Complex Network Of Connections Research Articles

Rhizomic authoritarianism: power, biopolitics and transnational authoritarian practices in Cameroon

ABSTRACT This article discusses a rhizomic authoritarianism that generally operates through a complex network of connections. It provides a genealogical reading of the authoritarian rule in Cameroon, with a particular focus on the roles of France and China, using both primary and secondary sources. Specifically, it seeks to enlighten the complex processes through which a transnational authoritarianism has been established in Cameroon over the past six decades, as well as how the formation of strategic alliances between local political actors and international institutions has contributed to the normalization of authoritarian policies and practices that sometimes occur in transregional contexts. The paper’s main argument is that the authoritarian system Cameroon has experienced since the late 1950s is rhizomatic in nature and is partially the result of the hegemonic interventions of international powers such as France and China in the country's politics and economy, as well as its development and modernization processes.

Jamail A. Kamlian and Juvanni A. Caballero. Informal Credit (Pagsanda) Among the Tausug of Sulu, Mindanao, Philippines: The Interrelation of Economic, Social and Religious Factors (New York: The Edwin Mellen Press, 2018). A Book Review

Focusing on an indigenously evolved informal credit system (pagsanda), the book dwells on the centrality of money-lending and indebtedness as the fulcrum of the dynamics of society. How is it that the pagsanda as a communal self-help mechanism intended to lend a hand in moments of need is tied up with, or even central to the dynamics of conflict among the Tausugs in Sulu? How can something so condemned as un-Islamic become so prevalent and widespread among the Muslims in the area? The book opens the discourse to help find answers to these questions. And it did so by weaving a narrative that captures the interplay of various issue areas that feed on the other to create a self-sustaining system that becomes a precarious social powder keg. The pagsanda’s pristine form of unselfish assistance has clearly morphed through time into many variants of exploitative and even oppressive callous social exchange. By analyzing the complex network of connections between the pagsanda, local politics, economics, socio-cultural practices and violent conflict, the book sheds light into the nature and causes of the social dynamics in Sulu.

Identifying passenger preferences for alternative ferry transport hubs

The demand and preference for alternative ferry transport hubs are discussed using the region of the Aegean Archipelago as a case study. A complex network of connections among the islands and to the Greek peninsula serves the needs of tourism, transportation of goods and services, and islanders traveling to and from the mainland.However, this system has shortcomings; in particular, interconnections for many islands are only possible via the mainland ports of Greece. Interconnectivity among the islands is important for the economic, social, and territorial cohesion of the archipelago as well as to meet the needs of tourism. This study looks into passengers’ acceptance of two alternative minor island connection ports that would increase remote island connectivity. Econometric tools and a stated preferences survey were used to assess the acceptance of minor island hubs versus the existing major mainland maritime hubs. Discrete choice models were estimated in which the attributes of the alternatives include travel times, waiting times at the interchange hubs, fares, and selected socioeconomic parameters of the respondents. The results show a positive trend in favor of the proposed minor hub system. In conclusion, this innovative methodology could be used for policy testing and transferable to similar archipelagic regions.

An image encryption scheme using a chaotic neural network and a network with multistable hyperchaos

In this paper a new image encryption scheme is proposed, by which the pixel values are bit-level shuffled by the encryption sequences generated by a complex network, while the pixel positions of diffused images are scrambled by the chaotic sequences generated by a time-delayed neural network. In this way the key space is greatly enlarged through the simultaneous use of the time-delayed neural network and the complex network with a high dimensional multistable hyperchaotic system as nodal model. In particular, different nodes in the complex network are selected to generate the encryption and the decryption sequences. To achieve the synchronization accuracy necessarily for correct decryption, the number of nodes needed in the fully connected complex network is investigated and determined. Through applications on different kinds of plain images, the proposed algorithm exhibits excellent performance on security, i.e., with better statistical and differential characteristics, better plain-image key sensitivity and a large enough key space. In addition the encryption scheme can also resist various knowing attacks.

