An estimation formula for the average path length of scale-free networks

A universal estimation formula for the average path length of scale free networks is given in this paper. Different from other estimation formulas, most of which use the size of network, N, as the only parameter, two parameters including N and a second parameter α are included in our formula. The parameter α is the power-law exponent, which represents the local connectivity property of a network. Because of this, the formula captures an important property that the local connectivity property at a microscopic level can determine the global connectivity of the whole network. The use of this new parameter distinguishes this approach from the other estimation formulas, and makes it a universal estimation formula, which can be applied to all types of scale-free networks. The conclusion is made that the small world feature is a derivative feature of a scale free network. If a network follows the power-law degree distribution, it must be a small world network. The power-law degree distribution property, while making the network economical, preserves the efficiency through this small world property when the network is scaled up. In other words, a real scale-free network is scaled at a relatively small cost and a relatively high efficiency, and that is the desirable result of self-organization optimization.

Robust Hybrid Artificial Fish Swarm Simulated Annealing Optimization Algorithm for Secured Free Scale Networks against Malicious Attacks

Due to the recent proliferation of cyber-attacks,highly robust wireless sensor networks(WSN)become a critical issue as they survive node failures.Scale-free WSN is essential because they endure random attacks effectively.But they are susceptible to malicious attacks,which mainly targets particular significant nodes.Therefore,the robustness of the network becomes important for ensuring the network security.This paper presents a Robust Hybrid Artificial Fish Swarm Simulated Annealing Optimization(RHAFS-SA)Algorithm.It is introduced for improving the robust nature of free scale networks over malicious attacks(MA)with no change in degree distribution.The proposed RHAFS-SA is an enhanced version of the Improved Artificial Fish Swarm algorithm(IAFSA)by the simulated annealing(SA)algorithm.The proposed RHAFS-SA algorithm eliminates the IAFSA from unforeseen vibration and speeds up the convergence rate.For experimentation,free scale networks are produced by the Barabási–Albert(BA)model,and real-world networks are employed for testing the outcome on both synthetic-free scale and real-world networks.The experimental results exhibited that the RHAFS-SA model is superior to other models interms of diverse aspects.

Betweenness-based algorithm for a partition scale-free graph

Many real-world networks are found to be scale-free. However, graph partition technology, as a technology capable of parallel computing, performs poorly when scale-free graphs are provided. The reason for this is that traditional partitioning algorithms are designed for random networks and regular networks, rather than for scale-free networks. Multilevel graph-partitioning algorithms are currently considered to be the state of the art and are used extensively. In this paper, we analyse the reasons why traditional multilevel graph-partitioning algorithms perform poorly and present a new multilevel graph-partitioning paradigm, top down partitioning, which derives its name from the comparison with the traditional bottom-up partitioning. A new multilevel partitioning algorithm, named betweenness-based partitioning algorithm, is also presented as an implementation of top-down partitioning paradigm. An experimental evaluation of seven different real-world scale-free networks shows that the betweenness-based partitioning algorithm significantly outperforms the existing state-of-the-art approaches.

Rational scale of urban rail transit network based on system dynamics

Urban rail transit(URT) has been playing an important role in urban sustainable development with its advantages of high speed,large capacity,high efficiency and low pollution.Estimating URT network scale is the key to ensure the scientificity and feasibility of its construction.The existing studies on rational scale of URT network have not dealt with the interaction of supply and demand.This paper describes the establishment of a system dynamics model of rational URT network scale determination,considering the interaction between URT construction and city social economic development as well as the dynamic equilibrium of capital supply and traffic demand,and the verification of the model validity by applying it to the case of Wuhan City's URT construction.

AN IDENTITY-BASED KEY AGREEMENT SCHEME FOR LARGE SCALE SENSOR NETWORKS

To solve the problems of high memory occupation, low connectivity and poor resiliency against node capture, which existing in the random key pre-distribution techniques while applying to the large scale Wireless Sensor Networks （WSNs）, an Identity-Based Key Agreement Scheme （IBKAS） is proposed based on identity-based encryption and Elliptic Curve Diffie-Hellman （ECDH）. IBKAS can resist man-in-the-middle attacks and node-capture attacks through encrypting the key agreement parameters using identity-based encryption. Theoretical analysis indicates that comparing to the random key pre-distribution techniques, IBKAS achieves significant improvement in key connectivity, communication overhead, memory occupation, and security strength, and also enables efficient secure rekcying and network expansion. Furthermore, we implement IBKAS for TinyOS-2.1.2 based on the MICA2 motes, and the experiment results demonstrate that IBKAS is feasible for infrequent key distribution and rekeying for large scale sensor networks.

