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The identification and localization of malicious nodes in wireless sensor networks (WSNs) is a hot area of research that can considerably extend the network's lifetime and make it more valuable. We use sensors whose positions are known, or anchor nodes, to make educated guesses about the positions of the unknown nodes. Several localization methods have been developed for precise estimation of the unknowable nodes. So, during the network setup process, finding suitable network parameters for node localization with the requisite accuracy in a short amount of time remains a tough task. Due to the fact that they manipulate network resources and routing protocols, routing assaults like wormhole attacks, Sybil attacks, blackhole attacks, and replay attacks are just a few examples of the types of attacks that have the potential to hinder the accuracy of localization and the quality of service provided by WSNs. This work proposes safe localization and detection of routing threats in wireless sensor networks by utilizing hybrid optimized machine learning approaches for optimal distance, position, and data communication. These approaches aim to find the optimal distance between sensors and the optimal position of sensors. Calculating the average localization accuracy and finding malicious nodes both need the use of the benchmark datasets CICIDS2017 and UNSW NB15. The machine learning algorithms that have been provided can be utilized with these datasets. The cluster labelling K-means clustering technique is applied to binary classification in the system that has been proposed. As a consequence, the system achieves an average detection accuracy of 100%. The findings of the simulation indicate that the proposed hybrid strategy is capable of achieving a higher level of localization accuracy of the unknown nodes, with an average localization error of 0.191. [ABSTRACT FROM AUTHOR]

• Graphs were utilized to describe interactions within plankton communities • GC-LSTM models were applied to address the importance of biotic interactions • GC-LSTM models consistently outperformed the LSTM and GCN models • There were insignificant differences in predictions with static and dynamic network • GNNExplainer yielded consistent importance patterns across different lead times Given the frequent association between freshwater plankton and water quality degradation, several predictive models have been devised to understand and estimate their dynamics. However, the significance of biotic and abiotic interactions has been overlooked. In this study, we aimed to address the importance of the interaction term in predicting plankton community dynamics by applying graph convolution embedded long short-term memory networks (GC-LSTM) models, which can incorporate interaction terms as graph signals. Temporal graph series comprising plankton genera or environmental drivers as node features and their relationships for edge features from two distinct water bodies, a reservoir and a river, were utilized to develop these models. To assess the predictability, the performances of the GC-LSTM models on community dynamics were compared those of LSTM and GCN models at various lead times. Moreover, GNNExplainer was used to examine the global and local importance of the nodes and edges for all predictions and specific predictions, respectively. The GC-LSTM models outperformed the LSTM models, consistently showing higher prediction accuracy. Although all the models exhibited performance degradation at longer lead times, the GC-LSTM models consistently demonstrated better performance regarding each graph signal and plankton genus. GNNExplainer yielded interpretable explanations for important genera and interaction pairs among communities, revealing consistent importance patterns across different lead times at both global and local scales. These findings underscore the potential of the proposed modeling approach for forecasting community dynamics and emphasize the critical role of graph signals with interaction terms in plankton communities. [Display omitted] [ABSTRACT FROM AUTHOR]

Ecosystem service (ES) flow can address mismatches between the supply and demand of ESs. Nevertheless, how to improve ES flow considering spatial flow information and interactions among different flow systems is a research gap. Taking the Beijing-Tianjin-Hebei (BTH) urban agglomeration region of China as an example, this study combined both system and network perspectives to analyze the ES flow of grain provision before and after optimization. Metacoupling system analysis was adopted to assess intra-regional and extra-regional flow. Linear programming was used to calculate the optimal distance cost flow solution with constraints. A network model was finally applied to build and analyze flow and transmission networks. In 2020, BTH participated in nearly 10% of the national flow, among which 57% was extra-regional flow. After optimization, the proportion of extra-regional flow decreased to 36%, all of which was inflow from the nearest provinces, while intra-regional flow increased by 35%. The optimized flow reduced distance costs by 143% and decreased network complexity. Core transmission nodes with both high degree and betweenness centrality played prominent connection roles in the process of flow. Strengthening regional connections and simultaneously effectively managing core transmission nodes are of great significance for improving flow efficiency and ensuring food provision. • Metacoupling system was adopted to assess intra-regional and extra-regional flow. • Flow networks before and after optimization were built to compare their attributes. • The optimized flow significantly reduced distance costs and the network complexity. • High outflow and key traffic nodes played strategic roles in transmission network. • This optimization is vital for material flow using artificial carriers and paths. [ABSTRACT FROM AUTHOR]

In this paper, a second-order adaptive model of a highly sensitive person's behaviors and biological foundation is presented. The model illustrates how genetic variance affects the serotonin level, leading to a certain level of neuronal hyperexcitability, which in turn influences behaviors, shaping different levels of sensory processing sensitivity. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive network model is presented of neurodegeneration processes over time. Mechanisms involved in these processes were used in the model which was used for computational analysis. This analysis provided insights into the interaction between the role of risk and protective factors with respect to lipid and alpha-synuclein homeostasis dysfunction. Different scenarios of disease progression have been addressed which can potentially be used to regard particular cases of PD. [ABSTRACT FROM AUTHOR]

There is an appreciable mental health treatment gap in the United States. Efforts to bridge this gap and improve resource accessibility have led to the provision of online, clinically-validated tools for mental health self-assessment. In theory, these screens serve as an invaluable component of information-seeking, representing the preparative and action-oriented stages of this process while altering or reinforcing the search content and language of individuals as they engage with information online. Accordingly, this work investigated the association of screen completion with mental health-related search behaviors. Three-year internet search histories from N = 7572 Microsoft Bing users were paired with their respective depression, anxiety, bipolar disorder, or psychosis online screen completion and sociodemographic data available through Mental Health America. Data was transformed into network representations to model queries as discrete steps with probabilities and times-to-transition from one search type to another. Search data subsequent to screen completion was also modeled using Markov chains to simulate likelihood trajectories of different search types through time. Differences in querying dynamics relative to screen completion were observed, with searches involving treatment, diagnosis, suicidal ideation, and suicidal intent commonly emerging as the highest probability behavioral information seeking endpoints. Moreover, results pointed to the association of low risk states of psychopathology with transitions to extreme clinical outcomes (i.e., active suicidal intent). Future research is required to draw definitive conclusions regarding causal relationships between screens and search behavior. • Online mental health screen completion is associated with search behavior changes. • Help-seeking after screen completion is prominent in those with mild symptomatology. • Suicidal intent is a highly probable search topic 2–4 weeks post screen completion. • Online screen completion and search behavior profiles may aid symptom forecasting. [ABSTRACT FROM AUTHOR]

