•ANNs are used to predict tar generation in biomass gasification processes in BFBs.•Different tar sampling methods are considered.•A large number of experimental data are used for the network ...training and validation.•ANNs differentiates between the different sampling techniques predicting tar content accurately.
Tars are one of the main barriers for the implementation of biomass gasification at industrial scale. Among the considerable number of models to predict gas composition, there is a lack of models predicting tar generation in gasification processes, as tar concentration data is far more difficult to collect and analyze. This study makes use of artificial neural networks (ANNs) to predict tar generation in gasification processes carried out in lab-scale bubbling fluidized bed reactors operating with silica sand and woody biomass. An exhaustive review of the existing literature and the different tar collection and analysis methods is conducted to create a consistent database for the ANNs to train on. The model integrates different tar data coming from different sampling protocols and analysis methods (tar protocol and gas chromatography, tar protocol and gravimetric method, and solid phase absorption). The predicted results show good accuracy (R2 > 0.97), concluding this generalized predictive novel model is a useful tool for tar prediction in gasification. The model results are in agreement with the literature, verifying how tar content in the product gas behaves when equivalence ratio (ER) and temperature are varied. The predicted versus experimental values are also compared with previous models for tar prediction. ANN modelling shows a higher accuracy than other models, demonstrating this data-driven modelling can be a good approach for tar content prediction.
A stochastic simplicial SIS model for complex networks Tocino, Angel; Hernández Serrano, Daniel; Hernández-Serrano, Juan ...
Communications in nonlinear science & numerical simulation,
June 2023, 2023-06-00, Letnik:
120
Journal Article
Recenzirano
Odprti dostop
We propose a stochastic epidemiological model for simplicial complex networks by means of a stochastic differential equation (SDE) that extends the mean field approach of the simplicial social ...contagion model. We show that, under appropriate conditions, if the stochastic basic reproductive number is smaller than one, then the disease dies out with probability one; otherwise the solution of the SDE oscillates infinitely often around a point which can be explicitly computed. We perform numerical experiments which illustrate the theoretical results. In addition, we carry out simulations on a real simplicial network and on a synthetic network, which show good agreement with the theoretical and numerical predictions of the SDE.
•A stochastic epidemiological model for simplicial complex networks is proposed.•Simulations on a real simplicial network and on a synthetic network are carried out.•The network simulations show good agreement with the theoretical and numerical results.•Sufficient conditions for the stochastic stability of the model are provided.
In the last few years, the importance of trucks on inland cargo transportation has not stopped increasing. Meanwhile, truck platooning is emerging, along with automated driving, to reduce costs using ...new technologies. In this context, this research aims to provide a first study on the effects of truck platoons on freeways’ road safety, focusing on the reduction of visibility caused by truck platoons with shorter gaps on horizontal curves. This safety issue will also affect motorways and multilane roads. A geometric model has been developed and computed, which provides the available sight distances and the stopping sight distance (SSD) for a vehicle overpassing a platoon in a circular curve without transition curves. There are many variables, such as radius, lane width, vehicle and truck platoon parameters, and relative position. The overpassing vehicle has been included in the model for both human‐driven and automated, considering the adaptive cruise control radar cone of visibility. The main result of this study is the minimum curve radius in order to allow a safe SSD, considering different design criteria. Moreover, depending on the level of automation of the vehicle, this minimum radius will be different, being higher for automated vehicles. Results prove the importance of the studied phenomenon and the necessity to implement further countermeasures. Additionally, a case study where the effects of truck platooning on the visibility of a real motorway stretch are evaluated.
•Higher order degree distribution in simplicial complexes.•Higher-order connectivity structures in complex systems.•Multi combinatorial Laplacacian in a simplicial complex.•Applications of ...Topological Data Analysis to Network Science.•Collaborative simplicial communities in complex networks.
