Average Paths Research Articles

Household health and medical care consumption decisions considering intertemporal uncertainty

To assist residents in exploring the optimal household health and medical care consumption decision-making strategies in an intertemporal uncertain environment, a consumption decision model based on a three-dimensional (3D) path planning PSO-GA-ACO was constructed. The results showed significant differences in the average optimal paths for household health and medical care consumption decisions among residents with different consumption preferences during the three different periods of the Covid-19 pandemic. During the Covid-19 period, the gap in household health and medical care consumption narrowed among residents with different consumption preferences, especially between steady and impulsive consumers. When comparing the post-Covid-19 period with the pandemic period, the monthly health and medical care consumption amounts decreased for both conservative and impulsive consumers, while it continued to rise for steady consumers. The 3D path planning PSO-GA-ACO algorithm improved the speed and accuracy of searching for optimal solutions and better described the decision-making behavior of household health and medical consumption compared to the traditional 3D path planning algorithms PSO, GA, and ACO. This study not only provides optimal household health and medical care consumption decision-making strategies for residents facing complex and uncertain environments but also offers scientific evidence for healthcare manufacturers and suppliers to take effective production and supply actions.

Chaotic Phase Modulation Direct-Sequence Spread Spectrum-Assisted Adaptive Serial Cancellation List Decoding Method for Underwater Acoustic Communication

Addressing the challenges of high decoding latency, reduced spectral efficiency, and substantial storage requirements in a Cyclic Redundancy Check (CRC)Aided Successive Cancellation List (CA-SCL) polar decoder, this paper proposes a chaotic phase modulation direct-sequence spread spectrum (CPMDSSS)-assisted adaptive serial cancellation list decoding method for underwater acoustic communication. The method involves segmenting the information bits to be transmitted, computing CRC for each segment, and mapping all CRCs to a CPMDSSS, which is then modulated onto the pilot subcarriers of underwater acoustic orthogonal frequency-division multiplexing (OFDM) to increase spectral efficiency. At the receiving end, CRCs are obtained by demodulating the CPMDSSS to verify the segmented information and adaptively select the number of decoding paths. The theoretical analysis and simulation results demonstrate that compared to CA-SCL, the proposed method effectively reduces the required storage units and improves spectral efficiency, with an average reduction of approximately 80% in the decoding paths. Sea trials further indicate that the proposed method reduces the average decoding paths by approximately 71% and decreases the average decoding delay by approximately 64% compared to CA-SCL.

Constraints on the Evolution of the Ionizing Background and Ionizing Photon Mean Free Path at the End of Reionization

The variations in Lyα forest opacity observed at z > 5.3 between lines of sight to different background quasars are too strong to be caused by fluctuations in the density field alone. The leading hypothesis for the cause of this excess variance is a late, ongoing reionization process at redshifts below six. Another model proposes strong ionizing background fluctuations coupled to a short, spatially varying mean free path of ionizing photons, without explicitly invoking incomplete reionization. With recent observations suggesting a short mean free path at z ∼ 6, and a dramatic improvement in z > 5 Lyα forest data quality, we revisit this latter possibility. Here, we apply the likelihood-free inference technique of approximate Bayesian computation (ABC) to jointly constrain the hydrogen photoionization rate ΓHI and the mean free path of ionizing photons λ mfp from the effective optical depth distributions at z = 5.0–6.1 from XQR-30. We find that the observations are well-described by fluctuating mean free path models with average mean free paths that are consistent with the steep trend implied by independent measurements at z ∼ 5–6, with a concomitant rapid evolution of the photoionization rate.

