Free Network Research Articles

Exploring phytoconstituent for confronting the symptoms of polycystic ovarian syndrome: molecular dynamics simulation, quantum studies, free energy calculations and network analysis approaches

Women of the current citizenry are majorly afflicted with gonadal hormonal fluctuations and stress, which play a predominant role in the occurrence and prevalence of PCOS. Synthetic medications under use influence in varietal adverse reactions, hence incorporation of herbs remains a critical part of the therapy. The present study includes selection of phytoconstituents from herbs related to uterine activity and subjection to in silico approach. The constituents were incorporated into a flow of molecular docking, ADMET analysis, toxicity, biological activity prediction, DFT studies, Molecular dynamics, free energy calculations, and network analysis. Three proteins namely Human androgen receptor (2AM9), Human progesterone (1E3K), and Estrogen receptor (1X7R) were selected and used throughout the study. From docking studies, Sarsasapogenin (SAR), β-sitosterol (BES), and Stigmasterol (STI) showed good binding energy interactions with all three proteins, and they also possess ideal drug-likeliness properties with acceptable toxicity profiles. Further analysis by Density functional theory proves they possess considerable intermolecular charge transfer. The insights from Molecular dynamics studies and free energy calculation explain, that Sarsasapogenin (SAR) among the three has strong stability and intermolecular interactions with the findings through network analysis justifying the selection of three proteins. Hence, on the compilation of results, Sarsasapogenin from Asparagus racemosus is determined to be potent and active with all three receptors together that majorly influence in the pathophysiology of the disorder. Further experimental evaluation of SAR in pre-clinical and clinical models would help emphasize the biological activity of the constituent in the management of PCOS.

Konnektor: A Framework for Using Graph Theory to Plan Networks for Free Energy Calculations.

Alchemical free energy campaigns can be planned using graph theory by building networks that contain nodes representing molecules that are connected by possible transformations as edges. We introduce Konnektor, an open-source Python package, for systematically planning, modifying, and analyzing free energy calculation networks. Konnektor is designed to aid in the drug discovery process by enabling users to easily setup free energy campaigns using complex graph manipulation methods. The package contains functions for network operations including concatenation of networks, deletion of transformations, and clustering of molecules along with a framework for combining these tools with existing network generation algorithms to enable the development of more complex methods for network generation. A comparison of the various network layout features offered is carried out using toy data sets. Additionally, Konnektor contains visualization and analysis tools, making the investigation of network features much simpler. Besides the content of the package, the paper also offers application examples, demonstrating how Konnektor can be used and how the different networks perform from a graph theory perspective. Konnektor is freely available via GitHub at https://github.com/OpenFreeEnergy/konnektor under the permissive MIT License.

Quality improvement approach to reduce patient cycle time at a student-run free healthcare clinical network

BackgroundThe University of Florida (UF) Equal Access Clinic Network (EACN) is the largest student-run free healthcare clinic network in Florida. The UF EACN serves those who are underinsured or uninsured...

Distribution of centrality measures on undirected random networks via the cavity method

The Katz centrality of a node in a complex network is a measure of the node's importance as far as the flow of information across the network is concerned. For ensembles of locally tree-like undirected random graphs, this observable is a random variable. Its full probability distribution is of interest but difficult to handle analytically because of its "global" character and its definition in terms of a matrix inverse. Leveraging a fast Gaussian Belief Propagation-Cavity algorithm to solve linear systems on tree-like structures, we show that i) the Katz centrality of a single instance can be computed recursively in a very fast way, and ii) the probability [Formula: see text] that a random node in the ensemble of undirected random graphs has centrality [Formula: see text] satisfies a set of recursive distributional equations, which can be analytically characterized and efficiently solved using a population dynamics algorithm. We test our solution on ensembles of Erdős-Rényi and Scale Free networks in the locally tree-like regime, with excellent agreement. The analytical distribution of centrality for the configuration model conditioned on the degree of each node can be employed as a benchmark to identify nodes of empirical networks with over- and underexpressed centrality relative to a null baseline. We also provide an approximate formula based on a rank-[Formula: see text] projection that works well if the network is not too sparse, and we argue that an extension of our method could be efficiently extended to tackle analytical distributions of other centrality measures such as PageRank for directed networks in a transparent and user-friendly way.

