Google matrix of business process management

Development of efficient business process models and determination of their characteristic properties are subject of intense interdisciplinary research. Here, we consider a business process model as a directed graph. Its nodes correspond to the units identified by the modeler and the link direction indicates the causal dependencies between units. It is of primary interest to obtain the stationary flow on such a directed graph, which corresponds to the steady-state of a firm during the business process. Following the ideas developed recently for the World Wide Web, we construct the Google matrix for our business process model and analyze its spectral properties. The importance of nodes is characterized by Page-Rank and recently proposed CheiRank and 2DRank, respectively. The results show that this two-dimensional ranking gives a significant information about the influence and communication properties of business model units. We argue that the Google matrix method, described here, provides a new efficient tool helping companies to make their decisions on how to evolve in the exceedingly dynamic global market.Comment: submitted to European Journal of Physics

Emergence of influential spreaders in modified rumor models

The burst in the use of online social networks over the last decade has provided evidence that current rumor spreading models miss some fundamental ingredients in order to reproduce how information is disseminated. In particular, recent literature has revealed that these models fail to reproduce the fact that some nodes in a network have an influential role when it comes to spread a piece of information. In this work, we introduce two mechanisms with the aim of filling the gap between theoretical and experimental results. The first model introduces the assumption that spreaders are not always active whereas the second model considers the possibility that an ignorant is not interested in spreading the rumor. In both cases, results from numerical simulations show a higher adhesion to real data than classical rumor spreading models. Our results shed some light on the mechanisms underlying the spreading of information and ideas in large social systems and pave the way for more realistic diffusion models.Comment: 14 Pages, 6 figures, accepted for publication in Journal of Statistical Physic

Quantifying the effect of temporal resolution on time-varying networks

Time-varying networks describe a wide array of systems whose constituents and interactions evolve over time. They are defined by an ordered stream of interactions between nodes, yet they are often represented in terms of a sequence of static networks, each aggregating all edges and nodes present in a time interval of size Δt. In this work we quantify the impact of an arbitrary Δt on the description of a dynamical process taking place upon a time-varying network. We focus on the elementary random walk, and put forth a simple mathematical framework that well describes the behavior observed on real datasets. The analytical description of the bias introduced by time integrating techniques represents a step forward in the correct characterization of dynamical processes on time-varying graphs

An AI approach to Collecting and Analyzing Human Interactions with Urban Environments

Thanks to advances in Internet of Things and crowd-sensing, it is possible to collect vast amounts of urban data, to better understand how citizens interact with cities and, in turn, improve human well-being in urban environments. This is a scientifically challenging proposition, as it requires new methods to fuse objective (heterogeneous) data (e.g. people location trails and sensors data) with subjective (perceptual) data (e.g. the citizens’ quality of experience collected through feedback forms). When it comes to vast urban areas, collecting statistically significant data is a daunting task; thus new data-collection methods are required too. In this work, we turn to artificial intelligence (AI) to address these challenges, introducing a method whereby the objective, sensor data is analyzed in real-time to scope down the test matrix of the subjective questionnaires. In turn, subjective responses are parsed through AI models to extract further objective information. The outcome is an interactive data analysis framework for urban environments, which we put to test in the context of a citizens’ well-being project. In our pilot study, each new entry (objective or subjective) is parsed through the AI engine to determine which action maximizes the information gain. This translates into a particular question being fired at a specific moment and place, to a specific person. With our AI data collection method, we can reach statistical significance much faster, achieving (in our city-wide pilot study) a 41% acceleration factor and a 75% reduction in intrusiveness. Our study opens new avenues in urban science, with potential applications in urban planning, citizen’s well-being projects, and sociology, to mention but a few cases

Photochemical dihydrogen production using an analogue of the active site of [NiFe] hydrogenase