Experimental study on the Klinkenberg effect for gas permeability in carboniferous shales, Eastern Qaidam Basin, China

It is crucial to understand the conditions and influencing mechanisms of shale gas slip effect for predicting the productivity of shale gas reservoirs. This study focuses on the Carboniferous Hurleg Formation shales in the eastern Qaidam Basin and conducts gas permeability tests using different gases (He/N2), as well as geochemical and pore-structure tests. The slip behavior of different gases in micro- and nanopores as well as the anisotropy of gas permeability were analyzed and discussed. The results show that helium permeability is 1.81–3.56 times higher than nitrogen permeability, with a greater difference at lower pore pressures. These permeability differences are attributed to variations in gas molecule size and slip effects. Specifically, the slip effect of helium gas has a greater contribution to permeability at lower pore pressures, with a helium slip factor averaging 2.79 times that of nitrogen. The effective pore size of shale, calculated based on the helium slip factor, is 0.74 to 1.51 times larger than when nitrogen is used, with an average of 1.67 times. Helium molecules have smaller diameters and longer average molecular free paths, resulting in a more pronounced slip effect compared to nitrogen. While helium does not adsorb, nitrogen exhibits some adsorption, causing radial expansion during gas penetration. Furthermore, when testing with different gases, the horizontal permeability (S043∥; S052∥) is higher than the vertical permeability (S043⊥; S052⊥). The anisotropy of permeability is controlled by the pore system formed by the arrangement and combination of minerals. Calcium-rich samples (S052) tend to exhibit higher anisotropy compared to calcite-rich samples (S043). The effective pore size in the vertical sample is larger than that in the parallel sample, and the gas slip effect is significantly greater in the vertical sample. These findings provide valuable data for future studies on shale gas slip effects and productivity prediction.

Exploring alterations of brain networks of AD patients using WTC method

Objective: To explore the influences of different frequency bands on preprocessing of resting-state fMRI datasets used by the Wavelet Transform Coherence (WTC) method, and to study changes in the functional brain networks of AD patients. Method: Resting-state fMRI datasets of 10 AD patients and 11 healthy controls were collected in this study and time series of 90 brain regions defined by AAL (Automated Anatomical Labeling) were exacted after preprocessing. Wavelet transformation was performed for each time series, and a functional brain network were established in different frequencies (0.125Hz, 0.0625Hz) using the WTC (Wavelet Transform Coherence) method. The topology parameters of networks, containing global efficiency, clustering coefficient, average short paths length and small world property were calculated and averaged within each group. Result: The results imply that there are significant differences of topology parameters in networks of different frequencies. Likewise, statistical analysis of topology parameters of AD and HC (Healthy Controls) show that global efficiency, clustering coefficient and small world properties of AD all decreased by varying degrees, while the short path length of AD remained longer. Conclusion: Our research provides a theoretical basis for the choice of filter bands for data preprocessing in functional magnetic resonance imaging. The findings may serve as indicators for early diagnosis of AD patients.

Smaller and Denser Speech Graphs in Nondemented Patients with Progressive Supranuclear Palsy.

The well-established semantic fluency test measures the ability to produce a sequence of spoken words from a particular category within a limited period of time. Like patients with Parkinson's disease (PD), patients with progressive supranuclear palsy (PSP) tend to produce fewer correct words than age-matched healthy adults. This study further examined the difference between patients with PSP and PD in their semantic fluency performance using a graph theory-based approach. Twenty-nine patients with PSP Richardson's syndrome (PSP-RS), thirty-eight patients with PD, and fifty-one healthy controls (HC) were recruited. All participants completed a standard semantic fluency test (animals). Their verbal responses were recorded, transcripted, and transformed into directed speech graphs. The speech graphs of the PSP-RS group showed higher density, shorter diameter, and shorter average shortest path than those of the PD and HC groups. It indicates that the PSP-RS group produced smaller and denser speech graphs than the PD and HC groups. In the PSP-RS group, moreover, the average shortest paths of the speech graphs correlated with the severity of motor symptoms. This study shows the potential of the graph theory-based approach in distinguishing the semantic fluency performance of nondemented patients with PSP-RS and PD.

Link cascade failure in directed networks with higher-order structures

In the field of complex networks, cascade failure has been widely studied due to its ubiquity and profound impact. While in recent years, the conceptualization of “higher-order structures” has brought the research of complex networks to a new stage. However, the effect of higher-order structures on cascade failure dynamics remains to be understood. In this study, we consider a link cascade failure mechanism in combination with higher-order structures. In the process of link cascade failure, we find that the network robustness would be affected by the nodes' ability to withstand link loss and the proportion of higher-order structures, corresponding to the changes in the critical point and the type of phase transition. These findings have been examined in the modeled and real directed networks. Interestingly, in empirical networks, we find that for networks with larger average shortest paths (e.g. US Airlines), the impact of higher-order structures on network robustness is more prominent.

