Graph Approach Research Articles

A conditional random field framework for language process in product review mining

The Part-Of-Speech tagging is widely used in the natural language process. There are many statistical approaches in this area. The most popular one is Hidden Markov Model. In this paper, an alternative approach, linear-chain Conditional Random Fields, is introduced. The Conditional Random Fields is a factor graph approach that can naturally incorporate arbitrary, non-independent features of the input without conditional independence among the features or distributional assumptions of inputs. This paper applied the Conditional Random Fields for the car review word Part-Of-Speech tagging and then the feature extraction, which can be used as an input to an opinion mining system. To reduce the computational time, we also proposed applying the Limited-memory BFGS algorithm to train the Conditional Random Fields. Furthermore, this paper evaluated the Conditional Random Fields and the classical graph approach using the car review dataset to demonstrate that the Conditional Random Fields have a more robust result with a smaller training dataset.

Towards Ecological Management and Sustainable Urban Planning in Seoul, South Korea: Mapping Wild Pollinator Habitat Preferences and Corridors Using Citizen Science Data.

Simple SummaryUrban landscapes, though a primary contributor to habitat fragmentation, have the potential to facilitate habitat connectivity for native pollinator species, especially with strategic green space planning. Citizen science data have been proven to be useful to address conservation issues in urban areas, especially where knowledge is lacking about species richness and habitat preferences. In this study, we used data collected by a citizen science program between 2016 and 2018 to determine which families of pollinators were the most frequently observed in Seoul, with which habitats pollinators had the highest affinities, and what parts of the city facilitated habitat connectivity. We found that the most suitable habitats for multiple families were located in public parks, university campuses, and Cultural Heritage sites. These results are directly applicable in order to better understand urban planning stakes for pollinators and wildlife in general and provide avenues for improvement to recreate functional greenways in a dense city like Seoul.The preservation and restoration of habitats and ecological connectivity inside cities is crucial to ensure wildlife can find suitable areas to forage, rest and reproduce, as well as to disperse, thereby allowing metapopulation functioning. In this study, we used data collected by a citizen science program between 2016 and 2018 to determine which families of pollinators were the most frequently observed in Seoul and with which habitats pollinators had the highest affinities. Using species distribution modeling and landscape graph approaches, we located the main habitats and corridors to reinforce connectivity for six pollinator families. Finally, we identified habitats and corridors where conservation actions should be prioritized. In total, 178 species belonging to 128 genera and 60 families were observed. Hymenopterans were the most recorded, followed by dipterans and lepidopterans. The most suitable habitats for pollinators were constituted of public parks, university campuses, and Cultural Heritage sites. In a dense city like Seoul, most of the conservation corridors are located in built-up areas. Innovative urban planning and architecture are therefore required as well as the setting-up of ecological management practices to lead to a more sustainable urbanism for pollinators and wildlife in general.

Correlation Analysis Model of Social Capital and Innovation Performance Based on Knowledge Mapping.

In this paper, the correlation between social capital and innovation performance is analyzed using a knowledge graph approach, and a correlation analysis model is designed for practical use. Given the advantages of knowledge graph technology and the fact that most recommendation models fail to make full use of the correlation between knowledge graph and recommended items, this paper proposes a cross-attention fusion-based knowledge graph recommendation algorithm (CAKR). The CAKR model contains a cross-fusion module, a recommendation task module, and a knowledge graph embedding task module, and the inputs of the two task modules are alternately learned in a low-dimensional space by the cross-fusion module. The input of the two task modules is alternately learned in the low-dimensional space by the cross-fusion module to interact with the two embedding vectors of items and entities, and then, the obtained feature vectors are fed into the relevant task modules, respectively, and then, the results are calculated by the respective prediction functions. There is a mediating role of knowledge management capabilities in the innovation ecology perspective between social capital and innovation performance of SMEs. Among them, it fully mediates the relationship dimension of technical social capital, the cognitive dimension of institutional social capital, the relationship and cognitive dimension of market-based social capital, and innovation performance, and partially mediates the structural and cognitive dimension of technical social capital, the structural and relationship dimension of institutional social capital, and innovation performance. The relationship between knowledge management capability and innovation performance is positively mediated by environmental dynamics in the innovation ecology perspective, and the positive effect of knowledge management capability on innovation performance is more significant when the environmental dynamics are stronger, and vice versa. Finally, based on the results of the empirical study, some management suggestions are provided on how SMEs can reasonably utilize social capital to enhance their innovation performance.

Modeling and Simulation of a UV Water Treatment System Fed by a GPV Source Using the Bond Graph Approach

This work presents a simulation model for a UV water treatment system, powered by a photovoltaic generator, which relates the current consumed by the lamp to the UV flux and water quality. The overall system also includes electronic converters, electronic ballast (RLC resonant circuit), a UV lamp (UV irradiation source), and a centrifugal pump. To optimize the power transfer from the PV generator to the ballast and the UV lamp, a Maximum Power Point Tracking (MPPT) device is used. The overall water treatment system presents a complex model due to its hybrid components. The bond graph tool with a multidisciplinary vocation allows precisely, by its graphic nature, using a unified language, to explicitly display the nature of the power exchanges in the system and facilitate its control. This tool is a solution for non-linear systems that guarantees and facilitates their modeling without difficulties.