The photoproduction of dihydrogen (H2) by a low molecular weight analogue of the active site of [NiFe] hydrogenase has been investigated by the reduction of the [NiFe2] cluster, 1, by a photosensitier PS (PS = [ReCl(CO)3(bpy)] or [Ru(bpy)3][PF6]2). Reductive quenching of the 3MLCT excited state of the photosensitiser by NEt3 or N(CH2CH2OH)3 (TEOA) generates PS•−, and subsequent intermolecular electron transfer to 1 produces the reduced anionic form of 1. Time-resolved infrared spectroscopy (TRIR) has been used to probe the intermediates throughout the reduction of 1 and subsequent photocatalytic H2 production from [HTEOA][BF4], which was monitored by gas chromatography. Two structural isomers of the reduced form of 1 (1a•− and 1b•−) were detected by Fourier transform infrared spectroscopy (FTIR) in both CH3CN and DMF (dimethylformamide), while only 1a•− was detected in CH2Cl2. Structures for these intermediates are proposed from the results of density functional theory calculations and FTIR spectroscopy. 1a•− is assigned to a similar structure to 1 with six terminal carbonyl ligands, while calculations suggest that in 1b•− two of the carbonyl groups bridge the Fe centres, consistent with the peak observed at 1714 cm−1 in the FTIR spectrum for 1b•− in CH3CN, assigned to a ν(CO) stretching vibration. The formation of 1a•− and 1b•− and the production of H2 was studied in CH3CN, DMF and CH2Cl2. Although the more catalytically active species (1a•− or 1b•−) could not be determined, photocatalysis was observed only in CH3CN and DMF

The role of endogenous and exogenous mechanisms in the formation of R&D networks

We develop an agent-based model of strategic link formation in Research and Development (R&D)networks. Empirical evidence has shown that the growth of these networks is driven by mechanisms whichare both endogenous to the system (that is, depending on existing alliances patterns) and exogenous (that is, driven by an exploratory search for newcomer firms). Extant research to date has not investigated both mechanisms simultaneously in a comparative manner. To overcome this limitation, we develop a general modeling framework to shed light on the relative importance of these two mechanisms. We test our model against a comprehensive dataset, listing cross-country and cross-sectoral R&D alliances from 1984 to 2009. Our results show that by fitting only three macroscopic properties of the network topology, this framework is able to reproduce a number of micro-level measures, including the distributions of degree, local clustering, path length and component size, and the emergence of network clusters. Furthermore, by estimating the link probabilities towards newcomers and established firms from the data, we find that endogenous mechanisms are predominant over the exogenous ones in the network formation, thus quantifying the importance of existing structures in selecting partner firms

Autoimmune and autoinflammatory mechanisms in uveitis

The eye, as currently viewed, is neither immunologically ignorant nor sequestered from the systemic environment. The eye utilises distinct immunoregulatory mechanisms to preserve tissue and cellular function in the face of immune-mediated insult; clinically, inflammation following such an insult is termed uveitis. The intra-ocular inflammation in uveitis may be clinically obvious as a result of infection (e.g. toxoplasma, herpes), but in the main infection, if any, remains covert. We now recognise that healthy tissues including the retina have regulatory mechanisms imparted by control of myeloid cells through receptors (e.g. CD200R) and soluble inhibitory factors (e.g. alpha-MSH), regulation of the blood retinal barrier, and active immune surveillance. Once homoeostasis has been disrupted and inflammation ensues, the mechanisms to regulate inflammation, including T cell apoptosis, generation of Treg cells, and myeloid cell suppression in situ, are less successful. Why inflammation becomes persistent remains unknown, but extrapolating from animal models, possibilities include differential trafficking of T cells from the retina, residency of CD8(+) T cells, and alterations of myeloid cell phenotype and function. Translating lessons learned from animal models to humans has been helped by system biology approaches and informatics, which suggest that diseased animals and people share similar changes in T cell phenotypes and monocyte function to date. Together the data infer a possible cryptic infectious drive in uveitis that unlocks and drives persistent autoimmune responses, or promotes further innate immune responses. Thus there may be many mechanisms in common with those observed in autoinflammatory disorders

Distinct gene subsets in pterygia formation and recurrence: dissecting complex biological phenomenon using genome wide expression data