Complex network recognition of electrocardiograph signals in health and myocardial infarction patients based on multiplex visibility graph

The visibility graph algorithm proves to be a simple and efficient method to transform time series into complex network and has been widely used in time series analysis because it can inherit the dynamic characteristics of original time series in topological structure. Now, visibility graph analysis of univariate time series has become mature gradually. However, most of complex systems in real world are multi-dimensional, so the univariate analysis is difficult to describe the global characteristics when applied to multi-dimensional series. In this paper, a novel method of analyzing the multivariate time series is proposed. For patients with myocardial infarction and healthy subjects, the 12-lead electrocardiogram signals of each individual are considered as a multivariate time series, which is transformed into a multiplex visibility graph through visibility graph algorithm and then mapped to fully connected complex network. Each node of the network corresponds to a lead, and the inter-layer mutual information between visibility graphs of two leads represents the weight of edges. Owing to the fully connected network of different groups showing an identical topological structure, the dynamic characteristics of different individuals cannot be uniquely represented. Therefore, we reconstruct the fully connected network according to inter-layer mutual information, and when the value of inter-layer mutual information is less than the threshold we set, the edge corresponding to the inter-layer mutual information is deleted. We extract average weighted degree and average weighted clustering coefficient of reconstructed networks for recognizing the 12-lead ECG signals of healthy subjects and myocardial infarction patients. Moreover, multiscale weighted distribution entropy is also introduced to analyze the relation between the length of original time series and final recognition result. Owing to higher average weighted degree and average weighted clustering coefficient of healthy subjects, their reconstructed networks show a more regular structure, higher complexity and connectivity, and the healthy subjects can be distinguished from patients with myocardial infarction, whose reconstructed networks are sparser. Experimental results show that the identification accuracy of both parameters, average weighted degree and average weighted clustering coefficient, reaches 93.3%, which can distinguish between the 12-lead electrocardiograph signals of healthy people and patients with myocardial infarction, and realize the automatic detection of myocardial infarction.

Structure, Function, and Genetics of the Cerebellum in Autism.

Autism spectrum disorders are common neurodevelopmental disorders that are defined by core behavioral symptoms but have diverse genetic and environmental risk factors. Despite its etiological heterogeneity, several unifying theories of autism have been proposed, including a central role for cerebellar dysfunction. The cerebellum follows a protracted course of development that culminates in an exquisitely crafted brain structure containing over half of the neurons in the entire brain densely packed into a highly organized structure. Through its complex network of connections with cortical and subcortical brain regions, the cerebellum acts as a sensorimotor regulator and affects changes in executive and limbic processing. In this review, we summarize the structural, functional, and genetic contributions of the cerebellum to autism.

Influences and aspirations in the production of national projects in Lebanon and Kuwait

PurposeBy looking at a selection of iconic modern projects designed by or commissioned to the prominent but not well-examined architect Sami Abdul Baki both in Lebanon and Kuwait during his most productive years in the 50s, this paper attempts to identify first main trends, influences and ideologies that shaped these works at the peak of modern architectural development in the region. Through these examples, the paper then aims at retracing predominant trajectories of intellectual capital exchange and transfer of knowledge between Lebanon and Kuwait. These can go far beyond their territorial boundaries, without claiming a single grand-narrative that describes the modern architectural development in any of the two countries.Design/methodology/approachThe data collected from discourse analysis, interviews and biographical notes were mapped into a schematic diagram illustrating a complex network of connections and multidisciplinary involvement in projects.FindingsHowever, the outcome did not generate a dominant theme for the projects or expertise of the architect.Originality/valueIt is very likely that Sami Abdul Baki's strong political dimension and quality as a mediator or facilitator in addition to his strong network of contacts played a significant role in the project commissions that he has won as an architect/engineer in Kuwait, Lebanon, Germany and other countries.

Quantum Machine Learning for 6G Communication Networks: State-of-the-Art and Vision for the Future

The upcoming fifth generation (5G) of wireless networks is expected to lay a foundation of intelligent networks with the provision of some isolated artificial intelligence (AI) operations. However, fully intelligent network orchestration and management for providing innovative services will only be realized in Beyond 5G (B5G) networks. To this end, we envisage that the sixth generation (6G) of wireless networks will be driven by on-demand self-reconfiguration to ensure a many-fold increase in the network performance and service types. The increasingly stringent performance requirements of emerging networks may finally trigger the deployment of some interesting new technologies, such as large intelligent surfaces, electromagnetic–orbital angular momentum, visible light communications, and cell-free communications, to name a few. Our vision for 6G is a massively connected complex network capable of rapidly responding to the users’ service calls through real-time learning of the network state as described by the network edge (e.g., base-station locations and cache contents), air interface (e.g., radio spectrum and propagation channel), and the user-side (e.g., battery-life and locations). The multi-state, multi-dimensional nature of the network state, requiring the real-time knowledge, can be viewed as a quantum uncertainty problem. In this regard, the emerging paradigms of machine learning (ML), quantum computing (QC), and quantum ML (QML) and their synergies with communication networks can be considered as core 6G enablers. Considering these potentials, starting with the 5G target services and enabling technologies, we provide a comprehensive review of the related state of the art in the domains of ML (including deep learning), QC, and QML and identify their potential benefits, issues, and use cases for their applications in the B5G networks. Subsequently, we propose a novel QC-assisted and QML-based framework for 6G communication networks while articulating its challenges and potential enabling technologies at the network infrastructure, network edge, air interface, and user end. Finally, some promising future research directions for the quantum- and QML-assisted B5G networks are identified and discussed.