In order to avoid excessive treatment of thyroid nodules in the clinic, it is necessary to find a simple and practical analysis method to comprehensively and accurately reflect benign or malignant thyroid nodules. This study aimed to construct and validate a comprehensive and reliable network-based predictive model using a variety of imaging and laboratory criteria for thyroid nodules to stratify the risk of malignancy prior to surgery. We retrospectively analyzed data from patients who underwent surgical treatment for thyroid nodules at the Thyroid and Breast Diagnosis and Treatment Center of Weifang Hospital of Traditional Chinese Medicine between January 2018 and December 2020. Binary logical regression analysis was performed to predict whether nodules were malignant or benign. The developmental dataset included 457 patients (January 2018–December 2020). The validation set included separate data points (n = 225, January 2018–December 2020). In this study, criteria that showed significant predictive value for malignant nodules included TI-RADS: 4b (p = 0.065); Bethesda IV, Bethesda V, Bethesda VI (P < 0.0001); BRAFV600E mutation (P < 0.0001); Calcitonin>5 pg/ml (p = 0.0037); and FNA-Tg>30 ng/ml (p = 0.0003). A 10-grade risk scoring system was developed. The risk of malignancy risk ranged from 2.06% to 100% and was positively associated with increasing risk grade. The areas under the receiver-operating characteristic curve of the development and validation sets were 0.972 and 0.946, respectively. A simple, comprehensive and reliable web-based predictive model was designed using a variety of imaging and laboratory criteria to stratify thyroid nodules by probability of malignancy. [ABSTRACT FROM AUTHOR]

The nanostructured thin film (NSTF) catalyst layers which have demonstrated high power densities, mass activities, and exceptional metal and support stability can have limited operational robustness due to their thin thickness and the hydrophilicity of the metal-coated nano whiskers. The dispersed nanostructured thin film (dNSTF) catalyst layers have been developed by dispersing the NSTF Pt whiskers with ionomer and carbon support to increase the thickness and hydrophobicity. Continuum and network models (NM) are coupled through boundary conditions to study the polymer electrolyte fuel cell with a dNSTF cathode catalyst layer. The coupled model combines the computational efficiency of the continuum model with the pore-scale information in the dNSTF cathode catalyst layer of the NM. It captures the special morphology of the partially ionomer/water covered cylindrical whiskers, as well as water percolation through the pore structures and their impact on the cell performance. We observe optimal ionomer coverage on whiskers to be 0.5, ionomer to carbon ratio to be 0.9 and higher whisker to carbon ratios to be desired. • High efficiency, high fidelity coupled continuum and network models developed. • Pore-scale description of the dispersed nanostructured thin film (dNSTF) catalyst layer. • Optimal parameters and operating conditions determined for the dNSTF catalyst layer. [ABSTRACT FROM AUTHOR]

In this paper, it is addressed by mathematical analysis how network-oriented modeling relates to the dynamical systems perspective on mental processes. It has been mathematically proven that any dynamical system can be modeled as a temporal-causal network model and that any adaptive dynamical system (of any order) can be modeled by a self-modeling network (of the same order). [ABSTRACT FROM AUTHOR]

This paper studies the optimal spatial pricing for a ride-sourcing platform subject to a congestion charge. The platform determines the ride prices over the transportation network to maximize its profit, while the regulatory agency imposes the congestion charge to reduce traffic congestion in the urban core. A network economic equilibrium model is proposed to capture the intimate interactions among passenger demand, driver supply, passenger and driver waiting times, platform pricing, vehicle repositioning and flow balance over the transportation network. The overall optimal pricing problem is cast as a non-convex program. An algorithm is proposed to approximately compute its optimal solution, and a tight upper bound is established to evaluate its performance loss with respect to the globally optimal solution. Using the proposed model, we compare the impacts of three forms of congestion charge: (a) a one-directional cordon charge on ride-sourcing vehicles that enter the congestion area; (b) a bi-directional cordon charge on ride-sourcing vehicles that enter or exit the congestion area; (c) a trip-based congestion charge on all ride-sourcing trips. We show that the one-directional congestion charge not only reduces the ride-sourcing traffic in the congestion area, but also reduces the travel cost outside the congestion zone and benefits passengers in these underserved areas. We establish that in all congestion charge schemes the largest share of the tax burden is carried by the platforms, as opposed to passengers and drivers. We further show that compared to other congestion charges, the one-directional cordon charge is more effective in congestion mitigation: to achieve the same congestion-mitigation target, it imposes a smaller cost on passengers, drivers, and the platform. On the other hand, compared with the other charges, the trip-based congestion charge is more effective in revenue-raising: to raise the same tax revenue, it leads to a smaller loss to passengers, drivers, and the platform. We validate these results through realistic numerical studies for San Francisco. • The optimal spatial pricing strategies are derived for the ride-sourcing platforms. • Empty vehicle relocation and interceptions during relocation are considered. • One-directional cordon price limits congestion in the urban core and reduces travel costs in the suburb. • One-directional cordon price is more effective in congestion mitigation. • Trip-based congestion charge is more effective in revenue-raising. [ABSTRACT FROM AUTHOR]