The description of dynamical fission process using improved quantum molecular dynamics model incorporated with microscopic potential energy surface

A new method, namely the improved quantum molecular dynamics model incorporated with the microscopic potential energy surface, is proposed to simulate the dynamical evolution from saddle to scission point and to calculate the isotope yields of fission fragments in thermal neutron-induced fission of actinide nuclei near 236U. With this approach, the shell and pairing effects are introduced by the microscopic potential energy, while the isospin effect, the dynamical effects of fluctuations and correlations in fission process are automatically included in the improved quantum molecular dynamics model. These two aspects eventually influence the productions of fission fragments. The calculated both charge and mass distributions of fission fragments for a series of actinide nuclei are in overall agreement with the evaluated data from JEFF-3.3 and ENDF/B-VIII.0. The isotope distributions of fission fragments in the fission of 236U reproduce the data reasonable well, from which we extract the relationship between peak mass number of the isotope distributions and the charge number of fission fragments. We find that this relationship deviates linear relation slightly at certain cases, which indicates the isospin nonequilibrium effect. By tracking back to the fission process on the two dimension plane of elongation and mass asymmetry of prefragments, we find that the necked-in shape and the separation of two parts happen much faster for the 132Sn than 128,136Sn in the time evolution of the distance R between the centers of two parts of the fissioning system. The events producing 128,132,136Sn and 136,140,144Xe present two kinds of average fission paths with bypassing and going through the minimum-energy valley of the microscopic potential energy surface, respectively.

Simulating stock prices using geometric Brownian motion model under normal and convoluted distributional assumptions

This study proposes a modified Geometric Brownian motion (GBM), to simulate stock price paths under normal and convoluted distributional assumptions. This study utilised four selected continuous probability distributions for the convolution because of shared properties, including normality, and parameters that have a standard distribution with a location and scale parameters of zero and one, in that order. The findings from this study revealed that the simulation of price paths looks identical under the assumption of normal distribution and normal convolved with normal, Laplace, and Rice distributions for different sample sizes and parameter settings but differs with respect to the Cauchy distribution. Furthermore, the study found that all the mean absolute percentage error (MAPE) and mean square error (MSE) values for the normal and convoluted distributions underlying the GBM were approximately less than 10%, indicating high forecast accuracy. However, the average simulated price paths for the GBM under the normal distribution was found to be significantly different from the GBM under convoluted distribution when a t-test was employed for different sample sizes and different settings of the drift and volatility values.

A deep learning framework for analyzing cloud characteristics of aggregated convection using cloud‐resolving model simulations

AbstractThis study introduces a framework to extract the high‐dimensional nonlinear relationships among state variables for aggregated convection. The prototype of such a framework is developed that applies the convolutional neural network models (CNN models) to retrieve the cloud characteristics from cloud‐resolving model (CRM) simulations. CNN model prediction factors are hidden in the high dimensional weighted parameters in each neural network layer. Therefore, we can dig out relevant physics processes by iterating the CNN models' training process and eliminating the features with the physics explanation we can provide at a given stage. Within a few iterations, explainable nonlinear relationships among variables can be provided. We identified that the averaged cloud water path (CWP), the maximum value of CWP in each cloud, and the cloud coverage rate are essential for identifying aggregation. Furthermore, by analyzing the encoded channels of the CNN model, we found a strong relationship between aggregation, cloud peripherals, and fractal dimensions. The results suggest that the important nonlinear cloud characteristics for identifying the aggregation can be captured with the proper adjustment and limitation of the input data to the CNN models. Our framework provides a possibility that we can explore the high dimensional relationship between the physics process with the assistance of the CNN model.

