Number Of Links Research Articles

Predicting the Conversion from Mild Cognitive Impairment to Alzheimer's Disease Using Graph Frequency Bands and Functional Connectivity-Based Features.

Accurate prediction of the progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD) is crucial for disease management. Machine learning techniques have demonstrated success in classifying AD and MCI cases, particularly with the use of resting-state functional magnetic resonance imaging (rs-fMRI) data.This study utilized three years of rs-fMRI data from the ADNI, involving 142 patients with stable MCI (sMCI) and 136 with progressive MCI (pMCI). Graph signal processing was applied to filter rs-fMRI data into low, middle, and high frequency bands. Connectivity-based features were derived from both filtered and unfiltered data, resulting in a comprehensive set of 100 features, including global graph metrics, minimum spanning tree (MST) metrics, triadic interaction metrics, hub tendency metrics, and the number of links. Feature selection was enhanced using particle swarm optimization (PSO) and simulated annealing (SA). A support vector machine (SVM) with a radial basis function (RBF) kernel and a 10-fold cross-validation setup were employed for classification. The proposed approach demonstrated superior performance, achieving optimal accuracy with minimal feature utilization. When PSO selected five features, SVM exhibited accuracy, specificity, and sensitivity rates of 77%, 70%, and 83%, respectively. The identified features were as follows: (Mean of clustering coefficient, Mean of strength)/Radius/(Mean Eccentricity, and Modularity) from low/middle/high frequency bands of graph. The study highlights the efficacy of the proposed framework in identifying individuals at risk of AD development using a parsimonious feature set. This approach holds promise for advancing the precision of MCI to AD progression prediction, aiding in early diagnosis and intervention strategies.

Global trends in research of venous thromboembolism associated with lower limb joint arthroplasty: A bibliometric analysis.

This study aims to visualize publications related to venous thromboembolism (VTE) and lower limb joint arthroplasty to identify research frontiers and hotspots, providing references and guidance for further research. We retrieved original articles published from 1985 to 2022 and their recorded information from the Web of Science Core Collection. The search strategy used terms related to knee or hip arthroplasty and thromboembolic events. Microsoft Excel was used to analyze the annual publications and citations of the included literature. The rest of the data were analyzed using the VOSviewer, citespace and R and produced visualizations of these collaborative networks. We retrieved 3543 original articles and the results showed an overall upward trend in annual publications. The United States of America had the most significant number of publications (Np) and collaborative links with other countries. McMaster University had the greatest Np. Papers published by Geerts WH in 2008 had the highest total link strength. Journal of Arthroplasty published the most articles on the research of VTE associated with lower limb joint arthroplasty. The latest research trend mainly involved "general anesthesia" "revision" and "tranexamic acid." This bibliometric study revealed that the research on VTE after lower limb joint arthroplasty is developing rapidly. The United States of America leads in terms of both quantity and quality of publications, while European and Canadian institutions and authors also make significant contributions. Recent research focused on the use of tranexamic acid, anesthesia selection, and the VTE risk in revision surgeries.

Is order-2 proportionality good enough for approximating the most likely path flow in user equilibrium traffic assignment?

The proportionality condition is a standard approach to dealing with the non-uniqueness issue in the user equilibrium (UE) traffic assignment problems (TAP). Although the proportionality condition can reduce the degree of arbitrariness, it remains unclear how much arbitrariness remains and whether it can meaningfully affect model outcomes and relevant decisions that depend on them. The answers to these questions are impeded by the lack of an efficient algorithm that can find the exact maximum entropy UE path flow solution for networks of practical size. In this paper, we fill this gap by developing a high-performance augmented Lagrangian algorithm that effectively exploits the special problem structure. Our numerical results reveal that there are a considerable number of links with non-negligible arbitrariness in the solution generated by the proportionality condition, and that this problem becomes worse if the level of congestion increases in the network. Since about a decade ago, many practitioners have relied on state-of-the-art traffic assignment tools based on the proportionality condition to perform select link analysis, among other applications. The results reported herein are a reminder that their toolbox may need reevaluation and perhaps an upgrade.

