Spread Parameter Research Articles

Simple model of epidemic dynamics with memory effects

We introduce a compartment model with memory for the dynamics of epidemic spreading in a constant population of individuals. Each individual is in one of the states S=susceptible, I=infected, or R=recovered (SIR model). In state R an individual is assumed to stay immune within a finite-time interval. In the first part, we introduce a random lifetime or duration of immunity which is drawn from a certain probability density function. Once the time of immunity is elapsed an individual makes an instantaneous transition to the susceptible state. By introducing a random duration of immunity a memory effect is introduced into the process which crucially determines the epidemic dynamics. In the second part, we investigate the influence of the memory effect on the space-time dynamics of the epidemic spreading by implementing this approach into computer simulations and employ a multiple random walker's model. If a susceptible walker meets an infectious one on the same site, then the susceptible one gets infected with a certain probability. The computer experiments allow us to identify relevant parameters for spread or extinction of an epidemic. In both parts, the finite duration of immunity causes persistent oscillations in the number of infected individuals with ongoing epidemic activity preventing the system from relaxation to a steady state solution. Such oscillatory behavior is supported by real-life observations and not captured by the classical standard SIR model.

Date of introduction and epidemiologic patterns of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Mogadishu, Somalia: estimates from transmission modelling of satellite-based excess mortality data in 2020.

Background: In countries with weak surveillance systems, confirmed coronavirus disease 2019 (COVID-19) deaths are likely to underestimate the pandemic's death toll. Many countries also have incomplete vital registration systems, hampering excess mortality estimation. Here, we fitted a dynamic transmission model to satellite imagery data of cemeteries in Mogadishu, Somalia during 2020 to estimate the date of introduction and other epidemiologic parameters of the early spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in this low-income, crisis-affected setting. Methods: We performed Markov chain Monte Carlo (MCMC) fitting with an age-structured compartmental COVID-19 model to provide median estimates and credible intervals for the date of introduction, the basic reproduction number ( R 0 ) and the effect of non-pharmaceutical interventions (NPIs) up to August 2020. Results: Under the assumption that excess deaths in Mogadishu March-August 2020 were attributable to SARS-CoV-2 infections, we arrived at median estimates of November-December 2019 for the date of introduction and low R 0 estimates (1.4-1.7) reflecting the slow and early rise and long plateau of excess deaths. The date of introduction, the amount of external seeding, the infection fatality rate (IFR) and the effectiveness of NPIs are correlated parameters and not separately identifiable in a narrow range from deaths data. Nevertheless, to obtain introduction dates no earlier than November 2019 a higher population-wide IFR (≥0.7%) had to be assumed than obtained by applying age-specific IFRs from high-income countries to Somalia's age structure. Conclusions: Model fitting of excess mortality data across a range of plausible values of the IFR and the amount of external seeding suggests an early SARS-CoV-2 introduction event may have occurred in Somalia in November-December 2019. Transmissibility in the first epidemic wave was estimated to be lower than in European settings. Alternatively, there was another, unidentified source of sustained excess mortality in Mogadishu from March to August 2020.

Early chains of transmission of COVID-19 in France, January to March 2020.

IntroductionSARS-CoV-2, the virus that causes COVID-19, has spread rapidly worldwide. In January 2020, a surveillance system was implemented in France for early detection of cases and their contacts to help limit secondary transmissions.AimTo use contact-tracing data collected during the initial phase of the COVID-19 pandemic to better characterise SARS-CoV-2 transmission.MethodsWe analysed data collected during contact tracing and retrospective epidemiological investigations in France from 24 January to 30 March 2020. We assessed the secondary clinical attack rate and characterised the risk of a contact becoming a case. We described chains of transmission and estimated key parameters of spread.ResultsDuring the study period, 6,082 contacts of 735 confirmed cases were traced. The overall secondary clinical attack rate was 4.1% (95% confidence interval (CI): 3.6–4.6), increasing with age of index case and contact. Compared with co-workers/friends, family contacts were at higher risk of becoming cases (adjusted odds ratio (AOR): 2.1, 95% CI: 1.4–3.0) and nosocomial contacts were at lower risk (AOR: 0.3, 95% CI: 0.1–0.7). Of 328 infector/infectee pairs, 49% were family members. The distribution of secondary cases was highly over-dispersed: 80% of secondary cases were caused by 10% of cases. The mean serial interval was 5.1 days (interquartile range (IQR): 2–8 days) in contact tracing pairs, where late transmission events may be censored, and 6.8 (3–8) days in pairs investigated retrospectively.ConclusionThis study increases knowledge of SARS-CoV-2 transmission, including the importance of superspreading events during the onset of the pandemic.