Bridges Between Bipolar and Borderline Personality Disorders: Clarifying Comorbidity Through the Analysis of the Complex Network of Connections Between Symptoms

Background: The reasons for the high rates of comorbidity between Bipolar (BD) and Borderline Personality (BPD) disorders remain elusive, due to the vast array of shared clinical features, which makes the differential diagnosis difficult. This constitutes an obstacle to provide quality of care services, which results in detrimental effects on individual’s mental health. The analysis of the complex network of connections between symptoms of both disorders is a promising pathway to uncover the mechanisms underlying the comorbidity structure of both disorders. Goals: In this study, we explored the comorbidity network that represents the connections between 32 DSM-5 symptoms of BD and BPD in order to (1) compare its modular structure (i.e., the constitution of cohesive subgroups of symptoms within the comorbidity network) with the nosographic proposal of the DSM-5; (2) distinguish between the different roles those symptoms have in the comorbidity network and identify the symptoms that overlap and bridge both disorders, as well as the distinctive symptoms that better discriminate them; (3) identify the most central symptoms and those with the highest impact on the strength or on the structure of the connections on the comorbidity network; and (4) analyze the association between symptoms roles and their centrality and impact.Methods: An epidemiological sample from the National Comorbidity Survey: Baseline (NCS) was analyzed. Data regarding bipolar and borderline personality symptoms were collected through the Composite Network International Diagnostic Interview (CIDI). The network of complex interactions between symptoms was estimated using the Ising model with the L1-regularization penalty (EBIC) and the nosographic structure was detailed with Moduland algorithms. Results: Data regarding an overall sample of 7556 individuals was analyzed (48.6% male, Mage = 33.400 years, SDage = 10.447). Results revealed differences between the modular structure of the comorbidity network and the DSM-5 nosographic proposal, namely about unstable relationships and substance abuse, that were assigned to the module constituted by symptoms of manic episode (ME). Symptoms such as money spending and sexual indiscretions, that overlap ME and BPD in the DSM-5, were assigned to the ME module. Psychomotor agitation, which overlaps depressive episode (DE) and ME in the DSM-5, was assigned to the DE module. Additionally, emptiness and worthlessness were identified as bridge symptoms between DE and BPD; anger and substance abuse between ME and BPD; and unstable relationships and psychomotor agitation between DE and ME. Fatigue was the most distinctive symptom of the DE module, unstable relationships of the ME module, and anger of the BPD module. Strength centrality (r = .61, 95%CI [.33, .79], p < .001) and modular bridgeness (r = .64, 95%CI [.38, .81], p < .001) were positively correlated with the impact on the structure of the comorbidity network; and modular overlap was negatively correlated with the impact on the strength (r = -.43, 95%CI [-.10, -.68], p = .01) of its connections. Discussion: Results suggest a similar structure of the comorbidity network to the nosographic proposal of DSM-5. Distinctive and bridge symptoms were identified for each disorder which might help with the differential diagnosis. It can also help us to unveil possible development pathways of comorbidity that might promote an improvement in psychological treatments. Keywords: Bipolar disorder, Borderline personality disorder, Network analysis, Comorbidity.

Joining the dots - protein-RNA interactions mediating local mRNA translation in neurons.

Establishing and maintaining the complex network of connections required for neuronal communication requires the transport and insitu translation of large groups of mRNAs to create local proteomes. In this Review, we discuss the regulation of local mRNA translation in neurons and the RNA-binding proteins that recognise RNA zipcode elements and connect the mRNAs to the cellular transport networks, as well as regulate their translation control. However, mRNA recognition by the regulatory proteins is mediated by the combinatorial action of multiple RNA-binding domains. This increases the specificity and affinity of the interaction, while allowing the protein to recognise a diverse set of targets and mediate a range of mechanisms for translational regulation. The structural and molecular understanding of the interactions can be used together with novel microscopy and transcriptome-wide data to build a mechanistic framework for the regulation of local mRNA translation.

Diffusion MRI fiber tractography of the brain.