Community Detection Fusing Graph Attention Network

It has become a tendency to use a combination of autoencoders and graph neural networks for attribute graph clustering to solve the community detection problem. However, the existing methods do not consider the influence differences between node neighborhood information and high-order neighborhood information, and the fusion of structural and attribute features is insufficient. In order to make better use of structural information and attribute information, we propose a model named community detection fusing graph attention network (CDFG). Specifically, we firstly use an autoencoder to learn attribute features. Then the graph attention network not only calculates the influence weight of the neighborhood node on the target node but also adds the high-order neighborhood information to learn the structural features. After that, the two features are initially fused by the balance parameter. The feature fusion module extracts the hidden layer representation of the graph attention layer to calculate the self-correlation matrix, which is multiplied by the node representation obtained by the preliminary fusion to achieve secondary fusion. Finally, the self-supervision mechanism makes it face the community detection task. Experiments are conducted on six real datasets. Using four evaluation metrics, the CDFG model performs better on most datasets, especially for the networks with longer average paths and diameters and smaller clustering coefficients.

Spatiotemporal investigation and determinants of interprovincial innovation network from a multilayer network perspective

ABSTRACT Technology cooperation and technology transfer can be beneficial for realising the flow of innovative resources and promoting technological progress and growth, which is crucial to achieving sustainable and coordinated economic development. We investigate the structural characteristics of interprovincial innovation multilayer network, also called technology cooperation and transfer network, including the technology cooperation sub-network and technology transfer sub-network. Data are derived from Patent Information Service Platform for 2008–2018. The results are as follows: (1) the two sub-networks and multilayer network show the increasing density, and all of those networks display large cluster coefficients and short average paths. The degree between different networks is highly relevant. GDP, indegree and outdegree are linearly related. (2) As the two sub-networks in different provinces become increasingly integrated, the core provinces control the flow in the network and the edge provinces surround the core provinces, indicating a clear ‘core-edge’ structure. (3) The exponential random graph model regression results demonstrate a significant ‘path dependence’ property for technology cooperation-transfer network. GDP, R&D, distance and railway freight flow have a significant impact on network format.

Black hole and host galaxy growth in an isolated z ∼ 6 QSO observed with ALMA

The outstanding mass growth of supermassive black holes (SMBHs) at the epoch of reionisation and its relation to the concurrent growth of their host galaxies poses challenges to theoretical models aimed at explaining how these systems formed on short timescales (< 1 Gyr). To trace the average evolutionary paths of quasi-stellar objects (QSOs) and their host galaxies in the plane of BH mass to host mass (Mdyn), we compare the star formation rate (SFR), derived from the accurate estimate of the dust temperature and the dust mass (Tdust, Mdust) based on infrared and sub-millimeter (sub-mm) spectral energy distribution (SED), with the BH accretion rate, derived from Lbol based on X-ray and optical and ultraviolet SED. To this aim, we analysed a deep ALMA observation of the sub-mm continuum, [CII], and H2O of the z ∼ 6 QSO J2310+1855 with a resolution of 900 pc, which enabled a detailed study of dust properties and cold gas kinematics. We performed an accurate SED analysis obtaining a dust temperature of Tdust = 71 ± 4 K, dust mass Mdust = (4.4 ± 0.7)×108 M⊙, and total far-infrared luminosity of LTIR = 2.5−0.5+0.6 × 1013 L⊙. The implied active galactic nuclei (AGN) – corrected SFR = 1240−260+310 M⊙ yr−1 is a factor of 2 lower than previously reported for this QSO. We measured a gas-to-dust ratio of GDR = 101 ± 20. The dust continuum and [CII] surface brightness profiles are spatially extended out to r ∼ 6.7 kpc and r ∼ 5 kpc, respectively, with half-light radii of 0.9 and 1.1 kpc for the dust and gas, respectively. The derived gas surface density, Σgas, and star formation rate density, ΣSFR, place the J2310+1855 host galaxy above the Kennicutt-Schmidt relation. We derived a best estimate of the dynamical mass Mdyn = 5.2 × 1010 M⊙ within r = 1.7 kpc based on a dynamical model of the system with a rotating disk inclined at i = 25 deg. The Toomre parameter profile across the disk is Qgas ∼ 3 and implies that the disk is unstable. We found that SFR/Mdyn > ṀBH/MBH, suggesting that AGN feedback might be efficiently acting to slow down the SMBH accretion, while stellar mass assembly is still vigorously taking place in the host galaxy. In addition, we were also able to detect high-velocity emission on the red and blue sides of the [CII] emission line that is not consistent with disk rotation and traces a gaseous outflow. We derived an outflow mass Mout = 3.5 × 108 M⊙, and a mass outflow rate in the range Ṁout = 1800 − 4500 M⊙ yr−1. The implied Ėout ∼ 0.0005 − 0.001 Lbol is in agreement with the values observed for ionised winds. For the first time, we mapped a spatially resolved water vapour disk through the H2O v = 0 3(2, 2) − 3(1, 3) emission line detected at νobs = 274.074 GHz, whose kinematic properties and size are broadly consistent with those of the [CII] disk. The luminosity ratio LH2O/LTIR = 1.4 × 10−5 is consistent with line excitation by dust-reprocessed star formation in the interstellar medium of the host galaxy.

