ovidiu; Babeş-Bolyai University, 400084 Cluj-Napoca, Romania; University of Derby (MDPI AG, 2021-04-12)
    We investigate the Fibonacci pseudoprimes of level k, and we disprove a statement concerning the relationship between the sets of different levels, and also discuss a counterpart of this result for the Lucas pseudoprimes of level k. We then use some recent arithmetic properties of the generalized Lucas, and generalized Pell–Lucas sequences, to define some new types of pseudoprimes of levels k+ and k− and parameter a. For these novel pseudoprime sequences we investigate some basic properties and calculate numerous associated integer sequences which we have added to the Online Encyclopedia of Integer Sequences.
  • On k-partitions of multisets with equal sums

    Andrica, Dorin; Bagdasar, Ovidiu; Babeş-Bolyai University of Cluj-Napoca, Cluj-Napoca, Romania; University of Derby (Springer Science and Business Media LLC, 2021-05-05)
    We study the number of ordered k-partitions of a multiset with equal sums, having elements α1,…,αn and multiplicities m1,…,mn. Denoting this number by Sk(α1,…,αn;m1,…,mn), we find the generating function, derive an integral formula, and illustrate the results by numerical examples. The special case involving the set {1,…,n} presents particular interest and leads to the new integer sequences Sk(n), Qk(n), and Rk(n), for which we provide explicit formulae and combinatorial interpretations. Conjectures in connection to some superelliptic Diophantine equations and an asymptotic formula are also discussed. The results extend previous work concerning 2- and 3-partitions of multisets.
  • Mechanical Engineering Design, Does the Past Hold the key to the Future?

    Sole, Martin; Ian, Turner; Barber, Patrick; University of Derby (The Design Society, 2021)
    Industry design of a complex product has always required a cross-disciplinary team of experts. Is it possible to mimic these teams in academia when training the design engineers of the future, and what disciplinary skills will they possess? The exceptional collaboration potential provided by the internet means industry experts can work as a team, and at the same time, reside anywhere in the world. What are the capabilities of teamwork when the team members may never see each other for real? Though a physical prototype is sometimes required, most prototypes are designed and created in the virtual world using 3D modelling. The model can be tested, checked for accuracy, have materials applied, and be created parametrically which allows the products geometry to be reset to different sizes by the designer. Collaboration, effective communication and 3D modelling make it possible to design intricate and complex designs remotely. While we rightly congratulate ourselves on the complexity of modern design and how clever we have become, we must not lose sight of past achievements. Design has become more complex in this modern age, but it would be incorrect to say that complex design did not exist in times past. Before the internet, aircraft were built, global communication systems existed, men went to the moon. What can we learn, if anything, by looking at the methods used to design complex products in the past? How can we apply what we learnt from the past to the future?
  • Design Education - A Reversed Method to Fill and Information and Knowledge Gap Between Full-Time and Part-Time Students

    Sole, Martin; Barber, Patrick; Ian, Turner; University of Derby (The Design Society, 2021-08)
    Teachers in schools, tutors in colleges, and lecturers in universities are all required to have specific teaching qualifications. As part of the qualification, it is normal to study tried and tested pedological theories. Some examples are Bloom’s Taxonomy, Constructivism, and Experiential Learning. This paper identifies a gap in the information and knowledge required of student design engineers studying on a full-time course, when compared to part-time students. To redress this gap, it is suggested that no new theories are required but just a new method of applying an old theory, the application of Bloom’s Taxonomy in reverse alongside reverse engineering. An example of applying this method to a class of design engineers in their final year of a BEng (Hons) Mechanical Engineering is provided.
  • Performance evaluation of machine learning techniques for fault diagnosis in vehicle fleet tracking modules

    Sepulevene, Luis; Drummond, Isabela; Kuehne, Bruno Tardiole; Frinhani, Rafael; Filho, Dionisio Leite; Peixoto, Maycon; Reiff-Marganiec, Stephan; Batista, Bruno; Federal University of Itajubá, Itajubá, Brazil; Federal University of Mato Grosso do Sul, Ponta Porã, Brazil; et al. (Oxford University Press, 2021-05-14)
    With industry 4.0, data-based approaches are in vogue. However, extracting the essential features is not a trivial task and greatly influences the fi nal result. There is also a need for specialized system knowledge to monitor the environment and diagnose faults. In this context, the diagnosis of faults is signi cant, for example, in a vehicle fleet monitoring system, since it is possible to diagnose faults even before the customer is aware of the fault, minimizing the maintenance costs of the modules. In this paper, several models using Machine Learning (ML) techniques were applied and analyzed during the fault diagnosis process in vehicle fleet tracking modules. Two approaches were proposed, "With Knowledge" and "Without Knowledge", to explore the dataset using ML techniques to generate classi fiers that can assist in the fault diagnosis process. The approach "With Knowledge" performs the feature extraction manually, using the ML techniques: Random Forest, Naive Bayes, Support Vector Machine (SVM) and Multi Layer Perceptron (MLP); on the other hand, the approach "Without Knowledge" performs an automatic feature extraction, through a Convolutional Neural Network (CNN). The results showed that the proposed approaches are promising. The best models with manual feature extraction obtained a precision of 99.76% and 99.68% for detection and detection and isolation of faults, respectively, in the provided dataset. The best models performing an automatic feature extraction obtained respectively 88.43% and 54.98% for detection and detection and isolation of failures.
  • The effect of fine droplets on laminar propagation speed of a strained acetone-methane flame: Experiment and simulations