Estimation of Data Transfer Routes Fractal Dimension in Large Scale Networks

The article is devoted to the evaluation of fractal properties of routing data in computer large scale networks. Implemented the study of percolation network topological structures of large dimension and made their transformation into fractal macrostructure. An example of calculating the fractal dimension of the data path for the boundary of the phase transition between the states of network connectivity. The dependence of the fractal dimension of the percolation cluster on the size of the square δ-cover and conductivity value network of large dimension. It is shown that for the value of the fractal dimension of the route dc ≈ 1.5, network has a stable dynamics of development and size of clusters are optimized with respect to the current load on the network.

Properties of multi‑GNSS uncalibrated phase delays with considering satellite systems,receiver types,and network scales

The Wide-Lane(WL)and Narrow-Lane(NL)Uncalibrated Phase Delays(UPDs)are the prerequisites in the traditional Precise Point Positioning(PPP)Ambiguity Resolution(AR).As the generation mechanism of various biases becomes more complex,we systematically studied the impact factors of four satellite systems WL and NL UPDs from the perspective of parameter estimation.Approximately 100 stations in a global network are used to generate the UPDs.The results of different satellite systems show that the estimation method,update frequency,and solution mode need to be treated differently.Two regional networks with different receiver types,JAVAD,and Trimble,are also adopted.The results indicate that the receiver-dependent bias has an influence on UPD estimation.Also,the hardware delays can inhibit the satellite-side UPDs if these receiver-specific errors are not fully deployed or even misused.Furthermore,the temporal stability and residual distribution of NL UPDs are significantly enhanced by utilizing a regional network,with the improvements by over 68%and 40%,respectively.It demonstrates that different network scales exhibit the different implication of unmodeled errors,and the unmodeled errors cannot be ignored and must be handled in UPD estimation.

Computational and theoretical modeling of intermediate filament networks:Structure,mechanics and disease

Intermediate filaments, in addition to microtubules and actin microfilaments, are one of the three major components of the cytoskeleton in eukaryotic cells. It was discovered during the recent decades that in most cells, intermediate filament proteins play key roles to reinforce cells subjected to large-deformation, and that they participate in signal transduction, and it was proposed that their nanome- chanical properties are critical to perform those functions. However, it is still poorly understood how the nanoscopic structure, as well as the combination of chemical composition, molecular structure and interfacial properties of these protein molecules contribute to the biomechanical properties of filaments and filament networks. Here we review recent progress in computational and theoretical studies of the intermediate filaments network at various levels in the protein＇s structure. A multiple scale method is discussed, used to couple molecular modeling with atomistic detail to larger-scale material properties of the networked material. It is shown that a finer-trains-coarser method- ology as discussed here provides a useful tool in understanding the biomechanical property and disease mechanism of intermediate filaments, coupling experiment and simulation. It further allows us to improve the understanding of associated disease mechanisms and lays the foundation for engineering the mechanical properties of biomaterials.

Scaling of weighted spectral distribution in weighted small-world networks

Many real-world systems can be modeled by weighted small-world networks with high clustering coefficients. Recent studies for rigorously analyzing the weighted spectral distribution（W SD） have focused on unweighted networks with low clustering coefficients. In this paper, we rigorously analyze the W SD in a deterministic weighted scale-free small-world network model and find that the W SD grows sublinearly with increasing network order（i.e., the number of nodes） and provides a sensitive discrimination for each input of this model. This study demonstrates that the scaling feature of the W SD exists in the weighted network model which has high and order-independent clustering coefficients and reasonable power-law exponents.

En-route headway-based bus reliability with real-time data at network scale

Bus reliability has long attracted attention and been extensively studied to enhance service quality.However,existing research generally evaluates bus reliability of specific routes or stops.To this end,this study explores en-route bus reliability with real-time data at network scale.Drawing on data of bus automatic vehicle location and smart card usage in Ningbo,China,this study calculates headway-based reliability with the difference between actual and scheduled headway at each stop.To demonstrate the trend of stop-level reliability along a bus route,reliability is graded and visualized on a map with ridership at each stop,which is then weighted with passenger-boarding volume.Route-level reliability is then quantified and mapped,where unreliable service basically concentrates in or extends through the centre area.With respect to network-level reliability,temporal changes are demonstrated with ridership on weekdays and at the weekend.It is observed that on weekdays,the reliability trend is similar to that of ridership,implying a causal relationship between bus travel-time variation and bus waiting-time at stops.Furthermore,a reliability comparison between weekdays in December and October shows the necessity of evaluating periodically and around important events to avoid negative riding experiences that discourage public transport usage.This research provides insights for bus agencies to systematically evaluate service reliability both spatially and temporarily,in order to identify and prioritize the routes and stops where the scope for reliability improvement and the expected benefit are greatest.