We investigate the thermoacoustic response of a simple laboratory combustor where the flame response is varied systematically by combining hydrogen enrichment with coherent vortex shedding to introduce modulations in the gain and phase of the flame transfer function (FTF). Previous work showed that the modulations in the FTF were produced by superposition of two convective disturbances with different time-delays: a convective time-delay from the flame base to the center of heat release rate and a convective time-delay due to controlled vortex shedding upstream of the flame. Although the latter can be independently controlled by geometry, the former depends on the combustion properties of the flame which determines the center of heat release rate. In this paper we take advantage of the combustion properties of different H 2 / CH 4 / air blends that produce flames with a different flame speed and center of heat release rate but have the same equivalence ratio, adiabatic flame temperature, thermal power, as well as bulk velocity. Since the flame temperature is proportional to the acoustic amplification of the flame, fixing this enables fully independent control of both time-delays that modify the gain and phase of the flame response enabling a systematic investigation into the thermoacoustic response of the system. To interpret the effect these modifications have on the thermoacoustic system response, a Distributed Time Lag (DTL) model is used to generalize and describe the FTF in terms of the two convective time delays, which are linked to physical parameters. The DTL model is then implemented into an elementary 1D network model of the system, and results from a linear stability analysis are compared with measurements of self-excited instabilities. Changes to the growth rate are shown to be consistent with the experimentally observed limit cycle when the time delay related to the modulations is varied. The relative importance of gain or phase modulations on the change in growth rate is then discussed. We find that a modification to the gain extends the initial eigenvalue trajectory, whereas a change in the phase leads to a rotation in the complex plane. Furthermore, the overall effect on the growth rate depends on the initial location of the eigenvalue in the complex plane, which is determined by the operating conditions. The influence of strong mode veering due to exceptional points on the eigenvalue trajectories is shown to play an important role when modulations are introduced. [ABSTRACT FROM AUTHOR]

[Display omitted] • A network build-up model for the crosslinking of thiol-X systems is presented. • Intra-molecular cyclization is the major source of non-ideal step-wise behavior. • A single parameter can model a range of materials with similar crosslinkers. • Better description of dual-curing systems and vitrimer relaxation is achieved. Thiol-click reactions are widely used in the preparation of thermosets. The network build-up process follows step-wise polymerization behavior, which is commonly analyzed assuming ideal step-wise behavior using well-known expressions such as the Flory-Stockmayer equation for the prediction of the gel point conversion. However, a number of factors contributing to departures from the expected ideal behavior can be identified: a) impurity of the reagents, b) complexity of the reaction mechanism and c) internal cyclization processes. In order to maximize the accuracy of predictions about the expected crosslinking behavior and therefore fine-tune the design of thermosets designed as dual-curing systems or covalent adaptable networks (CANs), it is necessary to take into consideration all these sources of non-ideality. In this work, we aim to describe the effects of such factors on the network build-up process of thiol-click thermosets, making use of the recursive methodology of Miller and Macosko. The contribution of each factor is analyzed separately and their overall combined effect is discussed in relation with real data reported in the literature and experimental results. [ABSTRACT FROM AUTHOR]

Mathematical models of sexually transmitted disease (STI) are increasingly relied on to inform policy, practice, and resource allocation. Because STI transmission requires sexual contact between two or more people, a model's ability to represent the dynamics of sexual partnerships can influence the validity of findings. This ability is to a large extent constrained by the model type, as different modeling frameworks vary in their capability to capture patterns of sexual contact at individual, partnership, and network levels. In this paper, we classify models into three groups: compartmental, individual-based, and statistical network models. For each framework, we describe the basic model structure and discuss key aspects of sexual partnership dynamics: how and with whom partnerships are formed, partnership duration and dissolution, and temporal overlap in partnerships (concurrency). We illustrate the potential implications of accurately accounting for partnership dynamics, but these effects depend on characteristics of both the population and pathogen; the combined impact of these partnership and epidemiologic dynamics can be difficult to predict. While each of the reviewed model frameworks may be appropriate to inform certain research or policy questions, modelers and consumers of models should carefully consider the implications of sexual partnership dynamics for the questions under study. [ABSTRACT FROM AUTHOR]

This paper contributes an adaptive temporal-causal model describing how the direction of evolutionary processes can be driven by the (free) behavioral choices made by the individuals. The model was designed based on the role of behavioral plasticity as described in the literature. Simulations indeed illustrate the overall process that takes place, and verification by mathematical analysis has been performed which shows that the model does what is expected. [ABSTRACT FROM AUTHOR]

This paper analyses computationally the effect of the gut microbiome on social interaction deficits related to ASD. An integrative second-order adaptive network model is introduced that integrates the relevant mental and physiological causal pathways and their (second-order) adaptivity. [ABSTRACT FROM AUTHOR]

This paper explores how becoming a conspiracy thinker can be modeled by an adaptive mental network model based on theories from the empirical literature. A number of simulation scenarios for different types of persons illustrate its usefulness. The model has been verified by mathematical analysis of stationary points and equilibrium values, and validated against data points representing patterns expected from the empirical literature. [ABSTRACT FROM AUTHOR]

Background: Foster children experience maltreatment at exceptionally high rates with increased risk to develop ICD-11 complex posttraumatic stress disorder (CPTSD). While rates of comorbidity between CPTSD and various disorders are high, the interplay between constituent aspects of psychopathology is not clearly understood. No study used network analysis to model the interplay between these aspects as potentially maintaining a stable condition of psychopathology, and research on the etiology and maintenance of CPTSD in children is especially scarce.Methods: Altogether, 208 Austrian foster children completed a set of standardized measures, resulting in a final sample of N = 122 foster children meeting the inclusion criteria. Experiences of childhood trauma, ICD-11 CPTSD, depression, dissociation, adaptive, and maladaptive emotion regulation were assessed. Following an exploratory approach, analyses were conducted using latent single indicator factor scores in two network models.Results: Domains of CPTSD, PTSD and disturbances in self-organization (DSO), evidenced as most central factors in children's complex psychopathology. Including cumulative childhood trauma did not influence the connectedness of factors in any relevant way. Shortest direct paths from cumulative childhood trauma to CPTSD included dissociation (PTSD) and adaptive emotion regulation (DSO) as mediating factors.Limitations: Results are based on a small sample of highly-traumatized foster children, potentially limiting current findings' generalizability.Conclusions: CPTSD identified as central in children's complex psychopathology, while the role of childhood trauma seems stronger for the onset than the maintenance of such psychopathology. The current network revealed central disorders and distinct mediating factors as important targets for treatment strategies and future research. [ABSTRACT FROM AUTHOR]