    Fan, Luming; Tian, Bo; Chong, Cheng Tung; Jaafar, Mohammad Nazri Mohd; Tanno, Kenji; McGrath, Dante; Oliveira, Pedro M.de; Rogg, Bernd; Hochgreb, Simone; University of Derby; et al. (Elsevier, 2021-07-31)
    In this study, we investigate the effect of the presence of fuel droplets, their size and concentration, on stretched laminar flame speeds. We consider premixed strained methane/air mixtures, with the addition of small acetone droplets, and compare the flame velocity field behaviour to that of the fully vaporized mixture. An impinging stagnation flame configuration is used, to which a narrowly distributed polydisperse mist of acetone droplets is added. Total acetone molar concentrations between 9% and 20% per mole of methane are used, corresponding to 18.6% and 41.4% of the total fuel energy. The Sauter Mean Diameter (SMD) of acetone droplets is varied from 1.0 to 4.7 μm by carefully tuning the air flow rate passing through an atomizer. The droplet size distribution is characterized by a Phase Doppler Anamometry (PDA) system at the outlet of the burner. The flame propagation speed is measured using Particle Image Velocimetry (PIV) for overall equivalence ratios ranging from 0.8 to 1.4 at various strain rates, and the result is compared with a reference case in which acetone was fully vaporized. Unlike the fully vaporized flame, a two-stage reaction flame structure is observed for all droplet cases: a blue premixed flame front followed by a reddish luminous zone. Comparison of the results between gas-only and droplet-laden cases shows that the mean reference burning velocity of the mixture is significantly enhanced when droplets are present under rich cases, whereas the opposite is true for stoichiometric and lean cases. The mean droplet size also changes the relationship between flame speed and strain rate, especially for rich cases. The result suggests that with typical conditions found in laminar strained flames, even for the finest droplets that may have been vaporized before reaching the flame front, the resulting inhomogeneities may lead the flame to behaves differently from the well-premixed gaseous counterpart. Simulations at similar conditions are performed using a two-phase counterflow flame model to compare with experimental data. Model results of reference velocities do not compare well with observations, and the possible reasons for this behaviour are discussed, including the difficulties in determining the pre-vaporization process and thus the boundary conditions, as well as the fidelity of the current point-source based 1D model.
  • Thermal Fatigue Life of Ball Grid Array (BGA) Solder Joints Made From Different Alloy Compositions

    Depiver, Joshua Adeniyi; Sabuj, Mallik; Amalu, Emeka H; University of Derby; Teeside University (Elsevier, 2021-04-27)
    As temperature cycling drives fatigue failure of solder joints in electronic modules, characterisation of the thermal fatigue response of different solder alloy formulations in BGA solder joints functioning in mission-critical systems has become crucial. Four different lead-free and one eutectic lead-based solder alloys in BGA solder joints are characterised against their thermal fatigue lives (TFLs) to predict their mean-time-to-failure for preventive maintenance advice. Five finite elements (FE) models of the assemblies of the BGAs with the different solder alloy compositions are created with SolidWorks. The models are subjected to standard IEC 60749-25 temperature cycling in ANSYS mechanical package environment. Plastic strain, shear strain, plastic shear strain, and accumulated creep energy density responses of the solder joints are obtained and inputted into established life prediction models – Coffin Manson, Engelmaier, Solomon and Syed – to determine the lives of the models. SAC405 joints have the highest predicted TFL of circa 13.2 years, while SAC387 joints have the least life of circa 1.4 years. The predicted lives are inversely proportional to the magnitude of the areas of stress-strain hysteresis loops of the BGA solder joints. The prediction models are significantly not consistent in predicted magnitudes of TFLs across the solder joints. With circa 838% variation in the magnitudes of TFL predicted for Sn63Pb37, the damage parameters used in the models played a critical role and justifies that a combination of several failure modes drives solder joints damage. This research provides a technique for determining the preventive maintenance time of BGA components in mission-critical systems. It proposes developing a new life prediction model based on a combination of the damage parameters for improved prediction.
  • Detection of Cover Collapse Doline and Other Epikarst Features by Multiple Geophysical Techniques, Case Study of Tarimba Cave, Brazil