Effect of Negative Pressure on the Structure and Diffusion Process of Silicon Dioxide at a Liquefied Nitrogen Temperature Using Molecular Dynamics Simulations

This study uses molecular dynamics (MD) simulations to investigate the effect of negative pressure on the structure and diffusion process of SiOx structural units (x = 4, 5) in Silicon dioxide at a liquefied nitrogen temperature. When decreases the pressure from 0 GPa to -10 GPa at 70 K, the lengths of the links Si-Si, Si-O, and O-O initially increase and then decrease, the system size increases, and the total energy of the system increases. During the diffusion process, number of structural units SiO4 increases, whereas the number of structural units SiO5 decreases. The average coordination number of link Si-O is constantly 4.0, while the average coordination number of link O-O decreases from 7.0 to 6.0, leading to changes in the microstructural characteristics. This is accompanied by changes in bond angles, with SiO4 has is 105 (degree) and SiO5 decreasing from 90 (degree) to 85 (degree). The length of the links increases from 1.64 Å to 1.66 Å for number of structural units SiO4 and increases from 1.68 Å to 1.74 Å for SiO5 units. These findings provide a basis for future experimental studies aimed at the research and development of advanced materials.

Kinematics and spatial structure analysis of TBM gunite robot based on D–H parameter method

In modern tunnel construction, TBM (Tunnel Boring Machine) plays a very important role. In response to the needs of tunnel wall reinforcement and TBM automated construction for tunnel construction, a shotcrete mechanism mounted on the TBM is designed. In order to evaluate the kinematic performance of the mechanism, this paper studies the forward and inverse kinematics and spatial architecture of the TBM shotcrete robot. Firstly, based on the D–H parameter method, the number of joints and links is determined and structural analysis is performed to obtain the robot's forward kinematics equation, achieving the mapping between joint space and pose space. Then, by determining the joint variables, the mapping of the end tool in Cartesian space is achieved. Finally, based on the Monte Carlo random sampling method, the workspace of the robot is constructed, and its reachability and flexibility within the robot workspace are evaluated. The performance of the device is verified by building a prototype, which meets the requirements well. Through the research in this paper, it can provide theoretical basis and guidance for the design and control of the shotcrete robot.

INCREASING THE ACCURACY OF THE POWER LINES MODELING BASED ON PMU

The article discusses the modeling of power lines based on PMU data to assess the state of overhead lines. Current approaches to solving the problem are analyzed. The advantages and disadvantages of these approaches are presented. It has been established that when modeling power lines are traditionally represented in the form of a - circuits scheme, which leads to methodological modeling errors. To reduce the methodological error of modeling, overhead lines (OHLs) can be presented in the form of equations with distributed parameters, which allows for maximum accuracy. However, distributed line equations contain hyperbolic functions and reduce performance in real-time simulations. Based on this, it is noted that the selection of an adequate mathematical model of overhead lines with the appropriate accuracy of synchronized vector measurements in real time when assessing the state of power lines is an important stage of modeling. The article discusses overhead line models using equations with distributed parameters, a -circuits, an equivalent - circuits and chain -circuits depending on the number of links. The results of calculating the mode based on the modeling accuracy using the example of a 500 kV overhead line are presented, as well as the results of comparing various equivalent circuits. It is shown that when modeling overhead line modes based on the results of PMU measurements, representing power lines as chain circuits with links 50 km or less in length is appropriate, since it makes it possible to obtain a model accuracy that is adequate to the PMU measurements. Methodological errors in modeling ultra-high voltage transmission lines using the equations of long lines have been studied. The obtained results can be used for monitoring, analysis and operational management of the electric power system

Kalman-filter based hierarchical channel estimation for RIS-aided mmWave communication systems

Reconfigurable intelligent surface (RIS) technology has emerged as a promising solution to enhance network coverage and spectral efficiency in mmWave communication systems, where the RIS-aided localization has attracted much attention for its capability of simultaneously obtaining two geographically separated copies of steering vectors of users at one base station (BS). However, the estimation of the cascaded channel parameters of RISs still remains a challenging problem due to the passive nature of RISs and the large number of channel links between the BS and RIS. To tackle this issue, we propose to jointly employ the multiple signal classification (MUSIC) algorithm and the Kalman filter (KF) to hierarchically estimate the azimuth angle and the path loss with the help of an anchor node. The simulation results indicate that the proposed method is significantly superior to the benchmark technique in terms of accuracy and computational complexity, and reduces the pilot frequency overhead by over 40%.