Network Science provides a universal formalism for modelling and studying complex systems based on pairwise interactions between agents. However, many real networks in the social, biological or computer sciences involve interactions among more than two agents, having thus an inherent structure of a simplicial complex. The relevance of an agent in a graph network is given in terms of its degree, and in a simplicial network there are already notions of adjacency and degree for simplices that, as far as we know, are not valid for comparing simplices in different dimensions. We propose new notions of higher-order degrees of adjacency for simplices in a simplicial complex, allowing any dimensional comparison among them and their faces. We introduce multi-parameter boundary and coboundary operators in an oriented simplicial complex and also a novel multi-combinatorial Laplacian is defined. As for the graph or combinatorial Laplacian, the multi-combinatorial Laplacian is shown to be an effective tool for calculating the higher-order degrees presented here. To illustrate the potential applications of these theoretical results, we perform a structural analysis of higher-order connectivity in simplicial-complex networks by studying the associated distributions with these simplicial degrees in 17 real-world datasets coming from different domains such as coauthor networks, cosponsoring Congress bills, contacts in schools, drug abuse warning networks, e-mail networks or publications and users in online forums. We find rich and diverse higher-order connectivity structures and observe that datasets of the same type reflect similar higher-order collaboration patterns. Furthermore, we show that if we use what we have called the maximal simplicial degree (which counts the distinct maximal communities in which our simplex and all its strict sub-communities are contained), then its degree distribution is, in general, surprisingly different from the classical node degree distribution.
The development of flat acoustic lenses for different applications such as biomedical engineering is a topic of great interest. Flat lenses like Fresnel Zone Plates (FZPs) are capable of focusing ...energy beams without the need of concave or convex geometries, which are more difficult to manufacture. One of the possible applications of these type of lenses is tumor ablation through High Intensity Focused Ultrasound (HIFU) therapies with real time Magnetic Resonance Imaging (MRI) monitoring. In order to be MRI compatible, the FZP material cannot have electromagnetic interaction. In this work, a Phase-Reversal FZP (PR-FZP) made of Polylactic Acid (PLA) manufactured with a commercial 3D printer is proposed as a better, more efficient and MRI compatible alternative to conventional Soret FZPs. Phase-Reversal lenses, unlike traditional FZPs, take advantage of all the incident energy by adding phase compensation regions instead of pressure blocking regions. The manufactured PR-FZP achieves 21.9 dB of focal gain, which increases the gain compared to a Soret FZP of its same size by a factor of 4.0 dB. Both numerical and experimental results are presented, demonstrating the improved focusing capabilities of these types of lenses.
•Centrality measures in simplicial complexes.•Degree centrality of a simplex, walks and distance in a simplicial complex.•Simplicial clustering coefficient.•Applications of Topological Data Analysis ...to Network Science.•Collaborative simplicial communities in complex networks.•Higher-order connectivity in complex networks.
Many real networks in social sciences, biological and biomedical sciences or computer science have an inherent structure of simplicial complexes reflecting many-body interactions. Therefore, to analyse topological and dynamical properties of simplicial complex networks centrality measures for simplices need to be proposed. Many of the classical complex networks centralities are based on the degree of a node, so in order to define degree centrality measures for simplices (which would characterise the relevance of a simplicial community in a simplicial network), a different definition of adjacency between simplices is required, since, contrarily to what happens in the vertex case (where there is only upper adjacency), simplices might also have other types of adjacency. The aim of these notes is threefold: first we will use the recently introduced notions of higher order simplicial degrees to propose new degree based centrality measures in simplicial complexes. These theoretical centrality measures, such as the simplicial degree centrality or the eigenvector centrality would allow not only to study the relevance of a simplicial community and the quality of its higher-order connections in a simplicial network, but also they might help to elucidate topological and dynamical properties of simplicial networks; sencond, we define notions of walks and distances in simplicial complexes in order to study connectivity of simplicial networks and to generalise, to the simplicial case, the well known closeness and betweenness centralities (needed for instance to study the relevance of a simplicial community in terms of its ability of transmitting information); third, we propose a new clustering coefficient for simplices in a simplicial network, different from the one knows so far and which generalises the standard graph clustering of a vertex. This measure should be essential to know the density of a simplicial network in terms of its simplicial communities.
Background
The strikingly higher prevalence of migraine in females compared with males is one of the hallmarks of migraine. A large global body of evidence exists on the sex differences in the ...prevalence of migraine with female to male ratios ranging from 2 : 1 to 3 : 1 and peaking in midlife. Some data are available on sex differences in associated symptoms, headache‐related disability and impairment, and healthcare resource utilization in migraine. Few data are available on corresponding sex differences in probable migraine (PM) and other severe headache (ie, nonmigraine‐spectrum severe headache). Gaining a clear understanding of sex differences in a range of severe headache disorders may help differentiate the range of headache types. Herein, we compare sexes on prevalence and a range of clinical variables for migraine, PM, and other severe headache in a large sample from the US population.