Accuracy of predicting epidemic outbreaks.

During the outbreak of a virus, perhaps the greatest concern is the future evolution of the epidemic: How many people will be infected and which regions will be affected the most? The accurate prediction of an epidemic enables targeted disease countermeasures (e.g., allocating medical staff and quarantining). But when can we trust the prediction of an epidemic to be accurate? In this work we consider susceptible-infected-susceptible (SIS) and susceptible-infected-removed (SIR) epidemics on networks with time-invariant spreading parameters. (For time-varying spreading parameters, our results correspond to an optimistic scenario for the predictability of epidemics.) Our contribution is twofold. First, accurate long-term predictions of epidemics are possible only after the peak rate of new infections. Hence, before the peak, only short-term predictions are reliable. Second, we define an exponential growth metric, which quantifies the predictability of an epidemic. In particular, even without knowing the future evolution of the epidemic, the growth metric allows us to compare the predictability of an epidemic at different points in time. Our results are an important step towards understanding when and why epidemics can be predicted reliably.

P1 - Calcium Silicate Loaded-Porous Chitosan Scaffold for Pulp-Dentin Complex Regeneration

Innovative pulp therapy strategies based on the use of biomaterials capable of stimulating dental pulp cells to participate effectively of the complete pulp-dentin complex regeneration is a challenge in Dentistry. Therefore, the aim of this study was to develop and characterize several formulations of calcium silicate (SiCa)-loaded porous chitosan (CH) scaffolds, and evaluate their bioactive and chemotactic potential on human dental pulp cells (hDPCs). Different concentrations of SiCa suspensions (0.5, 1.0 and 2.0%, w/v) were either incorporated (1:5; v/v) or not to 2% CH solution, establishing the following groups: CH (control); CH+0.5SiCa; CH+1.0SiCa; CH+2.0SiCa. The resulting solutions were submitted to thermally induced phase separation followed by freeze drying to obtain porous scaffolds. The topography and chemical characterization (SEM/EDS) of the scaffolds, as well as their pH and calcium release kinetics were assessed. Next, hDPCs were seeded onto the scaffolds to assess their bioactivity potential according with the parameters of cell viability (Live/Dead assay; 1, 7 and 14-days), proliferation (AlamarBlue assay; 1, 7 and 14-days), adhesion and spreading (F-actin assay; 1, 7 and 14-days). The pulp cells were also evaluated for total protein synthesis (Lowry Protein assay; 14-days), alkaline phosphatase activity (ALP Assay Kit; 14-days), mineralized matrix deposition (Alizarin Red assay; 21-days), and gene expression of odontogenic differentiation markers (ALP, DSPP, and DMP-1; RT-qPCR assay, 21-days). The chemotactic stimulus of the scaffolds on pulp cells (Transwell assay; 24 h) was also assessed. Data were analyzed by ANOVA and Tukey post-hoc (?=5%). The incorporation of SiCa in the chitosan scaffold formulations increased the pore diameter in comparison to control (p<0.05). High amounts of SiCa changed the surface nano-topography of the chitosan scaffolds (Image 1) and increased the pH and Ca release (p<0.05). Greater cell viability, proliferation, adhesion and spreading, as well as expression of odontogenic markers and mineralized matrix deposition was observed in the CH+1.0SiCa and CH+2.0SiCa groups (p<0.05). However, increased synthesis of total protein and mineralized matrix, as well as expression of DMP-1 and DSPP occurred only in group CH+2.0SiCa (p<0.05). Based on the fact that porous chitosan scaffolds enriched with 2% of calcium silicate showed improved bioactive and chemotactic potential on hDPCs, it should be considered as a potential biomaterial for cell-free tissue engineering strategy to drive pulp-dentin complex regeneration.