Network structure indicators predict ecological robustness in food webs

AbstractFood web robustness is a critical aspect of ecosystem stability and has been extensively studied in ecology. However, the potential of machine learning techniques in predicting food web robustness and the identification of key network structure indicators have not been fully explored. We compared the suitability of different machine learning methods and assessed the relative importance of network structure indicators for predicting the robustness of food webs. We utilized a variety of food web datasets spanning different ecosystems to calculate network structure indicators, which include average distance (AD), betweenness centrality (BC), directional connectivity (C), closeness centrality (CC), diameter (D), degree centrality (DC), edge betweenness centrality (EBC), number of links (L), linkage density (LD), and number of nodes (N). We then compared the performance of machine learning methods, including artificial neural network (ANN), random forest (RF), least absolute shrinkage and selection operator (LASSO), and decision tree (DT), and evaluated the relative importance of network structure indicators on robustness predictions. The results demonstrate that the RF model has the best performance (MAE = 0.0178, RMSE = 0.0263, R2 = 0.9063). Meanwhile, the CC indicator has a significant impact in predicting robustness of food webs. It is suggested that both the RF model and the CC indicator should be considered seriously in predicting food web robustness. This research elucidates the differential outcomes when various machine learning methodologies and indicators are employed to predict the robustness of food webs. It significantly enhances our understanding by demonstrating the precise capability of machine learning models in forecasting the robustness of food webs.

A study of the effect of array geometry on improving channel hardening in mm-Wave massive MIMO systems

The impact of array geometry on enhancing channel hardening in mm-wave massive MIMO systems is investigated. As the number of physical radio links increases, small-scale fading is averaged out, leading to a hardened channel and increased communication capacity. A comprehensive analysis is conducted using the physical ray distribution model (3GPP) to investigate the impact of various array geometries on channel hardening. A clear insight is obtained through the derivation of a closed-form relation, providing an understanding of the corresponding effect. Additionally, a novel measure called Array Spread is introduced, offering a practical tool for designing array geometry that minimizes channel variations in every specific physical scenarios. The findings of this study contribute to the overall understanding and optimization of MIMO communication systems, particularly in the context of massive MIMO deployments in mm-wave frequencies.

The supply chain of Javan spitting cobra (Naja sputatrix) utilization in cobra satay businesses in Jakarta, Indonesia

Abstract. Utama SE, Santosa Y, Masyud B. 2024. The supply chain of Javan spitting cobra (Naja sputatrix) utilization in cobra satay businesses in Jakarta, Indonesia. Biodiversitas 25: 2086-2092. One frequently utilized reptile is the Javan spitting cobra (Naja sputatrix). Apart from Indonesia, countries that utilize cobras are China and India. One form of actual utilization for the community's economy from cobra utilization is the cobra satay business. The high economic potential threatens the cobra population in the wild. Moreover, trade in the supply chain can have a negative impact ecologically and economically, so it is necessary to approach the supply chain of the cobra business to understand the cobra supply chain, considering the roles of participants, distribution processes, the number and specifications of cobras utilized, the source of cobras, and the incurred losses within the supply chain. This research was conducted using the snowball sampling method, with in-depth interviews. Interviews with vendor were conducted at their shops in Jakarta, and interviews with agent and collector were conducted at their homes in Karawang. It enabled the identification of the supply chain of the cobra satay business consisting of vendor in Jakarta and collectors, agents, and hunters in Karawang. The data collected were the number, specifications, and prices of each supply chain link, as well as the form of cobra utilization from traders. An estimated ±1,560 cobras are used in the cobra satay business annually, with a minimum total length of 70 cm for the cobras. The results showed that the percentage of dead cobras in the supply chain was 5%-35%, which resulted in a reduction in income of Rp25,000 - Rp125,000 for agents, Rp40,000 - Rp240,000 for collector, and Rp200,000 - Rp2,000,000 for vendor.