Methods
This study analyzed data from the 2004 American Migraine Prevalence and Prevention Study. Total and demographic‐stratified sex‐specific, prevalence estimates of headache subtypes (migraine, PM, and other severe headache) are reported. Log‐binomial models are used to calculate sex‐specific adjusted prevalence ratios and 95% confidence intervals for each across demographic strata. A smoothed sex prevalence ratio (female to male) figure is presented for migraine and PM.
Results
One hundred sixty‐two thousand seven hundred fifty‐six individuals aged 12 and older responded to the 2004 American Migraine Prevalence and Prevention Study survey (64.9% response rate). Twenty‐eight thousand two hundred sixty‐one (17.4%) reported “severe headache” in the preceding year (23.5% of females and 10.6% of males), 11.8% met International Classification of Headache Disorders‐2 criteria for migraine (17.3% of females and 5.7% of males), 4.6% met criteria for PM (5.3% of females and 3.9% of males), and 1.0% were categorized with other severe headache (0.9% of females and 1.0% of males). Sex differences were observed in the prevalence of migraine and PM, but not for other severe headache. Adjusted female to male prevalence ratios ranged from 1.48 to 3.25 across the lifetime for migraine and from 1.22 to 1.53 for PM. Sex differences were also observed in associated symptomology, aura, headache‐related disability, healthcare resource utilization, and diagnosis for migraine and PM. Despite higher rates of migraine diagnosis by a healthcare professional, females with migraine were less likely than males to be using preventive pharmacologic treatment for headache.
Conclusions
In this large, US population sample, both migraine and PM were more common among females, but a sex difference was not observed in the prevalence of other severe headache. The sex difference in migraine and PM held true across age and for most other sociodemographic variables with the exception of race for PM. Females with migraine and PM had higher rates of most migraine symptoms, aura, greater associated impairment, and higher healthcare resource utilization than males. Corresponding sex differences were not observed among individuals with other severe headache on the majority of these comparisons. Results suggest that PM is part of the migraine spectrum whereas other severe headache types are not. Results also substantiate existing literature on sex differences in primary headaches and extend results to additional headache types and related factors.
Objectives.— To estimate the prevalence and distribution of chronic migraine (CM) in the US population and compare the age‐ and sex‐specific profiles of headache‐related disability in persons with CM ...and episodic migraine.
Background.— Global estimates of CM prevalence using various definitions typically range from 1.4% to 2.2%, but the influence of sociodemographic factors has not been completely characterized.
Methods.— The American Migraine Prevalence and Prevention Study mailed surveys to a sample of 120,000 US households selected to represent the US population. Data on headache frequency, symptoms, sociodemographics, and headache‐related disability (using the Migraine Disability Assessment Scale) were obtained. Modified Silberstein–Lipton criteria were used to classify CM (meeting International Classification of Headache Disorders, second edition, criteria for migraine with a headache frequency of ≥15 days over the preceding 3 months).
Results.— Surveys were returned by 162,756 individuals aged ≥12 years; 19,189 individuals (11.79%) met International Classification of Headache Disorders, second edition, criteria for migraine (17.27% of females; 5.72% of males), and 0.91% met criteria for CM (1.29% of females; 0.48% of males). Relative to 12 to 17 year olds, the age‐ and sex‐specific prevalence for CM peaked in the 40s at 1.89% (prevalence ratio 4.57; 95% confidence interval 3.13‐6.67) for females and 0.79% (prevalence ratio 3.35; 95% confidence interval 1.99‐5.63) for males. In univariate and adjusted models, CM prevalence was inversely related to annual household income. Lower income groups had higher rates of CM. Individuals with CM had greater headache‐related disability than those with episodic migraine and were more likely to be in the highest Migraine Disability Assessment Scale grade (37.96% vs 9.50%, respectively). Headache‐related disability was highest among females with CM compared with males. CM represented 7.68% of migraine cases overall, and the proportion generally increased with age.
Conclusions.— In the US population, the prevalence of CM was nearly 1%. In adjusted models, CM prevalence was highest among females, in mid‐life, and in households with the lowest annual income. Severe headache‐related disability was more common among persons with CM and most common among females with CM.
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