Dynamic Vehicular Communication using Gaussian Interpolation of Cluster Head Selection (GI-CHS)

Decentralized and centralized vehicular communication is investigated in this work using Gaussian interpolation function with cluster head (CH) selection technique. The work uncovered that the best communication approach is to use both centralized and decentralized vehicular communication as combining them will achieve a much more uniform results as a function of communication radius values and vehicular speed. It is also found that vehicular speed contributes negatively to the efficiency of data communication if the relative speed of the vehicles to the communication radius is limited by their ratios. Mathematical expression is presented that relates probability of successful transmission to communication radius for both centralized and decentralized techniques with data proving the importance of the spread parameter within the Gaussian interpolation in a tabulated form, and explained to prove the adaptability of the function used. It is also shown in this work that weights affecting CH selection, thus using Gaussian interpolation is proved to be important as a weighting function in an a adaptive and dynamic vehicular ad-hoc networks (VANETS) covering both vehicle to vehicle (V2V), and vehicle to infrastructure (V2I) communication through cluster head selection.

Wind Direction Inversion Using Shore-Based UHF Radar

Shore-based Ultra-high-frequency (UHF) radars have similar characteristics as the High-frequency (HF) ocean radars, for example, the sea waves generating Bragg resonance are gravity waves. Compared to HF radars, UHF radars are more sensitive to the small changes of sea waves, have finer spatial resolution and measure sea areas closer to the coast, hence are expected to complement the nearshore sensing capacity of HF radars. Two wind direction inversion methods for coastal UHF radars are adopted. One is based on the Bragg peak power ratio and the other is based on the modified Doppler spectrum centroid. The latter is proposed to address the problem that it is often difficult to calculate the Bragg peak power ratio at high wind speeds. In both methods, several models describing the relationship between the Doppler spectrum characteristics and wind direction are used respectively in combination with the least square method to estimate the wind direction with the ambiguity removed. Using the buoy wind sensor data as the ground truth, the results of a three-week experiment demonstrate the feasibility of the two methods. It is also shown that the former method has slightly smaller errors and the latter method applies to a wider range of wind speeds. For both methods, the most recommended model is the one based on the cosine-type directional spreading function with variable spreading parameter, because it has small errors and does not require data fitting.

Mechanized spreading of ceramic powder layers for additive manufacturing characterized by transmission x-ray imaging: Influence of powder feedstock and spreading parameters on powder layer density

Spreading of uniform, dense powder layers is a critical requirement for powder bed additive manufacturing (AM) processes, and layer quality is influenced by powder feedstock selection and spreading parameters. In prior work, quantification of powder layer density has required disruption of the powder layer after spreading, or compromise in the precision and/or design of the spreading mechanism compared to actual AM equipment. This work demonstrates the use of a precision, mechanized powder spreading testbed coupled with transmission x-ray imaging for spatially-resolved, non-contact powder layer density measurements. Specifically, the influence of several variables on the spreading of aluminum oxide powders is studied: powder size and shape, spreading tool choice (i.e., blade versus roller), traverse speed and/or rotation rate, and powder dispensing methodology (i.e., piston-fed vs hopper). For coarse powders, the density of layers spread with a roller geometry outperforms a blade geometry, reaching the tapped density for the coarse spherical powder. In addition, spreading with roller counter-rotation increases layer density for fine powders but decreases layer density for coarse powders. Increasing traverse speed or the use of textured rollers results in reduced powder layer density for fine powders. Finally, for fine powders, deposition with a vibrating hopper dispenser instead of a piston-fed mechanism, coupled with spreading by a counter-rotating roller, gives spread layer density equal to the tapped density.