    Hussain, Yawar; Uagoda, Rogerio; Borges, Welitom; Prado, Renato; Hamza, Omar; Cárdenas-Soto, Martín; Havenith, Hans-Balder; Dou, Jie; Clemson University, Clemson, SC 29634, USA; University of Brasilia, Brasilia 70910-900, Brazil; et al. (MDPI, 2020-10-12)
    Reliable characterization of the karst system is essential for risk assessment where many associated hazards (e.g., cover-collapse dolines and groundwater pollution) can affect natural and built environments, threatening public safety. The use of multiple geophysical approaches may offer an improved way to investigate such cover-collapse sinkholes and aid in geohazard risk assessments. In this paper, covered karst, which has two types of shallow caves (vadose and fluvial) located in Tarimba (Goias, Brazil), was investigated using various geophysical methods to evaluate their efficiency in the delineation of the geometry of sediments filled sinkhole. The methods used for the investigation were Electrical Resistivity Tomography (ERT), Seismic Refraction Survey (SRS), Seismic Refraction Tomography (SRT) and the Very Low-Frequency Electromagnetic (VLF-EM) method. The study developed several (2D) sections of the measured physical properties, including P-wave velocity and electrical resistivity, as well as the induced current (because of local bodies). For the analysis and processing of the data obtained from these methods, the following approaches were adopted: ERT inversion using a least-square scheme, Karous-Hjelt filter for VLF-EM data and time-distance curves and Vp cross-sections for the SRS. The refraction data analysis showed three-layered stratigraphy topsoil, claystone and carbonate bedrock, respectively. The findings obtained from ERT (three-layered stratigraphy and sediment-filled doline), as well as VLF-EM (fractured or filled caves as a positive anomaly), were found to be consistent with the actual field conditions. However, the SRS and SRT methods did not show the collapsed material and reached the limited depth because of shorter profile lengths. The study provides a reasonable basis for the development of an integrated geophysical approach for site characterization of karst systems, particularly the perched tank and collapse doline.
  • On wind turbine power fluctuations induced by large-scale motions

    Ahmadi, Mohammad; Yang, Zhiyin; University of Derby (Elsevier, 2021-04-21)
    Our current understanding on the dynamic interaction between large-scale motions in the approaching turbulent flow and wind turbine power is very limited. To address this, numerical studies of a small-scale three-bladed horizontal axis wind turbine with cylinders placed in front of it to produce energetic coherent structures of varying scale relative to the turbine size have been carried out to examine the temporary variations of the turbine power. The predicted spectra reveal a strong interaction between large-scale turbulent motions generated by cylinders and the instantaneous turbine power. More specifically, it shows how the large dominant turbulent scales of incoming flow affect the spectral characteristics of turbine power, i.e, determining the level and trend of the turbine power spectrum. Comparisons reveal that there are two critical frequencies recognisable in the turbine power spectrum: the first one, close to the turbine rotational frequency, above which the coupling of upstream flow and turbine power disappears; the second one, identified for the first time and related to the dominant large-scale motions which dictate the level and trend of the turbine power spectrum. This study also shows that the strong scale-to-scale interaction between the upstream flow and turbine power reported previously does not appear at high Reynolds numbers.
  • Graph and Network Theory for the Analysis of Criminal Networks

    Cavallaro, Lucia; Bagdasar, Ovidiu; De Meo, Pasquale; Fumara, Giacomo; Liotta, Antonio; University of Derby; University of Messina, Italy; Free University of Bozen-Bolzano, Italy (Springer, Cham, 2021-02-19)
    Social Network Analysis is the use of Network and Graph Theory to study social phenomena, which was found to be highly relevant in areas like Criminology. This chapter provides an overview of key methods and tools that may be used for the analysis of criminal networks, which are presented in a real-world case study. Starting from available juridical acts, we have extracted data on the interactions among suspects within two Sicilian Mafia clans, obtaining two weighted undirected graphs. Then, we have investigated the roles of these weights on the criminal networks properties, focusing on two key features: weight distribution and shortest path length. We also present an experiment that aims to construct an artificial network which mirrors criminal behaviours. To this end, we have conducted a comparative degree distribution analysis between the real criminal networks, using some of the most popular artificial network models: Watts-Strogats, Erdős-Rényi, and Barabási-Albert, with some topology variations. This chapter will be a valuable tool for researchers who wish to employ social network analysis within their own area of interest.

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