An extension of the classical pandemic SIRD model is considered for the regional spread of COVID-19 in France under lockdown strategies. This compartment model divides the infected and the recovered individuals into undetected and detected compartments respectively. By fitting the extended model to the real detected data during the lockdown, an optimization algorithm is used to derive the optimal parameters, the initial condition and the epidemics start date of regions in France. Considering all the age classes together, a network model of the pandemic transport between regions in France is presented on the basis of the regional extended model and is simulated to reveal the transport effect of COVID-19 pandemic after lockdown. Using the measured values of displacement of people between cities, the pandemic network of all cities in France is simulated by using the same model and method as the pandemic network of regions. Finally, a discussion on an integro-differential equation is given and a new model for the network pandemic model of each age class is provided. [ABSTRACT FROM AUTHOR]

This work addresses the simulation of heat flow and electric currents in thin wires. An important application is the use of bond wires in microelectronic chip packaging. The heat distribution is modeled by an electrothermal coupled problem, which poses numerical challenges due to the presence of different geometric scales. The necessity of very fine grids is relaxed by solving and embedding a 1D sub-problem along the wire into the surrounding 3D geometry. The arising singularities are described using de Rham currents. It is shown that the problem is related to fluid flow in porous 3D media with 1D fractures (C. D'Angelo, SIAM Journal of Numerical Analysis 50.1, pp. 194–215, 2012). A careful formulation of the 1D–3D coupling condition is essential to obtain a stable scheme that yields a physical solution. Elliptic model problems are used to investigate the numerical errors and the corresponding convergence rates. Additionally, the transient electrothermal simulation of a simplified microelectronic chip package as used in industrial applications is presented. [ABSTRACT FROM AUTHOR]

Highlights • Nonlinear consolidation models for any type of constitutive dependences and eliminating the most restrictive hypotheses. • Tabulated models are also designed and solved by the network method. • The network models are built from very simple rules, providing the almost exact solution of the problem. Abstract The nonlinear consolidation problem in saturated soils, for any type of constitutive dependences of the hydraulic permeability and the void ratio on the effective pressure, has been numerically simulated by the network method. Three different network models, based on logarithmic and/or potential constitutive dependences, called the Davis and Raymond, Juárez-Badillo and Cornetti and Battaglio models, as well as a fourth one with dependences in tabulated form, are solved. In addition, new network models that delete the two restrictive hypotheses assumed by these authors are presented. These hypotheses are the influence of the void ratio changes in the term of contraction of the governing equation and the influence of the thickness change of the volume element as consolidation progresses. Only a few rules based on elementary theory of circuits are required for the design of the models, whose solution is reliable with relatively small grids and computational times. After verifying the results of the network method with the solutions of the classic authors, the extended models have been used to address a real case of consolidation. [ABSTRACT FROM AUTHOR]

Product service system (PSS), an undeveloped business model, provides consumers with full-life cycle services from the perspective of products, functions and services. This paper proposes a posture recognition method based on smart product service system from the aspects of technology and user' need. A Posture-CNN model based on the convolutional neural network (CNN) is established in this paper. According to the theory of PSS, this paper analyses the difficulties that the posture recognition technology is facing in the application of products, and resolves the existing problems such as low accuracy of recognition and unsupported complex environment. A self-made posture data set is tested with the results that the method can greatly reduce network parameters and improve network speed compared with other existing classification methods. To some extent, as a core technology of the PSS control terminal, this technology can perfect users' experiences, better serve consumers and promote the development of the PSS. [ABSTRACT FROM AUTHOR]

Abstract The study of underground natural ventilation opportunities has become increasingly significant in recent years with its promise of wide application in underground structures such as underground hydro power stations, metro stations, underground parking and laboratories. It is recognized that deployment of natural ventilation constitutes a passive technology that can lead to significant energy conservation if applied judiciously. This paper focuses on underground buildings that are buried deeply and typically consist of an underground complex network of connected structures. The other characteristic is that these structures house machinery and devices that generate heat, leading to elevated internal air temperatures. Combined with the deep location, it implies that buoyancy forces are significant which make natural ventilation through vertical shaft openings a viable option. These characteristics demand a study of the heat transfer processes between ambient conditions, soil and underground buildings. In this paper, we present a dynamic flow network model with loops for multizone airflow and apply it to deep buried underground structures considering the dominant heat transfer characteristics, not only through the elements of the network but also the heat exchange with the envelope and adjacent soil mass. Finally, a small-scale experiment of occurring airflow is conducted and compared with the outcomes of the dynamic simulation of the proposed model. The comparison serves as validation and illustration of the application potential of the network model for natural ventilation investigation and consecutive optimization of its use in underground buildings. Highlights • A loop method based flow network model is developed. • Heat transfer is coupled with airflow simulations. • The matrix form of the temperature distribution in the elements is derived. • Comparison is performed between small-scale experiment and the proposed model. • Filed measurement is conducted to validate the model. [ABSTRACT FROM AUTHOR]

This research explores how a new concept, support for energy transition narratives, and other commonly applied social licence factors influence local acceptance of large-scale solar farms. Producing renewable electricity requires deploying more large-scale renewable technology infrastructure and support of host communities will be paramount. There is a dearth of studies that investigate a comprehensive set of social licence factors that influence local support of solar farms. Moreover, there is a lack of research considering how broader environmental worldview and beliefs about the energy transition influence local level support for renewable energy infrastructure. Survey data from a large, representative sample of the Australian population is analysed using a network modelling approach to identify conditional associations between variables of interest. Results reveal that general support for renewables in the energy transition had relatively strong direct and indirect associations with local acceptance in addition to social licence factors. Conversely, beliefs about the energy transition beyond the role for renewables (i.e., support for the role of natural gas, support for exporting fossil fuels and willingness to make trade-offs for a faster energy transition) had relatively weak effects on local acceptance and other social licence factors. The findings suggest that individual alignment with broader energy transition narratives explains local solar farm support beyond social licence factors. Future social licence research should consider the role of broader public narratives when investigating the factors that facilitate or impede local acceptance of renewable energy projects. [Display omitted] • Environment worldviews relate to beliefs about the role for renewables. • Broader beliefs about the role of renewables influence local solar farm acceptance. • Beliefs about the role of fossil fuels are weakly associated with local acceptance. • Network models identified conditional associations between social licence factors. • Survey with a large, representative Australian sample (n = 3103). [ABSTRACT FROM AUTHOR]