The ability of fiber tractography to delineate non-invasively the white matter fiber pathways of the brain raises possibilities for clinical applications and offers enormous potential for neuroscience. In the last decade, fiber tracking has become the method of choice to investigate quantitative MRI parameters in specific bundles of white matter. For neurosurgeons, it is quickly becoming an invaluable tool for the planning of surgery, allowing for visualization and localization of important white matter pathways before and even during surgery. Fiber tracking has also claimed a central role in the field of "connectomics," a technique that builds and studies comprehensive maps of the complex network of connections within the brain, and to which significant resources have been allocated worldwide. Despite its unique abilities and exciting applications, fiber tracking is not without controversy, in particular when it comes to its interpretation. As neuroscientists are eager to study the brain's connectivity, the quantification of tractography-derived "connection strengths" between distant brain regions is becoming increasingly popular. However, this practice is often frowned upon by fiber-tracking experts. In light of this controversy, this paper provides an overview of the key concepts of tractography, the technical considerations at play, and the different types of tractography algorithm, as well as the common misconceptions and mistakes that surround them. We also highlight the ongoing challenges related to fiber tracking. While recent methodological developments have vastly increased the biological accuracy of fiber tractograms, one should be aware that, even with state-of-the-art techniques, many issues that severely bias the resulting structural "connectomes" remain unresolved.

G-Protein-Coupled Receptor Kinase 2 as a Potential Modulator of the Hallmarks of Cancer.

Malignant features-such as sustained proliferation, refractoriness to growth suppressors, resistance to cell death or aberrant motility, and metastasis-can be triggered by a variety of mutations and signaling adaptations. Signaling nodes can act as cancer-associated factors by cooperating with oncogene-governed pathways or participating in compensatory transduction networks to strengthen tumor properties. G-protein-coupled receptor kinase 2 (GRK2) is arising as one of such nodes. Via its complex network of connections with other cellular proteins, GRK2 contributes to the modulation of basic cellular functions-such as cell proliferation, survival, or motility-and is involved in metabolic homeostasis, inflammation, or angiogenic processes. Moreover, altered GRK2 levels are starting to be reported in different tumoral contexts and shown to promote breast tumorigenesis or to trigger the tumoral angiogenic switch. The ability to modulate several of the hallmarks of cancer puts forward GRK2 as an oncomodifier, able to modulate carcinogenesis in a cell-type specific way.

Restructuring coastal shipping: a participatory experiment

Coastal shipping (ro-ro passenger ferry services) has always been an essential means of domestic transportation, especially for islandic states. In the case of Greece, for instance, it would not be an exaggeration to say that maritime transport is responsible for binding the islands and the mainland together as one nation. Coastal shipping presents particular features and may involve seasonality, wide and/or complex network of connections, red tape and institutional rigidities. Nowadays, mainly due to the global economic crisis, it also faces additional impediments to its effective operation, such as the excessive fuel cost, the limited financial liquidity and lower transport demand. In this respect, it is a most important and urgent challenge to identify and assess, through a bottom-up process, the prevailing views of the stakeholders and the experts on the basic elements, principles and key factors of the structure of a coastal transport system that need to be reformed and redefined. The development of a participatory decision-making methodology is a prerequisite for restructuring this system with a smart, efficient and sustainable way, both in the short and in the long terms. This paper presents the results of an empirical research that aims to develop a participatory decision-making method, based on Delphi, adapted to the particularities of the coastal shipping markets and island communities. The method is applied in a case study of the Greek ferries paradigm, recording the critical issues and drafting a roadmap. Based on key findings, a hierarchy process study on key performance indicators of island transport services as well as on the transport models is also presented in this paper.

Pinning impulsive control algorithms for complex network

In this paper, we further investigate the synchronization of complex dynamical network via pinning control in which a selection of nodes are controlled at discrete times. Different from most existing work, the pinning control algorithms utilize only the impulsive signals at discrete time instants, which may greatly improve the communication channel efficiency and reduce control cost. Two classes of algorithms are designed, one for strongly connected complex network and another for non-strongly connected complex network. It is suggested that in the strongly connected network with suitable coupling strength, a single controller at any one of the network's nodes can always pin the network to its homogeneous solution. In the non-strongly connected case, the location and minimum number of nodes needed to pin the network are determined by the Frobenius normal form of the coupling matrix. In addition, the coupling matrix is not necessarily symmetric or irreducible. Illustrative examples are then given to validate the proposed pinning impulsive control algorithms.