A Further Application of Coefficient of Anisotropy and Reflection Coefficient as Indices in Delineating Groundwater Potential in Basement Complex of Ado Ekiti, Southwestern Nigeria

One index might not be enough in geophysical investigation of underground water potentials in basement terrain, there is a need to look into other indices for an improved investigation, the combination of some indices will lead credence to the result hence this work is a further application of coefficient of anisotropy, reflection coefficient and fracture coefficient on spatial distribution of underground water in Ado Ekiti basement complex. This work examined the thickness of aquifer, its resistivity spread and other hydro geological parameters such as total longitudinal conductance, total transverse resistances, coefficient of Anisotropy, reflection coefficient and fracture contract to determine water potentials in the study area. Resistivity and thickness are linearly related for high productive Aquifer formation, thus these indices generated from thickness and resistivity have been examined in other to determine the porosity and permeability and hence transmissivity of the aquiferous zone in the study area. Aquifer resistivity ranges from 7.7Ωm to 545Ωm in weather zone and between 9.5Ωm to 1902.6Ωm in fracture zone these values are within productive range in basement complex environment. Shallow basement of less water potential have been found in part of North west and north east of the study area, total transverse resistance also discovered low porosity at Aiyedun quarters, Elemi housing Estate and Ita Eku these are hard rock zone, this area has also been identified through their coefficient of anisotropy as low or none productive zone the coefficient of anisotropy is higher than 1.5. Area of promising water bearing zone are with the North west, west, south west and south east of the study area.

Adjusting cyber insurance premiums based on frequency in a communication network

This study compares cyber insurance premiums with and without a communication network effect frequency. As a cybersecurity factor, the frequency in a communication network influences the speed of cyberattack transmission. It means that a network or a high activity node is more vulnerable than a network with low activity. Traditionally, cyber insurance pricing considers historical data to set premiums or rates. Conversely, the network security level can evaluate using the Monte Carlo simulation based on the epidemic model. This simulation requires spreading parameters, such as infection rate, recovery rate, and self-infection rate. Our idea is to modify the infection rate as a function of the frequency in a communication network. The node-based model uses probability distributions for the communication mechanism to generate the data. It adopts the co-purchase network formation in market basket analysis for building weighted edges and nodes. Simulations are used to compare the initial and modified infection rates. This paper considered prism and Petersen graph topology as case studies. The relative difference is a metric to compare the significance of premium adjustment. The results show that the premium for a node with a low level in a communication network can reach 28.28% lower than the initial premium. The premium can reach 20.99% lower than the initial network premium for a network. Based on these results, insurance companies can adjust cyber insurance premiums based on computer usage to offer a more appropriate price.

Date of introduction and epidemiologic patterns of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Mogadishu, Somalia: estimates from transmission modelling of satellite-based excess mortality data in 2020

Background: In countries with weak surveillance systems, confirmed coronavirus disease 2019 (COVID-19) deaths are likely to underestimate the pandemic’s death toll. Many countries also have incomplete vital registration systems, hampering excess mortality estimation. Here, we fitted a dynamic transmission model to satellite imagery data of cemeteries in Mogadishu, Somalia during 2020 to estimate the date of introduction and other epidemiologic parameters of the early spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in this low-income, crisis-affected setting. Methods: We performed Markov chain Monte Carlo (MCMC) fitting with an age-structured compartmental COVID-19 model to provide median estimates and credible intervals for the date of introduction, the basic reproduction number ( R 0 ) and the effect of non-pharmaceutical interventions (NPIs) up to August 2020. Results: Under the assumption that excess deaths in Mogadishu March-August 2020 were attributable to SARS-CoV-2 infections, we arrived at median estimates of November-December 2019 for the date of introduction and low R 0 estimates (1.4-1.7) reflecting the slow and early rise and long plateau of excess deaths. The date of introduction, the amount of external seeding, the infection fatality rate (IFR) and the effectiveness of NPIs are correlated parameters and not separately identifiable in a narrow range from deaths data. Nevertheless, to obtain introduction dates no earlier than November 2019 a higher population-wide IFR (≥0.7%) had to be assumed than obtained by applying age-specific IFRs from high-income countries to Somalia’s age structure. Conclusions: Model fitting of excess mortality data across a range of plausible values of the IFR and the amount of external seeding suggests an early SARS-CoV-2 introduction event may have occurred in Somalia in November-December 2019. Transmissibility in the first epidemic wave was estimated to be lower than in European settings. Alternatively, there was another, unidentified source of sustained excess mortality in Mogadishu from March to August 2020.