We present and analyze a methodology for numerical homogenization of spatial networks models, e.g. heat conduction and linear deformation in large networks of slender objects, such as paper fibers. The aim is to construct a coarse model of the problem that maintains high accuracy also on the micro-scale. By solving decoupled problems on local subgraphs we construct a low dimensional subspace of the solution space with good approximation properties. The coarse model of the network is expressed by a Galerkin formulation and can be used to perform simulations with different source and boundary data, at a low computational cost. We prove optimal convergence to the micro-scale solution of the proposed method under mild assumptions on the homogeneity, connectivity, and locality of the network on the coarse scale. The theoretical findings are numerically confirmed for both scalar-valued (heat conduction) and vector-valued (linear deformation) models. [ABSTRACT FROM AUTHOR]

A cryptocurrency is a digital asset maintained by a decentralised system using cryptography. The complex correlations between the cryptocurrencies' prices may be exploited to understand the market dynamics and build efficient investment portfolios. We use network methods to select cryptocurrencies and the Markowitz Portfolio Theory to create portfolios that are agnostic to future market behaviour. The performance of our network-based portfolios is optimal with 46 cryptocurrencies and superior to benchmarks for short-term investments, reaching up to 1 , 066 % average expected returns within 1 day. Cryptocurrency portfolio investment may be competitive but calls for caution given the high variability of prices. • Price inter-dependencies can be exploited to create efficient agnostic portfolios. • Network-based crypto portfolios can achieve high returns with acceptable risks. • Obtain better performance on the short-term investment horizon. • The best-performing portfolios contained 46 cryptocurrencies. [ABSTRACT FROM AUTHOR]

The aim of the present study was to revalidate the Rosenberg Self-Esteem Scale using Network Analysis among Algerian university students. A cross-sectional dataset was collected from a sample of 1115 participants, including 738 females (66.2 %) and 377 males (33.8 %). Network Analysis was utilized to investigate the structure of the scale, identify the most influential items in measuring self-esteem, and compare the two networks between females and males. Results revealed a two-dimensional structure of the scale, with negative-worded items forming the first dimension and positive-worded items forming the second dimension. Item 8 was removed due to instability. Centrality indices were employed to identify the items that played a crucial role in enhancing an individual's self-esteem. The results indicated that items 3 and 10 held greater importance in the network for the entire participant group. The Network Comparison Test indicates no difference in Network Invariance between females' and males' networks. However, there is a difference in the global strength of the two networks. The comparison of their centrality indices reveals that items 3 and 10 continue to hold higher importance in the network for females. In contrast, for males, item 4 takes precedence over items 10 and 3. The findings provide evidence for the reliability and validity of the Rosenberg Self-Esteem Scale with nine items in measuring self-esteem among Algerian university students. • Deleting item 8 of the Rosenberg Self-Esteem Scale (RSES) because there are problems with it that may be due to cultural factors • Centrality indices to quantify the importance of the items of the RSES • Items 3 and 10 exhibit higher centrality indices within the RSES network among females. • Items 4, 3, and 10 exhibit higher centrality indices within the RSES network among males. [ABSTRACT FROM AUTHOR]

Abstract The movement of sediment from source to sink in a watershed is a complex process with multiple interactions and feedbacks across scales. At small scales, the size characteristics of a sediment mixture affect the transport of that sediment through hiding and through the nonlinear effect of sand content on gravel transport. At large scales, the channel network itself, through network geometry and the spatial pattern of transport capacity, adds additional complexity in organizing sediment moving through the system such that aggradational hotspots can emerge. The purpose of this paper is to present a Lagrangian framework for exploring complexities of mixed-size sediment transport in gravel-bedded river networks. The present model builds off of previous network-based, bed-material sediment transport models; but key advancements presented herein (i) allow for a mixture of sediment sizes in the river network, (ii) incorporate a mixed-size sediment transport equation, and (iii) utilize a daily flow hydrograph to drive intermittent transport. The model is applied to the roughly 4700 km2 Methow River Basin in Washington State, USA, using simplified model inputs with the goal of illustrating the utility of the model for motivating future work. Highlights • Model for mixed-size sediment transport in gravel-bedded river networks. • Illuminates complexities arising from sediment mixtures and river networks. • Illustrates the utility of the model to motivate future work. [ABSTRACT FROM AUTHOR]

Abstract Prediction of reservoir production using different technical scenarios and designs is essential for optimization of reservoir development plan. Due to the enormous scale of a reservoir, the simulators use simplified discretized equations to predict the production and other properties of the reservoir. Therefore, they cannot observe complicated physical phenomena that occur at the pore scale. Such phenomena have significant effect on the relative permeability of the fluids which is an important factor for predicting multiphase flow behaviour at reservoir scale. To remedy this problem, one can simulate the fluid flow in the pore scale and predict relative permeability curves for various rock types. To scale up the relative permeability curves from the pore-scale, it is necessary to scale up the porosity, permeability, and capillary pressure curves. This study is the first step towards this goal. The objective of this research is to calculate the single phase and immiscible two-phase flow properties including porosity, permeability, and capillary pressure of drainage and imbibition processes using random-network modelling technique and then scaling up the results to macro scale. This is done by generating four virtual porous media with various properties by simulating sedimentation process and dividing them into 8 sub-segments. Flow properties of all the sub-segments are computed using a network modelling technique. Flow properties are then scaled up to reconstruct the original macro scale media. Finally, the results are compared to ones obtained directly from the original samples. Results show relative errors ranging from 2% to 10% for homogenous and heterogenous media respectively. As the level of heterogeneric increases, deviation of the scaled-up properties from original values rises. Highlights • Synthetic media were generated using 3D object-based reconstruction algorithm and each medium was divided into 8 subsegments. • Single phase and immiscible two-phase flow properties were calculated using Network Modelling Technique. • Scale-up methods were employed to calculate the single phaseand two-phase flow properties of the scaled-up media. • Scaled-up properties are in good agreement with the ones obtained from the original blocks using PNM technique. [ABSTRACT FROM AUTHOR]