Link-limited bypass rewiring for enhancing the robustness of complex networks

Real-world networks often encounter disruptions that can have profound societal impacts. Therefore, enhancing network robustness against random failures and targeted attacks is crucial. Bypass rewiring, in which adjacent nodes are immediately reconnected following node removal, has been proposed as a promising method for enhancing network robustness. However, this method typically requires numerous bypass links and incurs significant costs. In this paper, we propose link-limited bypass rewiring, in which bypass links are reconnected stochastically. Additionally, we investigate the relationship between the number of bypass links and robustness improvement. Our findings, which are based on analytical and numerical results, reveal a tradeoff relationship between the number of added bypass links and robustness enhancement. Notably, preferentially reconnecting high-degree nodes was found to be effective for improving robustness. These insights can inform the design of resilient systems in real-world networks, offering strategies for enhancing robustness against node removal.

Robustness of Real-World Networks after Weight Thresholding with Strong Link Removal

Weight thresholding (WT) is a method intended to decrease the number of links within weighted networks that may otherwise be excessively dense for network science applications. WT aims to remove links to simplify the network by holding most of the features of the original network. Here, we test the robustness and the efficacy of the node attack strategies on real-world networks subjected to WT that remove links of higher weight (strong links). We measure the network robustness along node removal with the largest connected component (LCC). We find that the real-world networks under study are generally robust when subjected to WT. Nonetheless, WT with strong link removal changes the efficacy of the attack strategies and the rank of node centralities. Also, WT with strong link removal may trigger a more significant change in the node centrality rank than WT by removing weak links. Network science research with the aim to find important/influential nodes in the network has to consider that simplifying the network with WT methodologies may change the node centrality.

Total losses and dispersion effects management and upgrading fiber reach in ultra-high optical transmission system based on hybrid amplification system

Abstract This work demonstrated the total losses and dispersion effects management and upgrading the fiber reach in ultra-high optical transmission system based on hybrid amplification system. Based on the requirements of the system, a number of trade-offs must be made between the numerous interconnected performance characteristics of each component in the design of a high quality transmission link. The transmission distance is the greatest distance that an optical signal has to travel. It affects the choice of optical component, fiber type, and signal amplification techniques. Fiber amplifiers or signal regeneration may be required to compensate for signal loss over greater distances. Reviewing these crucial aspects helps when selecting the best optical components and system layout. This ensures the optical link performs as expected. Hybrid amplification system unit (EDFA/Raman/EDFA), (EDFA/Raman), (BFA/PDFA) and (Raman/EDFA) is employed in the transmission system to strength the signal and upgrade the system performance operation efficiency. Space division multiplexing scheme is also employed with the propagation scheme in order to upgrade and control of the total losses and dispersion effects. The optimum total losses/dispersion/repeater spacing are studied with the variations of the total number of links per fiber core channel.

Testing structural balance theories in heterogeneous signed networks

The abundance of data about social relationships allows the human behavior to be analyzed as any other natural phenomenon. Here we focus on balance theory, stating that social actors tend to avoid establishing cycles with an odd number of negative links. This statement, however, can be supported only after a comparison with a benchmark. Since the existing ones disregard actors’ heterogeneity, we extend Exponential Random Graphs to signed networks with both global and local constraints and employ them to assess the significance of empirical unbalanced patterns. We find that the nature of balance crucially depends on the null model: while homogeneous benchmarks favor the weak balance theory, according to which only triangles with one negative link should be under-represented, heterogeneous benchmarks favor the strong balance theory, according to which also triangles with all negative links should be under-represented. Biological networks, instead, display strong frustration under any benchmark, confirming that structural balance inherently characterizes social networks.

Abstract 2147: Impact Of Donor Age On Microvasculature Self-assembly Of Induced Pluripotent Stem Cell Derived Endothelial Progenitors