Towards a theory for diffusive coupling functions allowing persistent synchronization

We study synchronization properties of networks of coupled dynamical systems with interaction akin to diffusion. We assume that the isolated node dynamics possesses a forward invariant set on which it has a bounded Jacobian, then we characterize a class of coupling functions that allows for uniformly stable synchronization in connected complex networks—in the sense that there is an open neighbourhood of the initial conditions that is uniformly attracted towards synchronization. Moreover, this stable synchronization persists under perturbations to non-identical node dynamics. We illustrate the theory with numerical examples and conclude with a discussion on embedding these results in a more general framework of spectral dichotomies.

Spatially Distributed Social Complex Networks

We propose a bare-bones stochastic model that takes into account both the geographical distribution of people within a country and their complex network of connections. The model, which is designed to give rise to a scale-free network of social connections and to visually resemble the geographical spread seen in satellite pictures of the Earth at night, gives rise to a power-law distribution for the ranking of cities by population size (but for the largest cities) and reflects the notion that highly connected individuals tend to live in highly populated areas. It also yields some interesting insights regarding Gibrat's law for the rates of city growth (by population size), in partial support of the findings in a recent analysis of real data [Rozenfeld et al., Proc. Natl. Acad. Sci. U.S.A. 105, 18702 (2008)]. The model produces a nontrivial relation between city population and city population density and a superlinear relationship between social connectivity and city population, both of which seem quite in line with real data.

A syntactic classification of the synchronic use of gěi in Beijing Mandarin

This study fills a gap in the literature on the polyfunctional nature of the Chinese ditransitive verb gěi ‘to give’, which has undergone semantic and functional extensions. Our approach differs from previous studies by focusing on a narrowly defined location in time and space, i.e. contemporary Beijing oral language, and by basing our linguistic analysis on data obtained through the systematic sampling of a corpus of spontaneous casual discourse. Based on the existing literature we produce a taxonomy of five extended functions and structures 1) causative verb, 2) passive marker, 3) benefactive/malefactive/dative marker, 4) disposal marker and 5) ditransitive suffix. Of these, the first four share the common linear structure [NP1 gěi NP2 V]. Applying this taxonomy to our data we identified the relative productivity of each of these functions and created a synchronic constructional network of gěi, revealing its complex network of connections.

Network Analysis of Corticocortical Connections Reveals Ventral and Dorsal Processing Streams in Mouse Visual Cortex

Much of the information used for visual perception and visually guided actions is processed in complex networks of connections within the cortex. To understand how this works in the normal brain and to determine the impact of disease, mice are promising models. In primate visual cortex, information is processed in a dorsal stream specialized for visuospatial processing and guided action and a ventral stream for object recognition. Here, we traced the outputs of 10 visual areas and used quantitative graph analytic tools of modern network science to determine, from the projection strengths in 39 cortical targets, the community structure of the network. We found a high density of the cortical graph that exceeded that shown previously in monkey. Each source area showed a unique distribution of projection weights across its targets (i.e., connectivity profile) that was well fit by a lognormal function. Importantly, the community structure was strongly dependent on the location of the source area: outputs from medial/anterior extrastriate areas were more strongly linked to parietal, motor, and limbic cortices, whereas lateral extrastriate areas were preferentially connected to temporal and parahippocampal cortices. These two subnetworks resemble dorsal and ventral cortical streams in primates, demonstrating that the basic layout of cortical networks is conserved across species.

Digital microfluidics: a versatile tool for applications in chemistry, biology and medicine

Digital microfluidics (DMF) has recently emerged as a popular technology for a wide range of applications. In DMF, nanoliter to microliter droplets containing samples and reagents can be manipulated to carry out a range of discrete fluidic operations simply by applying a series of electrical potentials to an array of patterned electrodes coated with a hydrophobic insulator. DMF is distinct from microchannel-based fluidics as it allows for precise control over multiple reagent phases (liquids and solids) in heterogeneous systems with no need for complex networks of connections, microvalves, or pumps. In this review, we discuss the most recent developments in this technology with particular attention to the potential benefits and outstanding challenges for applications in chemistry, biology, and medicine.

Bistable-monostable transition in the Ising model on two connected complex networks

In this paper, we investigate the behavior of the Ising model on two sparsely connected complex networks. The networks have the topology of a random graph or Barabási and Albert scale-free networks. We extend our previous analysis and show that a bistable-monostable phase transition occurs in such systems. During this transition, the magnetization undergoes a discontinuous jump. We calculate the critical temperature analytically for regular random graphs and study a more general case using an iterative map corresponding to mean-field dynamics. The calculations are confirmed by numeric simulations based on Monte Carlo approach.