This paper proposes a new model for the Turkish Day Ahead Electricity Market including network equations. The existing one does not implicitly consider the power flow limitations, and these are considered separately that might result with a suboptimal day-ahead schedule. The dc network equations are added to the model, which is used to determine the Day Ahead System Sell and Buy Decisions and System Marginal Price in day-ahead planning. The impact of integration of network equations is exhibited using a small 6-bus network, mid-sized Turkish northwest sub-system, and full Turkish electric power system. [ABSTRACT FROM AUTHOR]

Abstract Against the background of globalization, city branding has become a useful strategy utilized by decision-makers in cities to promote their cities in the race for limited resources. Urban transformation factors, i.e., economic development stage and geographic position play an important role in city branding choices. We argue that city branding choices of a city can be affected by other cities through city interactions and networking. The effects of city interactions can spread across cities either through spatial proximity-referring physical or geographic connections, or logical proximity-representing city interconnectivity caused by collaboration activities. To test our argument, we develop a two-layer network model using the generalized modeling framework (GEMF) to model city branding choices, based on empirical data of sustainable brand developmental pathways from the three economic regions in China. We examine the general evolution pattern of city branding practices and construct different scenarios to analyze the impact of city interactions. The results indicate that taking city interactions as an additional factor to urban transformation allows for a better prediction on city branding choices. Policy-makers and urban planners need to consider infrastructure and economic development policies in designing city branding strategies. Highlights • Offers a novel approach to model city branding development in China. • Explains city branding choices by factors in urban transformation and city interactions through a two-layer network model. • Examines this approach using intense empirical data from all cities in the three Chinese economic regions. • Models potential future scenarios with infrastructure improvement and policy changes. • City brand choices is more sensitive to infrastructure development within a region than that between the regions. [ABSTRACT FROM AUTHOR]

Abstract The thermal conductivities of borosilicate melts, which have scarcely been measured, are one of the important thermophysical properties for optimization of temperature distribution in a glass melting furnace. In this study, the thermal effusivities of B 2 O 3 -SiO 2 and CaO-B 2 O 3 -SiO 2 melts were first measured using a front heating–front detection laser flash method. Then, the thermal conductivities, which were obtained by combining the measured thermal effusivity data with specific heat capacity and density, could be evaluated through an equation using the least-squares method. As such, the thermal conductivities of the B 2 O 3 -SiO 2 and CaO-B 2 O 3 -SiO 2 melts were found to slightly linearly decrease with increasing temperature over the investigated temperature range of 1248–1723 K. In addition, the thermal conductivities of the samples of CaO-B 2 O 3 -SiO 2 melts were higher than those of the samples of B 2 O 3 -SiO 2 melts. Furthermore, the network models of the thermal conductivities for the B 2 O 3 -SiO 2 and CaO-B 2 O 3 -SiO 2 melts were discussed using the boron-11 NMR spectra of the glassy samples for the B 2 O 3 -SiO 2 and CaO-B 2 O 3 -SiO 2 samples. Highlights • Thermal effusivities were measured using the FH–FDLF method. • Thermal conductivities of B 2 O 3 -SiO 2 and CaO-B 2 O 3 -SiO 2 melts were evaluated. • Thermal conductivity equation could be evaluated using the least-squares method. • Network models of the thermal conductivities are discussed. [ABSTRACT FROM AUTHOR]

Abstract Urban lifeline systems play vital roles in sustaining fundamental functionalities in urban areas. These systems, working collaboratively and synergistically, form a complex system of systems, in which disruptions in one system can rapidly propagate to others, posing a great challenge for the identification and protection of critical infrastructure facilities. This study introduces a new criticality assessment approach of interdependent lifeline systems. Given a weighted directed network of infrastructure systems, the proposed approach calculates vertex criticality through the biased PageRank algorithm: compared with the original PageRank algorithm, the biased one utilizes a personalization vector (in this context, vertex functional importance) in the process of criticality measurement. This algorithm design fulfils an integration of both network topology and function, and can comprehensively measure vertex criticality. To consider the impact of cascading failure, this criticality assessment method also adopts a linear combination form to take into account the criticality of child vertices using conditional probabilities as parameters. A case study is conducted on five real lifeline systems in a middle-sized county in China with over 300,000 inhabitants. Based on the case study model, targeted vertex attacks are carried out to illustrate the validity and effectiveness of this criticality measurement. Examining both network topological and function response, resulting curves show that the criticality ranking calculated with the proposed approach is better to reflect component topological and functional importance compared to other commonly used metrics. The main contribution of this study to the body of knowledge is the proposition of a new approach for criticality assessment of facilities in interdependent infrastructure systems under disaster scenarios, which provides a useful and intuitive guide for decision making process with regards to pre-disaster infrastructure protection. [ABSTRACT FROM AUTHOR]

In this paper, we represent a subway network as a dynamic, directed and weighted graph, where vertices represent subway stations and edge weights represent passenger volume passing between two stations. Static and dynamic metrics which can represent vertices and edges' local and global attributes are proposed. Then dynamic properties of subway network in heterogeneity and vulnerability are further analyzed by standard deviation. Through a detailed analysis of Beijing subway network, we illustrate that the heterogeneity and vulnerability of Beijing subway network vary over time when passenger flow is taken into consideration. In addition, the vulnerability of the network is correlated with its heterogeneity based on local dynamic metric's distribution when passenger flow is taken into account, instead of the global dynamic metric's distribution, and the important station with higher flow degrees are identified. [ABSTRACT FROM AUTHOR]

A numerical simulator based on the discrete network model approach has been developed to simulate drainage processes in a water-saturated porous medium. To verify the predictive potential of the approach to simulate the unstable migration of a dense nonaqueous phase liquid (DNAPL) at the pore scale, the numerical model was applied to laboratory experiments conducted on a sand-filled column. The parameters relative to pore body size and pore throat size used in the construction of the equivalent network were derived from discrete grain-size distribution of the real porous medium. The observed water retention curve (WRC) was first simulated by desaturation of the network model. The good agreement of the modelled WRC with the experimental one highlights that the applied approach reproduces the main characteristics of the real pore space. The numerical model was then applied to rate controlled experiments performed on a homogenous sand-filled column to study the gravity-driven fingering phenomenon of immiscible two-phase flow of water and a DNAPL. The numerical results match within 10% based on the standard deviation with the experiments. They correctly reproduce the effect of several system parameters, such as flow mode (upward flow and downward flow) and the flow rate, on the stability of the water/DNAPL front in a saturated porous medium. [ABSTRACT FROM AUTHOR]