Energy criterion for synchronization of neuron populations

Energy regulates the neuron activities, including inter-neuronal communication, biorhythm, and brain function. In this paper, we propose the energy criterion for the synchronization of neuron populations, and neurons tend to be synchronous if the energy variance approaches zero. Furthermore, we investigate the energy distribution of neuron population. The individual energy groups, two or three groups, expand until they merge into a uniform energy group with the increasing of the coupling intensity. Then, the uniform energy group tends to be a Normal distribution when the neuron population is synchronized. The energy criterion and energy distribution for synchronization are confirmed in the chain network, small world network and the scale free network composed by the FithzHugh-Nagumo (FHN) neurons. These results advance the understanding of the role played by the energy in neuron populations.

A Novel Adaptive Random Access Window (RAW) Slot Adjustment Model for IoT Network Using an Integrated Scheme

The RAW (Restricted Access Window) component of the IoT network is deployed to reduce traffic and channel contention in dense and heterogeneous sensor network environment. It divides sensor nodes into groups and slots, allowing channel access only to one RAW slot at a time. Several algorithms and improved channel utilization optimization models have been proposed to optimize the RAW parameters, to ensure a contention free network or at least, minimally reduce it. These techniques often rely on previous traffic demands schedules, collision analysis and send/receive matrices to accurately predict the future of stations’ interactions in an IoT environment. Thus systematically adjusting its operations to reduce contention among the stations and the Access Point(AP), thereby ensuring a flexible transmission even in a dense network environment. This paper critically investigated, analysed and proposed a novel approach to RAW size adjustment. The new approach will invoke data mining instruments and optimisation algorithms to improve performance of stations in an IoT environment and thereafter simulated (with respect to some specified QoS parameters) to ascertain it’s (output) performance index with the already existing models

N-Alkanes formed by methyl-methylene addition as a source of meteoritic aliphatics

Aliphatics prevail in asteroids, comets, meteorites and other bodies in our solar system. They are also found in the interstellar and circumstellar media both in gas-phase and in dust grains. Among aliphatics, linear alkanes (n-CnH2n+2) are known to survive in carbonaceous chondrites in hundreds to thousands of parts per billion, encompassing sequences from CH4 to n-C31H64. Despite being systematically detected, the mechanism responsible for their formation in meteorites has yet to be identified. Based on advanced laboratory astrochemistry simulations, we propose a gas-phase synthesis mechanism for n-alkanes starting from carbon and hydrogen under conditions of temperature and pressure that mimic those found in carbon-rich circumstellar envelopes. We characterize the analogs generated in a customized sputter gas aggregation source using a combination of atomically precise scanning tunneling microscopy, non-contact atomic force microscopy and ex-situ gas chromatography-mass spectrometry. Within the formed carbon nanostructures, we identify the presence of n-alkanes with sizes ranging from n-C8H18 to n-C32H66. Ab-initio calculations of formation free energies, kinetic barriers, and kinetic chemical network modelling lead us to propose a gas-phase growth mechanism for the formation of large n-alkanes based on methyl-methylene addition (MMA). In this process, methylene serves as both a reagent and a catalyst for carbon chain growth. Our study provides evidence of an aliphatic gas-phase synthesis mechanism around evolved stars and provides a potential explanation for its presence in interstellar dust and meteorites.

An application of complex networks on predicting the behavior of infectious disease on campus

Networks are widely used in understanding the spread of infectious disease. Universities and colleges are characterized by frequent social gatherings and extensive interpersonal communication, making them ideal applications of complex networks. In this paper, a compound model consisting of a homogeneous (small world) network and a heterogeneous (scale free) network is established, the spreading characteristics of epidemics are analyzed by using the mean‐field (MF) and the heterogeneous mean‐field (HMF) approaches, and the effects of various factors such as the total node number (N), the randomly reconnection probability (p), the initial proportion of infected node (p0), the average degree (k), and the shared nodes (s) are simulated by numerical calculations. The simulation results are in consistent with theoretical predictions, indicating that the randomness effect decreases with increasing k and N and is small when k (k = 8) and N (N = 200) are relatively large; the randomness effect is more significant in the scale free (SF) network than in the small world (SW) network, and the disease spread faster in the SF network; shared nodes can weakly increase the disease spread, but when the shared nodes are involved in an infected and an uninfected network, it can induce the disease outbreak in the uninfected network.