Prenatal stress perturbs fetal iron homeostasis in a sex specific manner

The adverse effects of maternal prenatal stress (PS) on child’s neurodevelopment warrant the establishment of biomarkers that enable early interventional therapeutic strategies. We performed a prospective matched double cohort study screening 2000 pregnant women in third trimester with Cohen Perceived Stress Scale-10 (PSS-10) questionnaire; 164 participants were recruited and classified as stressed and control group (SG, CG). Fetal cord blood iron parameters of 107 patients were measured at birth. Transabdominal electrocardiograms-based Fetal Stress Index (FSI) was derived. We investigated sex contribution to group differences and conducted causal inference analyses to assess the total effect of PS exposure on iron homeostasis using a directed acyclic graph (DAG) approach. Differences are reported for p < 0.05 unless noted otherwise. Transferrin saturation was lower in male stressed neonates. The minimum adjustment set of the DAG to estimate the total effect of PS exposure on fetal ferritin iron biomarkers consisted of maternal age and socioeconomic status: SG revealed a 15% decrease in fetal ferritin compared with CG. Mean FSI was higher among SG than among CG. FSI-based timely detection of fetuses affected by PS can support early individualized iron supplementation and neurodevelopmental follow-up to prevent long-term sequelae due to PS-exacerbated impairment of the iron homeostasis.

THE CONSTRUCTION OF THE BEST MATHEMATICAL MODEL FOR CALCULATING RADON CONCENTRATION IN MINERAL WATER

In this paper, we presented three mathematical models to calculate the amount of radon concentration in water which is considered a poisonous gas. two of these models are established by numerical methods, which are the Neville method and the Spline method. The graph approach is used to create the third model. It is notable that the current study is original and having a novelty as it has created for the first time three mathematical methods to calculate radon gas concentrations in water; as well as it is the first study that has applied the numerical methods to carry out the calculations. the results obtained from these methods are compared with previous studies and then finding the error rates. After that, we have made a comparison between these three models to obtain the best one. Since the error rate is slightly lower than the Neville method so the Spline method is the best of the three methods that we worked with.

As Data Flows Surge Data Infrastructure Groans

As Data Flows Surge Data Infrastructure Groans

Priority flow divider valve and its dynamic analysis using various hydraulic drive systems: a bond graph approach

Abstract. The priority flow divider valve (PFDV) is used in power hydraulic system to split the supply flow in dual paths, i.e., primary path and secondary path. The flow ratio in each path is either predetermined or can be adjustable as per the loading conditions. The stated properties of the valve can be applied in a steering mechanism in automobile and a hydrostatic power transmission unit in a wind turbine. In a steering mechanism, it helps the system to perform two different functions against two different loads simultaneously, whereas, in a wind turbine, it can be used in hydrostatic power transmission unit to reduce the power fluctuation obtained from it. The hydrostatic power transmission system with PFDV (SMHPTSPFDV) used in a steering mechanism and the hydrostatic power transmission system with PFDV (WTHPTSPFDV) used in a wind turbine are modeled using the bond graph technique, and simulated in SYMBOLS Shakti software to analyze the steady and dynamic performance of the PFDV. The user-defined rectangular and sinusoidal variable pump speed has been used as inputs for system performance analysis. It is found that the PFDV may handle dual loads simultaneously, and it eliminates the use of multiple control valves to operate the same dual loads. Hence, it not only improves the system stability but also reduces the maintenance cost of the system. Moreover, the power and energy loss through the PFDV have been analyzed for both applications. It has been found that the power loss through the PFDV is higher when it is connected with SMHPTSPFDV. Also, the influences on the power and energy loss through PFDV are analyzed under various loading conditions.

The Connected Critical Node Problem

The Critical Node Problem is a well-known optimisation problem that aims to find the subset of nodes in a graph whose removal impacts the graph connectivity as much as possible according to a specific connectivity measure. In this work, we study a new version of the Critical Node Problem, which we call the Connected Critical Node Problem, where the set of the removed nodes has to form a connected subgraph. We consider three connectivity measures and provide complexity results and solution approaches for general graphs and specific classes of graphs such as graphs with bounded treewidth, trees and series-parallel graphs. We consider the Connected Critical Node Problem where the pairwise connectivity (related to the number of pairs of vertices still connected in the graph) is minimised. We prove that this problem is strongly NP-hard and inapproximable on general graphs and is polynomial-time solvable on graphs with bounded treewidth and with unit connection costs. Further, we prove the NP-hardness of the problem with arbitrary connection costs over trees and series-parallel graphs and derive dynamic programming algorithms. We extend our results to the problem variants that consider the minimisation of the largest connected component and the maximisation of the number of connected components (also called K-way Vertex Cut Problem). As side results, we provide new complexity results for the classic Critical Node Problem on series-parallel graphs.