Date of introduction and epidemiologic patterns of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Mogadishu, Somalia: estimates from transmission modelling of satellite-based excess mortality data in 2020.

Background: In countries with weak surveillance systems, confirmed coronavirus disease 2019 (COVID-19) deaths are likely to underestimate the pandemic's death toll. Many countries also have incomplete vital registration systems, hampering excess mortality estimation. Here, we fitted a dynamic transmission model to satellite imagery data of cemeteries in Mogadishu, Somalia during 2020 to estimate the date of introduction and other epidemiologic parameters of the early spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in this low-income, crisis-affected setting. Methods: We performed Markov chain Monte Carlo (MCMC) fitting with an age-structured compartmental COVID-19 model to provide median estimates and credible intervals for the date of introduction, the basic reproduction number ( R 0 ) and the effect of non-pharmaceutical interventions (NPIs) up to August 2020. Results: Under the assumption that excess deaths in Mogadishu March-August 2020 were attributable to SARS-CoV-2 infections, we arrived at median estimates of November-December 2019 for the date of introduction and low R 0 estimates (1.4-1.7) reflecting the slow and early rise and long plateau of excess deaths. The date of introduction, the amount of external seeding, the infection fatality rate (IFR) and the effectiveness of NPIs are correlated parameters and not separately identifiable in a narrow range from deaths data. Nevertheless, to obtain introduction dates no earlier than November 2019 a higher population-wide IFR (≥0.7%) had to be assumed than obtained by applying age-specific IFRs from high-income countries to Somalia's age structure. Conclusions: Model fitting of excess mortality data across a range of plausible values of the IFR and the amount of external seeding suggests an early SARS-CoV-2 introduction event may have occurred in Somalia in November-December 2019. Transmissibility in the first epidemic wave was estimated to be lower than in European settings. Alternatively, there was another, unidentified source of sustained excess mortality in Mogadishu from March to August 2020.

RESEARCH ON THE EFFECTS OF ROLLER-SPREADING PARAMETERS FOR NYLON POWDER SPREADABILITY IN ADDITIVE MANUFACTURING

The powder spreading process is one of the key processes in the powder-bed-based additive manufacturing (AM) technology. The roller-spreading parameters include the powder spreading layer thickness H, roller’s diameter D, roller’s rotational speed ω and translational velocity V, which have a major impact on the powder spreadability in AM processes. In this paper, the nylon powder was taken as the research object, and the discrete element method (DEM) was deployed to simulate the nylon powder spreading process by a roller. The three powder spreadability indicators including the deposition fraction, percent coverage and deposition rate were established. The central composite design (CCD) model was used to generate 30 groups of simulation cases. The regression models of three powder spreadability indicators were fitted by the response surface method (RSM). The analysis of variance was used to prove the accuracy and predicting effectiveness of regression models. In addition, the effect of process parameters on powder spreadability indicators was analyzed in detail. The results showed that the powder spreading layer thickness H was a leading influencing factor. The roller’s translational velocity V was a less important influencing factor. The roller’s diameter D and rotational speed ω had a slight influence on powder spreadability indicators. Both the H and D with V were determined as the main interactive factors on powder spreadability indicators. The three powder spreadability indicators were taken as the optimization goal, and the multi-objective optimization of roller-spreading parameters was carried out by the expectation method. The predicted optimal combination of powder spreading parameters and powder spreadability indicators were obtained. Moreover, the optimal results were verified through the experiments. The results showed that the predicted results of powder spreadability indicators were in good agreement with experimental results. The research results in this paper can provide guidance for the optimization of roller-spreading parameters in AM.