Fundamentals and recent developments of free-space optical neural networks

Machine learning with artificial neural networks has recently transformed many scientific fields by introducing new data analysis and information processing techniques. Despite these advancements, efficient implementation of machine learning on conventional computers remains challenging due to speed and power constraints. Optical computing schemes have quickly emerged as the leading candidate for replacing their electronic counterparts as the backbone for artificial neural networks. Some early integrated photonic neural network (IPNN) techniques have already been fast-tracked to industrial technologies. This review article focuses on the next generation of optical neural networks (ONNs), which can perform machine learning algorithms directly in free space. We have aptly named this class of neural network model the free space optical neural network (FSONN). We systematically compare FSONNs, IPNNs, and the traditional machine learning models with regard to their fundamental principles, forward propagation model, and training process. We survey several broad classes of FSONNs and categorize them based on the technology used in their hidden layers. These technologies include 3D printed layers, dielectric and plasmonic metasurface layers, and spatial light modulators. Finally, we summarize the current state of FSONN research and provide a roadmap for its future development.

A stable and robust fault diagnosis method for bearing using lightweight batch normalization-free residual network

Abstract The conventional deep learning-based bearing fault diagnosis methods tend to utilize denoising modules to improve the fault diagnosis performance in noisy scenes. However, the addition of denoising modules will increase expensive computational costs, leading to a delayed acquisition of fault diagnosis results. This work proposed a lightweight batch normalization (BN)-free residual network without any denoising modules for bearing fault diagnosis which properly rescaled the weights in a standard initialization instead of BN to avoid the exploding gradient problem and vanishing gradient problem at the beginning of training for deep neural networks. Therefore, it prevents the undesirable properties caused by BN. Compared with other methods, the fault diagnosis performance of the proposed method can maintain a high level with different input sizes and batch sizes. Especially in noisy scenes, the testing accuracy of fault diagnosis on different bearing datasets can be improved by 13.54% and 7.74% using fewer parameters and floating point operations on different bearing datasets.

High-accuracy height differences using a pressure sensor for ground control points measurement in underwater photogrammetry

Abstract. Three-dimensional reference points (RPs) are fundamental for datum definition and metric validation in many photogrammetric applications, often used as ground control points (GCPs) to constrain the bundle adjustment solution. Nevertheless, survey operations underwater present challenges due to the physical characteristics of the water itself and the technological limitations of available instruments. Traditional methods to collect RPs underwater rely on direct geodetic measurements like slope distances, height differences, and depths from a dive computer. These methods can be time-consuming and impractical to scale up to large areas, particularly in deeper waters. This paper reports on the use of a custom-developed low-cost pressure sensor to measure depths and height differences of underwater RPs with survey-grade accuracy. Laboratory and open water tests demonstrated the method's potential, achieving an RMSEZ of less than 1 mm over a 1.5 m height range in the laboratory in static water and a sub-centimetre RMSE of relative depth differences in shallow water tests carried out in two different locations at sea with maximum significant wave height of 9 cm. The sensor proved its effectiveness also for constraining a corridor-like underwater photogrammetric survey reducing the bending of the 3D model with respect to the free network solution (RMSEZ lowered from 10 cm to less than 1 cm). The preliminary tests with the presented approach proved several advantages against other consolidated methods, including cost reduction (compared to commercial survey instruments), rapidity, safety, and accuracy, especially at depths greater than 3–5 m where other approaches (e.g., GNSS or topographic measures) cannot be applied.

Physics-informed neural networks for fully non-linear free surface wave propagation

This study proposed fully nonlinear free surface physics-informed neural networks (FNFS-PINNs), an advancement within the framework of PINNs, to tackle wave propagation in fully nonlinear potential flows with the free surface. Utilizing the nonlinear fitting capabilities of neural networks, FNFS-PINNs offer an approach to addressing the complexities of modeling nonlinear free surface flows, broadening the scope for applying PINNs to various wave propagation scenarios. The improved quasi-σ coordinate transformation and dimensionless formulation of the basic equations are adopted to transform the time-dependent computational domain into the stationary one and align variable scale changes across different dimensions, respectively. These innovations, alongside a specialized network structure and a two-stage optimization process, enhance the mathematical formulation of nonlinear water waves and solvability of the model. FNFS-PINNs are evaluated through three scenarios: solitary wave propagation featuring nonlinearity, regular wave propagation under high dispersion, and an inverse problem of nonlinear free surface flow focusing on the back-calculation of an initial state from its later state. These tests demonstrate the capability of FNFS-PINNs to compute the propagation of solitary and regular waves in the vertical two-dimensional scenarios. While focusing on two-dimensional wave propagation, this study lays the groundwork for extending FNFS-PINNs to other free surface flows and highlights their potential in solving inverse problems.