SCALE-TYPE STABILITY FOR NEURAL NETWORKS WITH UNBOUNDED TIME-VARYING DELAYS

This paper studies scale-type stability for neural networks with unbounded time-varying delays and Lipschitz continuous activation functions. Several sufficient conditions for the global exponential stability and global asymptotic stability of such neural networks on time scales are derived. The new results can extend the existing relevant stability results in the previous literatures to cover some general neural networks.

CARVING-DETC: A network scaling and NMS ensemble for Balinese carving motif detection method

Balinese carvings are cultural objects that adorn sacred buildings. The carvings consist of several motifs,each representing the values adopted by the Balinese people. Detection of Balinese carving motifs ischallenging due to the unavailability of a Balinese carving dataset for detection tasks, high variance,and tiny-size carving motifs. This research aims to improve carving motif detection performance onchallenging Balinese carving motifs detection task through a modification of YOLOv5 to support adigital carving conservation system. We proposed CARVING-DETC, a deep learning-based Balinesecarving detection method consisting of three steps. First, the data generation step performs dataaugmentation and annotation on Balinese carving images. Second, we proposed a network scalingstrategy on the YOLOv5 model and performed non-maximum suppression (NMS) on the modelensemble to generate the most optimal predictions. The ensemble model utilizes NMS to producehigher performance by optimizing the detection results based on the highest confidence score andsuppressing other overlap predictions with a lower confidence score. Third, performance evaluation onscaled-YOLOv5 versions and NMS ensemble models. The research findings are beneficial in conservingthe cultural heritage and as a reference for other researchers. In addition, this study proposed a novelBalinese carving dataset through data collection, augmentation, and annotation. To our knowledge,it is the first Balinese carving dataset for the object detection task. Based on experimental results,CARVING-DETC achieved a detection performance of 98%, which outperforms the baseline model.

Multi-Wavelength Ultra-Weak Fiber Bragg Grating Arrays for Long-Distance Quasi-Distributed Sensing

Fiber Bragg grating(FBG)array,consisting of a number of sensing units in a single optical fiber,can be practically applied in quasi-distributed sensing networks.Serious signal crosstalk occurring between large-serial of identical FBGs,however,has limited the further increase in the number of sensing units,thus restricting applications only for short-distance sensing networks.To reduce the signal crosstalk,we design two novel types of 10-kilometer-long FBG arrays with 10000 equally spaced gratings,written on-line using a customized grating inscription system,which is affiliated to a drawing tower.Main factors causing signal crosstalk,such as spectral shadowing and multiple reflections,are firstly investigated in theory.Consistent with the theoretical findings,experimental results are proving that ultra-weak(the reflectivity of—40 dB)and multi-wavelength gratings of a number more than 10000 can be readily identified,with satisfied low crosstalk.The maximum attenuation of grating signal and minimum signal-to-noise ratio(SNR)in a single-wavelength array are 10.69 dB and 5.62 dB,respectively.As a comparison,by increasing the number of central wavelengths to three,the attenuation can be effectively reduced to 5.54dB and the minimum SNR has been improved to 8.14 dB.The current study significantly enhances the multiplexing capacity of FBG arrays and demonstrates promising potentials for establishing large-capacity quasi-distributed sensing networks.

MRHCA： a nonparametric statistics based method for hub and co-expression module identification in large gene co-expression network

Background： Gene co-expression and differential co-expression analysis has been increasingly used to study co- functional and co-regulatory biological mechanisms from large scale transcriptomics data sets. Methods： In this study, we develop a nonparametric approach to identify hub genes and modules in a large co- expression network with low computational and memory cost, namely MRHCA. Results： We have applied the method to simulated transcriptomics data sets and demonstrated MRHCA can accurately identify hub genes and estimate size of co-expression modules. With applying MRHCA and differential co- expression analysis to E. coil and TCGA cancer data, we have identified significant condition specific activated genes in E. coil and distinct gene expression regulatory mechanisms between the cancer types with high copy number variation and small somatic mutations. Conclusion： Our analysis has demonstrated MRItCA can （i） deal with large association networks, （ii） rigorously assess statistical significance for hubs and module sizes, （iii） identify co-expression modules with low associations, （iv） detect small and significant modules, and （v） allow genes to be present in more than one modules, compared with existing methods.