Introduction: Current vascular tissue-engineering using induced pluripotent stem cells (iPSCs) predominantly relies on somatic cells obtained from newborn or fetal donors. However, iPSCs sourced from aged individuals exhibit epigenetic differences in angiogenic genes that may hinder their utility in iPSC derived vasculature. We hypothesized that, due to these differences, iPSCs from aged donors will exhibit decreased vasculogenic potential. Methods: We differentiated iPSCs into endothelial progenitors (iEPs) and encapsulated them in an interpenetrating polymer network hydrogel containing norbornene-functionalized hyaluronic acid and Collagen I at cell densities and hydrogel stiffness optimal for vasculogenesis. Seven days after encapsulation, the hydrogels were fixed, and the resulting microvasculature imaged. We then utilized a previously developed computational pipeline to quantify several vasculogenic properties. Results: We derived iEPs from four separate iPSC lines: 2 sourced from neonatal donors (ND) and 2 from mature donors (MD). Irrespective of donor age, differentiation yielded ~15% (n=4) iEPs of the total cell population. The two ND lines developed a similar plexus, whereas this structure was diminished in one adult line and absent in the second. Between iEPs matched for sex and somatic cell origin, there is a 50% decrease in vessel volume fraction and number of links between vessels, a 66% reduction in the percentage of vessels connected to the largest vessel, and a 43% reduction in branch points between vessels ( Fig. 1 ). Conclusion: Overall, these data suggest MD iEPs are less likely to form dense interconnected microvasculature as compared to ND iEPs. Interestingly, the average diameter did not change across all cell lines. This suggests that while MD iEPs exhibited cell clustering, they were less likely to branch out and interconnect with other clusters of iPSC-EPs to form interconnected networks

Vibro-acoustic coupling analysis of dynamic performance of the double panel system with mechanical links

The theoretical prediction model for the dynamic performance of a double panel system that takes into account the effect of mechanical links is developed in this paper. The double panel system is established considering both the vibro-acoustic coupling and the effect of the mechanical links between two flexible panels. Firstly, the modal characteristics of the double panel system are analyzed and compared with the results calculated by the finite element method. The accuracy and efficiency of the proposed model have been validated. Subsequently, the forced response of the double panel system under different external excitations is studied. It is shown that the mechanical link resulted in less response level of the double panel system in in low frequency range due to the structural path in the energy transmission of the double panel system. Finally, the effect of the mechanical link parameters on the dynamic performance of the double panel system is discussed. The results reveal that the stiffness coefficients, location distributions, and number of mechanical links can significantly influence the dynamic behavior of the double panel system. Therefore, the theoretical model can be used in optimization techniques to improve the sound transmission characteristics of the double panel system.

Custom-made tubular Tesla valves for laboratory lessons in classroom

The article proposes new, easy-to-manufacture multivariate tubular Tesla valves that can be used in the educational process at universities and colleges. Detailed instructions for making Tesla valves from available materials using simple tools are provided. The advantages of the proposed Tesla valves over the conventional ones, described in the literature, such as manufacture ease, availability of materials and accessories, mechanical flexibility, and the ability to quickly change the number of links and change the link connection pattern, are indicated. A simple method is proposed for conducting hydrodynamic studies of liquid flow from a vessel through Tesla valves, based on measuring the water level dynamics in the vessel and, in particular, measuring the vessel's complete emptying time. Some simple experiments to demonstrate the diodicity of the valves are carried out. The repeatability and measurement errors are assessed. Parallel and series connections of valve sections are studied. The prospects for Tesla valves' use in various fields of science and technology are considered. The advantages of the proposed valves for laboratory teaching experiments in the classroom are indicated.

Comparison of two statistical measures of complexity applied to ecological bipartite networks

Networks are a convenient way to represent many interactions among different entities as they provide an efficient and clear methodology to evaluate and organize relevant data. While there are many features for characterizing networks, a quantity seems rather elusive: Complexity. The quantification of the complexity of networks is nowadays a fundamental problem. Here, we present a novel tool for identifying the complexity of ecological networks. We compare the behavior of two relevant indices of complexity: K-complexity and Single Value Decomposition (SVD) entropy. For that, we use real data and two types of null models. Both null models consist of randomized networks built by swapping a controlled number of links of the original ones. We analyze 23 plant–pollinator and 19 host-parasite networks as case studies. Our results show that (a) it is necessary to calculate, not only the original network K-complexity and SVD entropy but also to calculate the corresponding indices of the randomized networks (b) the density and degree distribution are essential in the characterization of a network and the randomized networks are a suitable tool to detect the network complexity, and (c) plant–pollinator networks are more complex than host-parasite networks. We found that, for the first null model, K-complexity and SVD did not change with link swapping in both pollinator-plant and host-parasite networks. For the second null model, K-complexity for pollinator-plant networks generally decreased with an increasing number of links swapped (i.e. negative slope), showing that plant–pollinator networks lose complexity with increasing link swapping. In contrast, there was a positive slope between K-complexity and link swapping for host-parasite networks, showing that these networks are less complex than plant–pollinator networks. For both types of networks, in general, the slope between K-complexity and the number of links swapped became more positive with network density. Overall, SVD entropy was less responsive to link swapping. Our analyses show that although SVD entropy has been widely used to characterize network complexity, K-complexity is a more reliable tool. Additionally, they show that degree distribution and density are important drivers of network complexity and should be accounted for in future studies.