Development of Membrane Permeability Coefficient by Means of Novel Molecular Dynamics Methods

The membrane permeability, and its evaluation, is crucial factor in the process of uptake of compounds from outside to inside the cell and in the inhibition of the activity of disease-causing target proteins. Although molecular dynamics (MD) simulations have been shown to be able to reproduce the conformational changes of compounds occurring during membrane permeation, it is still challenging to extract the membrane permeability at an affordable computational workload solely by conventional MD. Indeed, the time scale accessible by MD is far below the one characterizing the actual permeation process. Phenomena occurring in living organisms escaping the reach of standard MD are generally referred to as biological rare events, and the membrane permeation process is one of them. To overcome this time-scale problem, several enhanced sampling methods have been proposed over the years to improve conformational sampling. In this review, a hybrid sampling method that combines the parallel cascade selection MD (PaCS-MD) and the outlier flooding method (OFLOOD), introduced and developed by our group, is proposed as a tool to study the membrane permeation from structural sampling (rare-event sampling). The obtained trajectories are used to estimate the free energy profiles for the membrane permeation and to compute the membrane permeation coefficients. Moreover, we present an example of application of the free energy reaction network method as a versatile way for incorporating explicitly into reaction coordinates the degrees of freedom related to internal motion.

Auto Encoder Driven Hybrid Pipelines for Image Deblurring using NAFNET

The project introduces an innovative solution to the persistent challenge of image blurring in the realm of Computer Vision. Leveraging the synergies between auto-encoder structures and Non-Linear Activation Free Networks (NAFNET), the proposed methodology aims to achieve superior image restoration results by effectively addressing diverse types of blur. This approach offers a holistic solution that combines the strengths of traditional methods and state-of-the-art deep learning techniques. Quantitative evaluation using metrics demonstrates the efficacy of the proposed methodology in achieving superior deblurring results compared to existing techniques. By pushing the boundaries alongside of image deblurring capabilities, the project contributes to the advancement of the field and holds promise for applications across various domains, including photography, medical imaging, and surveillance.

Reflection modulation generated by scale-free network resonator periodic structure

The pattern of resonator in a periodical array is decisive for its response under impinging electromagnetic wave. A scale-free network resonator is proposed by connecting sub-units in the form of scale free network. Scale-free network resonator array’s reflectivity near resonant frequency could be modulated at arbitrary value between zero and unity, which is different from known resonator array or frequency selective surface. Reflection amplitude modulation could generate rich behaviors, as effective medium property in wide band could be maintained near zero constant, which means zero phase shift and is favorable in radar radome structure. S-parameter analysis indicates amplitude modulation is owing to polarization conversion generated by network links. Scale-free network resonator array has application potentials in future radar radomes, electromagnetic wave lens and microwave antenna panels.

Quantum aided efficient resource control for connected support in IRS assisted networks

Context:To achieve the vision of all connected world with uninterrupted communication support, 6G technology plays an important role. But the scarce radio spectrum and limited network resources is the main challenge in delivering its promised performance. Objectives:This paper presents an IRS-aided cell free NOMA network model that aims to provide uniform network coverage. The future 6G technology envisions for serving billions of interconnected devices with seamless communication support, data handling capabilities and computational accuracy. But the scarcity of network resources is the main limitation. Thus, the need is to design quantum enabled intelligent and dynamic networks capable of offering extended network capabilities. Proposed work is on intelligent network framework that provides uniform network coverage through efficient resource management for a 6G enabled expanded IoT network. Methods:To enable efficient resource management, a quantum enabled resource control algorithm is proposed that creates user clusters and associates each AP-IRS pair to each cluster. Each AP transmits the superimposed signals of its intended cluster against all the user clusters as in conventional NOMA system. The nodes in each cluster have been assigned unique pilots so as to avoid intracluster interference. The use of IRS enables desired NOMA beamforming such that the effect of unfavourable wireless environment is mitigated. Results:The performance of the IRS-aided cell-free NOMA network is evaluated for average sum rate with different AP transmit power, cluster sizes, IRS reflecting elements and IRS phase shifts. It is shown that at transmit power per AP of 30dBm, the average sum rate of the system improves by 7.52% with the proposed algorithm using equal power allocation scheme. Further, the comparative performance analysis of three different communication systems is carried out to validate the proposed communication model. Conclusion:It is observed that with more number of users per cluster, the average sum rate of the system initially increases for small cluster sizes and then it becomes constant for large cluster sizes. The proposed clustering method outperforms the random clustering approach achieving sum rate of 12.9 bits/s/Hz with N =300 and M= 8. The comparison of different communication scenarios reveals that the maximum sum rate of 12.2 bits/s/Hz is achieved with the proposed model incorporating proposed clustering mechanism. Further, the energy efficiency analysis suggests that energy efficiency improves with N and Pc with proposed clustering approach. The use case scenarios for the integration of quantum computing with IRS technology are also presented.