In microfiltration and ultrafiltration, particulate foulants are inevitably deposited on the membrane surface, forming a cake whose structure and behaviour play crucial roles in the subsequent filterability of the suspensions. This study investigated the impact of fouling by three types of latex particulate foulants, which were of the same size (3 µm) but with different surface charges. Surprisingly, although the positively charged aminated latex was expected to perform the worst in the flux-decline experiments due to attractive electrostatic interaction with the negatively charged membrane, this latex displayed the best performance relative to the two negatively charged latex. To understand these counter-intuitive results, a novel network model (Han et al., 2017) [1] and three-dimensional (3D) optical coherence tomography (OCT) image analysis (Li et al., 2016) [2] were employed to reveal the underlying reasons for the different fouling behaviors. Two mechanisms were found to contribute to the worse performance of the negatively charged latex. Firstly, these particles tended to deposit on the pore rather than non-pore region of the membrane due to the repulsive particle-membrane electrostatic interactions, which led to a more complete pore blockage and thereby greater initial cake resistance. Secondly, these particles had a greater tendency to cluster and deposit on other deposited latex due to similarly repulsive particle-membrane and particle-particle interactions, which led to a more inhomogeneous cake and thereby greater specific cake resistance. [ABSTRACT FROM AUTHOR]

Differential shrinkage in particulate quasi-brittle materials causes microcracking which reduces durability in these materials by increasing their mass transport properties. A hydro-mechanical three-dimensional periodic network approach was used to investigate the influence of particle and specimen size on the specimen permeability. The particulate quasi-brittle materials studied here consist of stiff elastic particles, and a softer matrix and interfacial transition zones between matrix and particles exhibiting nonlinear material responses. An incrementally applied uniform eigenstrain, along with a damage-plasticity constitutive model, are used to describe the shrinkage and cracking processes of the matrix and interfacial transition zones. The results showed that increasing particle diameter at constant volume fraction increases the crack widths and, therefore, permeability, which confirms previously obtained 2D modelling results. Furthermore, it was demonstrated that specimen thickness has, in comparison to the influence of particle size, a small influence on permeability increase due to microcracking. [ABSTRACT FROM AUTHOR]

For high heat flux and low thermal resistance, the multiple-artery heat-pipe spreader uses distributed high-permeability arteries (posts) for liquid supply and high-capillary pressure monolayer wick for liquid spreading and evaporation. Experiments indicate the receding meniscus transitions in monolayer play a role in sudden drop in thermal resistance prior to dryout. Using monolayer SEM images and the minimum surface energy principles, the meniscus dynamics up to dryout is analyzed, and the meniscus location, capillary pressure, effective thermal conductivity, and permeability are also predicted for heterogeneous, periodic sintered copper-particle (including bimodal particle size) unit cells. The liquid thickness is nonuniform within the heterogeneous unit cell, and with increase in the wick superheat local dryout occurs (meniscus snaps) in the loose-packed region influencing the wet-wick properties and the occurrence of the minimum thermal resistance. The monolayer wick continues to function under local dryout (away from post) until a receding dry front is formed followed by complete dryout. These predictions are in good agreement with experiments. The optimal wick thermal-hydraulic performance, i.e., dimensionless ratio of heat flux to thermal resistance (wick figure of merit Z m ) is sought through analysis. The uniform, sintered, close-packed 30–50 μ m particles give the highest Z m over a range of superheat in the wet regime, and 30 μ m particles give a record low resistance near 2.5 μ K/(W/m 2 ) [ABSTRACT FROM AUTHOR]

In this paper we present the numerical solution of the problem 2-D unsteady free convective heat and mass transfer flow over a moving semi-infinite vertical porous plate with thermal diffusion in presence of magnetic field, dissipative heat and Soret effects taking into account the induced magnetic field. The steady-state numerical solutions for the velocity field, induced magnetic field, temperature distribution and concentration distribution are obtained by the explicit finite difference method and the network simulation method. The obtained results have been shown graphically. The obtained numerical results were compared with other author data to demonstrate the efficiency of the method used, moreover all the cases the convergence is always quickly reached. An increase in Soret or Eckert number is found to strongly enhance the fluid velocity and temperature values, and this effect is reversed for the induced magnetic field. It is found that the flow velocity decreases with the increase in Hartmann and magnetic Prandtl number, and an opposite behaviour is observed for the fluid temperature. Applications of the study arise in the thermal plasma reactor modelling, the electromagnetic induction, MHD transport phenomena in chromatographic systems, and the magnetic field control of materials processing. The results show that both methods provide excellent approximations to the solution of this nonlinear system with high accuracy. [ABSTRACT FROM AUTHOR]

The effective thermal conductivity of granular materials is widely used in numerous geothermal engineering applications, such as the ground source heat pump (GSHP) system. However, for unsaturated granular materials, it is difficult to predict the thermal conductivity because of the interaction between solid and fluid in media. In this study, the effective thermal conductivity of unsaturated granular materials was measured, reviewed and analysed using a macroscopic pore structure network model with a randomly packed particles. The network model was verified by measured data (soil water characteristics curve, thermal conductivity and etc.) of three different glass beads and also Jumunjin sand (standard sand of South Korea). Upon the series of laboratory experiments, some modification to the existing network model were introduced, such as the use of soil water characteristic curve (SWCC) applied to modelling the thermal conductivity of granular materials. In addition, an empirical correlation between the fraction of the mean radius (χ) and the thermal conductivity at a given saturated condition was developed through comparison with the test results. In the range of lower degree of saturation (5%–20%), the modified network model shows relatively higher thermal conductivity than the laboratory measurements. However, for the higher degree of saturation (>40%), it shows a similar tendency to the laboratory measurements. [ABSTRACT FROM AUTHOR]