Optimizing the path of seedling transplanting with multi-end effectors by using an improved greedy annealing algorithm

Automated transplanters perform the repetitive task of replugging bad or missing cells with healthy seedlings in greenhouses. The work efficiency of transplanters can be improved by optimizing the transplanting paths of manipulators. In this study, an improved greedy simulated annealing algorithm (IGSA) based on MTSP model was proposed for path optimization. Aimed at improving the computation speed of the algorithm, a new improved 2-Opt algorithm based on the metropolis criterion was utilized. Moreover, the greedy algorithm (GRA) and the greedy genetic algorithm (GGA) were modified to adapt to the multi-end effectors’ replugging operation. The performance of IGSA and those of the GGA, GRA, and common sequence method (CSM) in path planning for replugging transplanting were compared in terms of their optimization effects and computation times. Then, manipulators with different numbers of end effectors (four, five, and six end effectors) were used to verify the increased effect of the path planning. Under the verification test conditions, the comparative average optimization ratios of GRA, GGA, and IGSA compared to the CSM were 4.87 %, 10.64 %, and 20.07 %, respectively. The performance ranking of the methods with short average paths was in the order of IGSA, GGA, GRA, and CSM. Compared with the average path shortening ratio of the four end effectors, those of the five and six end effectors were 11.44 % and 18.68 %, respectively. The average computation times of CSM, GRA, GGA, and IGSA were 0.002, 0.007, 6.94, and 3.49 s in MATLAB (R2019a), respectively. All of them can meet the real-time operation requirements except for GGA. In contrast to GGA, GRA, and CSM, the proposed IGGA performed effectively in the path planning of the seedlings’ replug transplanting owing to its comprehensive performance derived from its path optimization ratio and low cost in terms of computation time.

Paper 10: Influence of Arm Path on Elbow Varus Torque in Professional Baseball Pitchers