Поиск глобального минимума целевой функции для определения псевдообратной матрицы поисковым методом при уравнивании свободных геодезических сетей

When evaluating the accuracy of equalized free geodetic networks based on the search method of nonlinear programming, a degenerate matrix of unknowns’ normal equations coefficients is formed. It must be pseudoinverse in order to calculate the inverse weight one of the equalized parameters. Performing a mathematical procedure for calculating a pseudoinverse matrix based on the mentioned technology of nonlinear programming at equalizing free geodetic constructions, a situation may arise when the minimization step is incorrectly selected or the approximate values in the desired array of numbers are set roughly. It can lead to finding a local, not a global minimum, as well as to cycling the algorithm. The authors provide the conclusion of the formula and substantiate the conditions on which the elements of the required matrix change, so that the value of the objective function either comes as close as possible to the global minimum, or indicates that it is impossible to achieve it. The correctness of the proposed conditions and the derived formula are confirmed by a test example

A new community business model for a free, open, and neutral network: Considering the wireless to fiber transition

Free, open, and neutral network (FONN) is an alternative to the prevailing proprietary ISP networks on the Internet. From the FONN manifesto, we conceive two main goals: to give freedom to users and to cooperate in building and maintaining the FONN infrastructure. However, the manifesto avoids talking about the business model of this infrastructure and neglects to give the appropriate role to each member of the community in building and maintaining this infrastructure. Moreover, current research misplaces FONN’s cooperation in the avoidance of the tragedy of the commons and also overlooks ownership. The move from wireless to fiber has amplified the consequences of not considering ownership or member roles. In order to fill the previous gaps, in this article we will first relate the objectives of this manifesto to the field of free software. Next we will argue that FONN’s cooperation should be placed in the avoidance of the tragedy of the anticommons. Inspired by free software we define a new business model for FONN besides discussing different ownership licenses. Furthermore, we state the roles of members and rigorously model the economic flow of construction and maintenance costs. The main implication of the proposed business model is that future FONNs will avoid the economic and organizational collapse of some current FONN communities, mainly because funding cannot be based solely on crowdfunding or volunteering. Yet, as an overall benefit, FONN avoids the duplication of infrastructure. This new business model has been used successfully in the fiber deployment of a FONN section.

FSMN-Net: a free space matching network based on manifold convolution for optical molecular tomography.

Optical molecular tomography (OMT) can monitor glioblastomas in small animals non-invasively. Although deep learning (DL) methods have made remarkable achievements in this field, improving its generalization against diverse reconstruction systems remains a formidable challenge. In this Letter, a free space matching network (FSMN-Net) was presented to overcome the parameter mismatch problem in different reconstruction systems. Specifically, a novel, to the best of our knowledge, manifold convolution operator was designed by considering the mathematical model of OMT as a space matching process. Based on the dynamic domain expansion concept, an end-to-end fully convolutional codec further integrates this operator to realize robust reconstruction with voxel-level accuracy. The results of numerical simulations and in vivo experiments demonstrate that the FSMN-Net can stably generate high-resolution reconstruction volumetric images under different reconstruction systems.

서울시 공공 WIFI를 대상으로 개인정보취득이 불가능한 무료통신망의 품질 향상 방안

서울시 공공 WIFI를 대상으로 개인정보취득이 불가능한 무료통신망의 품질 향상 방안