Design of Sliding Correlator Channel Sounder for Ionospheric Channel Probing Based on Software Define Radio

Observation of wireless channel using channel sounder method is needed in order to obtain the channel characteristics in the form of delay spread (Tm) and doppler spread (fD) parameters for optimizing the design of digital communication systems. This also applies to the ionosphere channels which are known to have dynamic properties in the time and place domains. The Sliding Correlator Channel Sounder technique known as a popular technique due to the advantage in the low level of complexity and relatively low cost. Similar to the advantages of the sliding correlator technique, Software Define Radio (SDR) device is one of the popular inexpensive platforms for implementing the communication system into the real world. The implementation of the sliding correlator channel sounder technique in the SDR platform will increase the advantages of the ionosphere channel measurement system that needed as an effort to obtain the dynamic of ionospheric channel characteristic information. However, the process of implementing a sliding correlator channel sounder system into the SDR device requires precise calculations to guarantee the designed system able to works according to the specifications of the SDR device and also meet the system performance targets. This paper discusses the design of a sliding correlator channel sounder system for observing the characteristics of ionosphere channels using SDR devices. The system design starts with the process of calculating the clock frequency generator bit (fchirp) based on the system performance target which refers to the recommendation of the ionospheric channel characteristics based on ITU-R F.1487 documents. Parallel with the calculation of the fchirp, the calculation of the Pseudo Noise (PN) bit length (L) based on the Dynamic Range (DR) value that meets with the specifications of the SDR hardware also carried out. Based on the obtained fchirp and L values, the calculation of other sliding correlator channel sounder parameters is performed with a limitation of a hardware specification and the target system performance. MATLAB simulation results show that the expected system performance can be met. This systems can also be realized significantly through the results of real signal transmission experiments conducted in a closed room laboratory

Air pollution impact on the Covid-19 mortality in Iran considering the comorbidity (obesity, diabetes, and hypertension) correlations

Since the rise of the Covid-19 pandemic, several researchers stated the possibility of a positive relationship between Covid-19 spread and climatic parameters. An ecological study in 12 Iranian cities using the report of daily deaths from Covid-19 (March to August 2020) and validated data on air pollutants, considering average concentrations in each city in the last year used to analyze the association between chronic exposure to air pollutants and the death rate from Covid-19 in Iran. Poisson regression models were used, with generalized additive models and adjustment variables. A significant increase of 2.7% (IC(95%) 2.6–4.4) was found in the mortality rate due to Covid-19 due to an increase of 1 μg/m3 of NO2. The results suggest an association between Covid-19 mortality and NO2 exposure. As a risk approximation associated with air pollution, more precise analysis is done. The results also show a good consistency with studies from other regions; this paper's results can be useful for the public health policymakers and decision-making to control the Covid-19 spread.

An Experimental Study on the Particle Size and Shape Distribution of Coal Drill Cuttings by Dynamic Image Analysis

Particle size and shape distribution can be measured in great detail by dynamic image analysis (DIA). The narrow dispersion of repeated experiment results indicates that the particle size distribution can be obtained with high reliability. Particle size distribution can be better fitted to Rosin-Rammler equation than Gaudin-Schuhmann distribution and the lognormal distribution. The spread parameter ( m ) and the location parameters ( d 0 ) of the Rosin-Rammler equation can be calculated precisely. We analyzed the similarities and differences between the different particle shape distributions. The distributions of form factor and circularity are right-skewed distributions, while the distributions of ellipse ratio, irregularity, and aspect ratio obey a normal distribution. By studying the relation between particle size and shape, we find a linear relationship between the ellipse ratio and the Legendre ellipse diameter on the logarithmic scale.

Optimal Control Strategies and Sensitivity Analysis of an HIV/AIDS-Resistant Model with Behavior Change.

Despite several research on HIV/AIDS, it is still incumbent to investigate more effective control measures to mitigate its infection level. Therefore, we introduce an HIV/AIDS-resistant model with behavior change and study its basic properties. In order to determine the most sensitive parameters that are responsible for disease transmission with respect to the basic reproduction number and those responsible for disease prevalence with respect to the endemic equilibrium, the sensitivity analysis was established and it was confirmed that the influx rate of people into the infected population and total abstinence from all risk practices and endemic areas are some of the most sensitive parameters for disease spread and disease eradication, respectively. Furthermore, by considering controls u_1 denoting the government’s intervention in promoting and encouraging behavior change, u_2 representing intake of balanced nutritional supplementation, and u_3 connoting antiretroviral therapy (ART), an optimal control problem was developed and analyzed. Before the establishment of the necessary conditions of the optimal control using Pontryagin’s Maximum Principle, we proved the existence of the optimal control triplet (u_1(t),u_2(t),u_3(t)in Phi , where Phi is the control set at time t,) which has been neglected by many researchers in recent years. Using the Runge–Kutta scheme, the optimal control problem was solved to understand the best combination of control strategies. Using the demographic and epidemiological data for South Africa on HIV/AIDS, a numerical simulation was carried out and results are presented on 3D surface plots. The obtained results suggested that the combination of all the considered control measures is the best method to ensure disease eradication.