Objectives:Arm path, or the distance the hand travels from the glove to ball release, has become an increasingly discussed topic within the baseball community. Arm path is a combination of elbow flexion, shoulder external rotation, shoulder abduction, forearm pronation, and wrist flexion movements. Traditionally, pitchers have been taught to throw with a longer arm path during the first part of the pitching motion, which includes extending the throwing arm away from the body and delaying shoulder external rotation. Advocates of the shorter arm path have recently made a push to get pitchers to shorten up as it is thought that it results in a more efficient motion and this in turn may decrease injury risk. By shortening the arm path, the throwing arm is closer to the body in a more loaded position theoretically creating less torque on the elbow. However, there is a lack of published data to support either claim of the benefits of a shorter or longer arm path. The purpose of this study was to determine if there was a relationship between arm path and elbow varus torque in professional pitchers.Methods:Data from 285 professional pitchers were included in this study (189.9±5.9 cm, 95.1±9.8 kg). Pitchers were tested pitching 8-12 fastballs at 480 Hz using a 3D motion capture system (Motion Analysis Corp., Santa Rosa, CA). The fastest pitch from each pitcher was used in the analysis. Arm path was measured as the total path the hand marker traveled during the entire pitch. Arm path was also normalized as a percentage of arm length (%AL). The pitch was broken down into four time points: maximum knee height (MKH), foot contact (FC), maximum external rotation (MER), ball release (BR). Linear regressions were calculated to evaluate the relationship between arm path and elbow varus torque at each time point. Significance was set at p<0.05.Results:There was no significant relationship between arm path and elbow varus torque (Figure 1). Arm path did not have a significant effect on elbow varus torque for MKH to BR (r2=.04, p<0.001), MKH to FC (r2=.05, p<0.001), and FC to BR (r2=0.00, p=0.207). Furthermore, there was no significant effect on elbow varus torque for MKH to BR, MKH to FC, and FC to BR when arm path was normalized by the pitcher’s arm length (r2=0.00 p=0.262; r2=0.02 p=0.018; r2=0.01 p=0.081 respectively).Conclusions:Recent anecdotal trends towards a shorter arm path in pitchers does not alter elbow varus torque as compared to both average and longer arm paths in a sample of professional pitchers. None of the studied time periods within the pitching motion revealed correlations between elbow varus torque and shorter arm paths. This suggests that total distance travelled by the arm during the pitching motion may not be a significant risk factor for injury in professional level pitchers. However, it is possible that components of arm path (i.e. elbow flexion, shoulder rotation, and shoulder horizontal abduction) play a larger role in the influence on elbow varus torque than simply arm path. Future studies should investigate the combined effects of arm path and arm path’s components on elbow varus torque.Figure 1.Correlations of arm path to elbow varus torque during a pitch. The left hand column measures arm path in centimeters and the right hand column uses arm path normalized to pitcher’s arm length. There were no correlations in arm path to elbow varus torque. Maximum knee height. MKII; FC. foot contact; BR. ball release.

Group composition of individual personalities alters social network structure in experimental populations of forked fungus beetles.

Social network structure is a critical group character that mediates the flow of information, pathogens and resources among individuals in a population, yet little is known about what shapes social structures. In this study, we experimentally tested whether social network structure depends on the personalities of individual group members. Replicate groups of forked fungus beetles (Bolitotherus cornutus) were engineered to include only members previously assessed as either more social or less social. We found that individuals expressed consistent personalities across social contexts, exhibiting repeatable numbers of interactions and numbers of partners. Groups composed of more social individuals formed networks with higher interaction rates, higher tie density, higher global clustering and shorter average shortest paths than those composed of less social individuals. We highlight group composition of personalities as a source of variance in group traits and a potential mechanism by which networks could evolve.

Evaluation of probabilistic constellation shaping performance in Flex Grid over multicore fiber dynamic optical backbone networks [Invited

In this paper, we present a worst-case methodology for estimating the attainable spectral efficiency over end-to-end paths across a Flex Grid over multicore fiber (MCF) optical network. This methodology accounts for physical link noise, as well as for the signal-to-noise ratio in the Add module ( S N R T X ) of spatial-division-multiplexing-enabled reconfigurable optical add and drop multiplexers (SDM-ROADMs), introducing a dominant noise contribution over that of their Bypass and Drop modules. The proposed methodology is subsequently used to quantify the benefits that probabilistic constellation shaping (PCS) can bring to Flex-Grid/MCF dynamic optical backbone networks, compared to using traditional polarization-multiplexed modulation formats. In a first step, insight is provided into the spectral efficiency attainable along the precomputed end-to-end paths in two reference backbone networks, either using PCS or traditional modulation formats. Moreover, in each one of these networks, two S N R T X values are identified: the S N R T X yielding the maximum average paths’ spectral efficiency, as well as an S N R T X that, although slightly degrading the average paths’ spectral efficiency (by 10%), would yet enable a cost-effective SDM-ROADM Add module implementation. Extensive simulations are conducted to analyze PCS offered load gains under 1% bandwidth blocking probability. Furthermore, the study lastly focuses on finding out whether lower fragmentation levels in Flex-Grid/MCF dynamic optical backbone networks can push PCS benefits even further.