The present work analyzed the hydromagnetic effect on three-dimensional Couette flow of viscous incompressible, electrically conducting and Newtonian fluid through a porous medium bounded by two horizontal parallel porous flat plates with transverse sinusoidal injection of the fluid at the stationary plate and its corresponding removal by periodic suction through the plate in uniform motion. The flow becomes three dimensional due to this injection/suction velocity. Approximate solutions are obtained for the flow field, the pressure, the skin-friction, the temperature field, and the rate of heat transfer. It is found that the cross velocity w is reduced considerably with a rise in the magnetic body parameter ( M ), permeability parameter ( K ) and injection/suction ( λ ) in the forward flow, while a reverse effect is observed in the backward flow. An increase in K or λ is found to escalate the main flow velocity whereas an increase in the magnetic body parameter ( M ) is shown to exert the opposite effect. Similarly, the shear stress due to main flow is considerably increased with an increase in M and K . The results show that both methods, perturbation and electrical network schemes provides excellent approximations to the solution of this nonlinear system with high accuracy. The acquired knowledge in our study can be used by designers to control Magnetohydrodynamic (MHD) flow as suitable for a certain application. Other possible applications include materials processing, MHD propulsion thermo-fluid dynamics, boundary layer in aerodynamics, chemical engineering etc. [ABSTRACT FROM AUTHOR]

In this paper, a subway network is represented as a dynamic, directed and weighted graph, in which vertices represent subway stations and weights of edges represent the number of vehicles passing through the edges by considering vehicles operation timetable. Meanwhile the definitions of static and dynamic metrics which can represent vertices’ and edges’ local and global attributes are proposed. Based on the model and metrics, standard deviation is further introduced to study the dynamic properties (heterogeneity and vulnerability) of subway networks. Through a detailed analysis of the Beijing subway network, we conclude that with the existing network structure, the heterogeneity and vulnerability of the Beijing subway network varies over time when the vehicle operation timetable is taken into consideration, and the distribution of edge weights affects the performance of the network. In other words, although the vehicles operation timetable is restrained by the physical structure of the network, it determines the performances and properties of the Beijing subway network. [ABSTRACT FROM AUTHOR]

In the near future, an important milestone for the evolution of wireless technologies will be the deployment of 5G network, having the target of supporting very huge data rate generated by a very high number of devices. One of the main technological enablers in this evolution is the joint SDN/NFV paradigm, defined in the last years to support the softwarization process of the Telco Operator networks. Given the very hard quality of experience (QoE) and quality of service (QoS) requirements in some application scenarios, mainly in terms of end-to-end delay, a challenging activity is to realize tools that can support network architects in performance evaluation and network design. With this in mind, this paper proposes a simulative tool for 5G networks, which is able to capture delay statistics due to both CPU load and transmission link congestions in NFVI-PoP nodes. The model is then applied to a case study to demonstrate how it can be applied for performance evaluation. [ABSTRACT FROM AUTHOR]

Granular filters retain base material within the narrowest constrictions of their void network. A direct comparison of the base material particle size distribution (PSD) and the filter constriction size distribution (CSD) cannot easily be used to assess filter-base compatibility. Here a conceptually simple random-walk network model using a filter CSD derived from discrete element modelling and base PSD is used to assess filter-base compatibility. Following verification using experimental data the model is applied to assess empirical ratios between filter and base characteristic diameters. The effects of filter density, void connectivity and blocking in the first few filter layers are highlighted. [ABSTRACT FROM AUTHOR]

During transcription, RNA polymerase II elongates RNA by adding nucleotide triphosphates (NTPs) complementary to a DNA template. Structural studies have suggested that NTPs enter and exit the active site via the narrow secondary pore but details have remained unclear. A kinetic model is presented that integrates molecular dynamics simulations with experimental data. Previous simulations of trigger loop dynamics and the dynamics of matched and mismatched NTPs in and near the active site were combined with new simulations describing NTP exit from the active site via the secondary pore. Markov state analysis was applied to identify major states and estimate kinetic rates for transitions between those states. The kinetic model predicts elongation and misincorporation rates in close agreement with experiment and provides mechanistic hypotheses for how NTP entry and exit via the secondary pore is feasible and a key feature for achieving high elongation and low misincorporation rates during RNA elongation. [ABSTRACT FROM AUTHOR]

To improve the accuracy of the DC network model, we re-examine the power flow equations and propose an improved network model for optimal power flow (OPF) calculation with reactive power and network losses. Voltage angle and the square of voltage magnitude are used as independent variables. A mathematical transformation of the nonlinear voltage magnitude term is used, which decomposes the voltage magnitude term to a linear expression and a quadratic expression. The quadratic expression presents the influence of voltage magnitude on network losses. To handle the non-convexity of the OPF model caused by the network losses, a convex relaxation method is used. The relaxed model and the proposed OPF model are typically equivalent in transmission systems. Methods of restricting the potential relaxation errors are introduced. It is shown that the accuracy of the proposed network model can be further improved with initial points of voltage angles. Case studies in several IEEE benchmark systems validate the performance of the proposed formulation and approach. [ABSTRACT FROM AUTHOR]

The structural evolution of liquid Mg–Al alloys has been investigated with composition variation from pure Mg to Al at 973 K using ab initio molecular dynamics simulation. The local structures are characterized in terms of pair correlation functions, coordinate numbers, and bond pairs. The atomic interactions are evaluated by the electronic structures to reveal the formation mechanism of the liquid structures. As a result of the fact that the formation of Mg–Al bonds is actually restrained by Al–Al bonds with lower energy, the initial local structures around Mg trend to be maintained as the fragments during the structural evolution, and the discrete Al atoms transforms to be segregated around Mg in the composition region from 18.75 to 31.25 at.% Al. Based on these structural characteristics, the liquid structures are modeled as the Al bonded networks surrounding Mg fragments. Further, the dynamical properties are reflected by the self-diffusion coefficients. The results show that the diffusion of Mg atoms is actually limited by Al bonded networks, which confirm the reliability of the model. It is also worth expecting that the structure model may provide a new viewpoint for further study on the complex metallic alloys in solid Mg–Al system. [ABSTRACT FROM AUTHOR]

For understanding dynamical phenomena Barabási and Albert shown a process that has been a useful mathematical method, and was changed in various forms and applied in researching of scale-free networks. We focus on dynamic differential equations like that by Barabási and Albert, and construct network models by the typical and generalized dynamic differential equations. Analysis of degree distribution and further problems on our models are given. [ABSTRACT FROM AUTHOR]