Modeling and simulation of secure connectivity and hop count of multi-hop ad-hoc wireless networks with colluding and non-colluding eavesdroppers

Connectivity and hop count are two crucial attributes of dynamic wireless networks such as ad-hoc wireless networks (AHWNs). When the security issue is considered, establishing a secure multi-hop path in AHWNs is challenging because an intermediate node having a wireless link with previous nodes may not be secure to receive data from those nodes, and vice versa. In this paper, stochastic geometry graphs is used to model the AHWNs with eavesdroppers’ presence, then a simulation-based analysis method is proposed to investigate the characteristics of connectivity, hop count distribution, average hop count, and their relations in various scenarios. The proposed simulation-based analysis method consists of two algorithms. The first one finds all possible secure communication paths between two arbitrary legitimate users selected as source and destination and returns the shortest path’s hop count. This algorithm utilizes the random locations of legitimate users and eavesdroppers, the wireless channel condition, and the eavesdropper’s operating mode as input data. The second one provides the hop count distribution and the connection probability of the obtained multi-hop path of the first algorithm by averaging a huge number of network topology trials. So far, this paper is the first work that investigates both connectivity and hop count of security-aware multi-hop AHWNs. Moreover, these characteristics of multi-hop AHWNs with colluding eavesdroppers are compared with those of multi-hop AHWNs with non-colluding eavesdroppers and without eavesdroppers. Simulation results show that putting more legitimate users does not greatly improve the quality of the multi-hop path in the non-colluding case compared with that in colluding case. Furthermore, the shape parameter of Nakagami fading has a more substantial effect on the connectivity and hop count than the spread parameter. The results in this paper provide valuable insights in designing and evaluating security-aware multi-hop AHWNs.

Impact of interactions between layers on source localization in multilayer networks

Nowadays it is not uncommon to have to deal with dissemination on multilayer networks and often finding the source of said propagation can be a crucial task. We examine the issue of locating the source of Susceptible–Infected spreading process in a multilayer network using the Bayesian inference and the maximum likelihood method established for general networks and adapted here to cover multilayer topology. We show how its accuracy depends on network and spreading parameters and find the existence of two parameter ranges with different behavior. If inter-network spreading rate is low, observations in different layers interfere, lowering accuracy below that of relying on single layer observers only. If it is high, on the other hand, observations synergize, raising accuracy above the level of single layer network of the same size and observer density. We also show a heuristic method to determine the case in a system and potentially improve accuracy by rejecting interfering observations. This paper is dedicated to the memory of Professor Dietrich Stauffer, who was a pioneer in new approaches in statistical physics and its interdisciplinary applications.

Trimetallic magnetite-Ti-Au nanoparticle formation: A theoretical approach

Geometric, electronic and magnetic structure of planar slabs consisting of magnetite Fe3O4, titanium and gold layers are investigated by DFT-GGA calculations. It is assumed that these slabs can be used to simulate the upper layers of magnetite nanoparticles covered with an intermediate layer of titanium and a gold layer on the surface. Specific energies and spreading parameters (wettability) of the magnetite-gold, magnetite-titanium and titanium-gold interfaces are calculated. The specific energy and spreading parameter of the magnetite-gold interface is found to be negative, while these values of the magnetite-titanium (for thin Ti layer) and magnetite-titan-gold interfaces are significantly positive. This allows us to hope that the intermediate thin layer of titanium at the boundary between the surface of the magnetite nanoparticle and the gold layer stabilizes this three-layer structure and allows obtaining magnetite nanoparticles covered with continuous gold coating.