Dynamics of Multiagent Reinforcement Learning Compared to Synchronisation Dynamics of Kuramoto Oscillators

Multiagent reinforcement learning is a widespread approach to optimisation problem on systems with possibility of feedback links. Multiple intelligent agents interact with each other to enhance search of the optimal behavioural patterns. Structure of interactions was shown to affect system dynamics, in particular, on learning dynamics of intelligent agents, resulting in emergent behaviour, which make topicality of optimal topology exploration for different task formulations. To effectively solve certain problems, the ability of agents to cooperate in the frame of environments is extremely important. The main idea of coordinated reinforcement learning is that agents, when performing actions, take into account the actions of their neighbors, and this is taken into account when updating their parameters. However, due to the fact that multi-agent environments are very dynamic, a large amount of computation is required to effectively train MARL system. We believe the development of graph formation method for multiagent reinforcement learning systems is able to decrease resource consumption. In order to contribute to this problem, we explore effects of complete, star, path, and ring graphs on learning dynamics in a predator-prey environment, which is compared to synchronisation dynamics on graphs. In contrast to synchronisation case, showing dependence on average shortest paths, predator-prey system has two types of agents, which results in different patterns of influence.

Evolving complex networks with logistic property: Global versus local growth

Considering the retarding effect of natural resources, environmental conditions, and other factors on network growth, the capacity of network nodes to connect to new edges is generally limited. Inspired by this hindered growth of many real-world networks, two types of evolving network models are suggested with different logistic growth schemes. In the global and local logistic network, the total number of network edges and the number of edges added into the network at each step are in line with the Logistic growth, respectively. The most exciting feature of the Logistic growth network is that the growth rule of network edges is first fast, then slow and finally reaches the saturation value [Formula: see text]. Theoretical analysis and numerical simulation reveal that the node degrees of two new networks converge to the same results of the BA scale-free network, [Formula: see text], as the growth rate [Formula: see text] approaches to 0. The local logistic network follows a bilateral power-law degree distribution with a given value of [Formula: see text]. Meanwhile, for these two networks, it is found that the greater [Formula: see text] and [Formula: see text], the smaller the average shortest paths, the greater the clustering coefficients, and the weaker the disassortativity. Additionally, compared to the local logistic growth network, the clustering feature of the global logistic network is more obvious.

The effect of cervical intervertebral disc degeneration on the motion path of instantaneous center of rotation at degenerated and adjacent segments: A finite element analysis

BackgroundThe motion path of instantaneous center of rotation (ICR) is a crucial kinematic parameter to dynamically characterize cervical spine intervertebral patterns of motion; however, few studies have evaluated the effect of cervical disc degeneration (CDD) on ICR motion path. The purpose of this study was to investigate the effect of CDD on the ICR motion path of degenerated and adjacent segments. MethodA validated nonlinear three-dimensional finite element (FE) model of a healthy adult cervical spine was used. Progressive degeneration was simulated with six FE models by modifying intervertebral disc height and material properties, anterior osteophyte size, and degree of endplate sclerosis at the C5–C6 level. All models were subjected to a pure moment of 1 Nm and a compressive follower load of 73.6 N to simulate physical motion. ICR motion paths were compared among different models. ResultsThe normal FE model results were consistent with those of previous studies. In degenerative models, average ICR motion paths shifted significantly anterior at the degenerated segment (β = 0.27 mm; 95% CI: 0.22, 0.32) and posterior at the proximal adjacent segment (β = −0.09 mm; 95% CI: −0.15, −0.02) than those of the normal model. ConclusionCDD significantly affected ICR motion paths at the degenerated and proximal adjacent segments. The changes at adjacent segments may be a result of compensatory mechanisms to maintain the balance of the cervical spine. Surgical treatment planning should take into account the restoration of ICR motion path to normal. These findings could provide a basis for prosthesis design and clinical practice.