<p>Abstract</p> <p>Background</p> <p>Pterygium is a common ocular surface disease characterized by fibrovascular invasion of the cornea and is sight-threatening due to astigmatism, tear film disturbance, or occlusion of the visual axis. However, the mechanisms for formation and post-surgical recurrence of pterygium are not understood, and a valid animal model does not exist. Here, we investigated the possible mechanisms of pterygium pathogenesis and recurrence.</p> <p>Methods</p> <p>First we performed a genome wide expression analysis (human Affymetrix Genechip, >22000 genes) with principal component analysis and clustering techniques, and validated expression of key molecules with PCR. The controls for this study were the un-involved conjunctival tissue of the same eye obtained during the surgical resection of the lesions. Interesting molecules were further investigated with immunohistochemistry, Western blots, and comparison with tear proteins from pterygium patients.</p> <p>Results</p> <p>Principal component analysis in pterygium indicated a signature of matrix-related structural proteins, including fibronectin-1 (both splice-forms), collagen-1A2, keratin-12 and small proline rich protein-1. Immunofluorescence showed strong expression of keratin-6A in all layers, especially the superficial layers, of pterygium epithelium, but absent in the control, with up-regulation and nuclear accumulation of the cell adhesion molecule CD24 in the pterygium epithelium. Western blot shows increased protein expression of beta-microseminoprotein, a protein up-regulated in human cutaneous squamous cell carcinoma. Gene products of 22 up-regulated genes in pterygium have also been found by us in human tears using nano-electrospray-liquid chromatography/mass spectrometry after pterygium surgery. Recurrent disease was associated with up-regulation of sialophorin, a negative regulator of cell adhesion, and <it>never in mitosis a</it>-5, known to be involved in cell motility.</p> <p>Conclusion</p> <p>Aberrant wound healing is therefore a key process in this disease, and strategies in wound remodeling may be appropriate in halting pterygium or its recurrence. For patients demonstrating a profile of 'recurrence', it may be necessary to manage as a poorer prognostic case and perhaps, more adjunctive treatment after resection of the primary lesion.</p

Diffusion on networked systems is a question of time or structure

Network science investigates the architecture of complex systems to understand their functional and dynamical properties. Structural patterns such as communities shape diffusive processes on networks. However, these results hold under the strong assumption that networks are static entities where temporal aspects can be neglected. Here we propose a generalized formalism for linear dynamics on complex networks, able to incorporate statistical properties of the timings at which events occur. We show that the diffusion dynamics is affected by the network community structure and by the temporal properties of waiting times between events. We identify the main mechanism—network structure, burstiness or fat tails of waiting times—determining the relaxation times of stochastic processes on temporal networks, in the absence of temporal–structure correlations. We identify situations when fine-scale structure can be discarded from the description of the dynamics or, conversely, when a fully detailed model is required due to temporal heterogeneities

Towards a characterization of behavior-disease models

The last decade saw the advent of increasingly realistic epidemic models that leverage on the availability of highly detailed census and human mobility data. Data-driven models aim at a granularity down to the level of households or single individuals. However, relatively little systematic work has been done to provide coupled behavior-disease models able to close the feedback loop between behavioral changes triggered in the population by an individual's perception of the disease spread and the actual disease spread itself. While models lacking this coupling can be extremely successful in mild epidemics, they obviously will be of limited use in situations where social disruption or behavioral alterations are induced in the population by knowledge of the disease. Here we propose a characterization of a set of prototypical mechanisms for self-initiated social distancing induced by local and non-local prevalence-based information available to individuals in the population. We characterize the effects of these mechanisms in the framework of a compartmental scheme that enlarges the basic SIR model by considering separate behavioral classes within the population. The transition of individuals in/out of behavioral classes is coupled with the spreading of the disease and provides a rich phase space with multiple epidemic peaks and tipping points. The class of models presented here can be used in the case of data-driven computational approaches to analyze scenarios of social adaptation and behavioral change.Comment: 24 pages, 15 figure