Monitoring carbohydrate 3D structure quality with the Privateer database.

The remediation of the carbohydrate data of the Protein Data Bank (PDB) has brought numerous enhancements to the findability and interpretability of deposited glycan structures, yet crucial quality indicators are either missing or hard to find on the PDB pages. Without a way to access wider glycochemical context, problematic structures may be taken as fact by keen but inexperienced scientists. The Privateer software is a validation and analysis tool that provides access to a number of metrics and links to external experimental resources, allowing users to evaluate structures using carbohydrate-specific methods. Here, we present the Privateer database, a free resource that aims to complement the growing glycan content of the PDB.

A Machine Learning Model for Identifying Sexual Health Influencers to Promote the Secondary Distribution of HIV Self-Testing Among Gay, Bisexual, and Other Men Who Have Sex With Men in China: Quasi-Experimental Study.

Sexual health influencers (SHIs) are individuals actively sharing sexual health information with their peers, and they play an important role in promoting HIV care services, including the secondary distribution of HIV self-testing (SD-HIVST). Previous studies used a 6-item empirical leadership scale to identify SHIs. However, this approach may be biased as it does not consider individuals' social networks. This study used a quasi-experimental study design to evaluate how well a newly developed machine learning (ML) model identifies SHIs in promoting SD-HIVST compared to SHIs identified by a scale whose validity had been tested before. We recruited participants from BlueD, the largest social networking app for gay men in China. Based on their responses to the baseline survey, the ML model and scale were used to identify SHIs, respectively. This study consisted of 2 rounds, differing in the upper limit of the number of HIVST kits and peer-referral links that SHIs could order and distribute (first round ≤5 and second round ≤10). Consented SHIs could order multiple HIV self-testing (HIVST) kits and generate personalized peer-referral links through a web-based platform managed by a partnered gay-friendly community-based organization. SHIs were encouraged to share additional kits and peer-referral links with their social contacts (defined as "alters"). SHIs would receive US $3 incentives when their corresponding alters uploaded valid photographic testing results to the same platform. Our primary outcomes included (1) the number of alters who conducted HIVST in each group and (2) the number of newly tested alters who conducted HIVST in each. We used negative binomial regression to examine group differences during the first round (February-June 2021), the second round (June-November 2021), and the combined first and second rounds, respectively. In January 2021, a total of 1828 men who have sex with men (MSM) completed the survey. Overall, 393 SHIs (scale=195 and ML model=198) agreed to participate in SD-HIVST. Among them, 229 SHIs (scale=116 and ML model=113) ordered HIVST on the web. Compared with the scale group, SHIs in the ML model group motivated more alters to conduct HIVST (mean difference [MD] 0.88, 95% CI 0.02-2.22; adjusted incidence risk ratio [aIRR] 1.77, 95% CI 1.07-2.95) when we combined the first and second rounds. Although the mean number of newly tested alters was slightly higher in the ML model group than in the scale group, the group difference was insignificant (MD 0.35, 95% CI -0.17 to -0.99; aIRR 1.49, 95% CI 0.74-3.02). Among Chinese MSM, SHIs identified by the ML model can motivate more individuals to conduct HIVST than those identified by the scale. Future research can focus on how to adapt the ML model to encourage newly tested individuals to conduct HIVST. Chinese Clinical Trials Registry ChiCTR2000039632; https://www.chictr.org.cn/showprojEN.html?proj=63068. RR2-10.1186/s12889-021-11817-2.