Domain-independent thermoelastic coupling mechanism for multi-physical systems applications: a Bond Graph approach

In this paper, we propose a domain-independent nonlinear thermoelastic coupling element to enable the realization of a two-way dynamic coupling between the thermal and elastic domains. The implementation of the proposed coupling element in multi-physical systems modeling will provide reversibly conserved power transactions among the existing physical domains (thermal and elastic domains), which is a favorable demand in coupled field dynamic investigations. To guarantee a conserved and continuous power transmission between the thermal and elastic domains, by means of the Bond Graph notation, a reversible multi-dimensional storage element is designed, and the corresponding multi-dimensional constitutive equations are derived that satisfy the Maxwell reciprocity. The power network of each domain of interest is, also, represented in the Bond Graph notation, and the corresponding governing equation of each domain is extracted on the basis of physical system theory. The reversible connectivity of the thermal and elastic domains is then executed via replacing the storage elements of each of the domains with the proposed coupling element. The ensuing model will provide the thermoelastic behavior of the system from the reversible thermal and elastic domains’ energetic interactions, which preserves the fundamental physical natures of such phenomena in the model. The generated coupling element will allow the model of each physical domain to remain independent from other existing domains while they are dynamically coupled, which provides a unique benefit for multi-physical phenomena investigations.

Hemodynamic profiling by critical care echocardiography could be more accurate than invasive techniques and help identify targets for treatment

In this prospective observational study, non-invasive critical care echocardiography (CCE) was used to obtain quantitative hemodynamic parameters in 107 intensive care unit (ICU) patients; the parameters were then visualized in a novel web graph approach to increase the understanding and impact of CCE abnormalities, as an alternative to thermodilution techniques. Visualizing the CCE hemodynamic data in six-dimensional web graph plots was feasible in almost all ICU patients. In 23.1% of patients, significant tricuspid regurgitation prevented correlation between thermodilution techniques and echocardiographic hemodynamics. Two parameters of longitudinal right ventricular function (TAPSE and S’) did not correlate in ICU patients. Clinical surrogate parameters of hemodynamic compromise did not correlate with measured hemodynamics. 26.2% of the patients with mean arterial pressures above 60 mmHg had cardiac indices (CI) below 2.5 L min−1·m−2. A CI below 2.2 L·min−1·m−2 was associated with a significant ICU survival disadvantage. CCE was feasible in addition or as an alternative to thermodilution techniques for the hemodynamic evaluation of ICU patients. Six-dimensional web graph plots visualized the hemodynamic states and were especially useful in conditions in which thermodilution methods were not reliable. Hemodynamic CCE identified patients with previously unknown low CI, which correlated with a higher ICU mortality.

A microenvironment prediction model for Chinese solar greenhouses based on the bond graph approach.

To improve the prediction accuracy of temperature and humidity in typical Chinese solar greenhouses, this paper proposed a new longwave/shortwave radiation modeling method using bond graph. This model takes into account sun position, useful incoming solar radiation model, sky longwave radiation model, inside longwave, and shortwave radiation model. The approach solves the problems caused by underestimating the effects of longwave radiation on night temperature and relative humidity. The study found that after a period of t = 7.5 h, with the increase of sun altitude angle, the internal temperature was significantly affected by the temperature rise of outside environment on sunny day. The sun altitude angle gradually falls over a period of t = 12.5 h (beginning at 12.30 p.m.). The decline in night temperature steadily slowed after a period of t = 20.5 h. On the other hand, the temperature variation has a multi-peak distribution and the warming rate of the CSG slows down on cloudy days. Furthermore, a good agreement between the experimental and simulation data were obtained, with a maximum temperature deviation of 2°C and maximum humidity deviation of 5%. The developed model is a universal and valuable approach that can be used for greenhouse climate simulation. Furthermore, it can be used as a support system during decision-making processes to help manage Chinese solar greenhouses more efficiently, which provides several control perspectives on the low-energy greenhouse in the future. This work has also provided several control perspectives on the low energy greenhouse in the future.

A physics-, SCADA-based remaining useful life calculation approach for wind turbine drivetrains

This paper describes the development of a physics-, SCADA-based model able to predict the expected lifetime for wind turbine drivetrains. A real-time coupled torsional gearbox-generator model is developed using the bond graph approach in the software 20SIM. The model uses SCADA data with a sampling frequency of one hertz to impose a load reference on the wind turbine for the simulation model. From the SCADA measurements, rotor torque is estimated and used as input load to the wind turbine rotor, while generator speed is used as reference in the control loop for maximum power point tracking. Shaft torsion is used to predict highspeed shaft radial and axial bearing loads from static equilibrium. The load amplitude and the number of stress cycles are calculated using the load duration distribution method and damage is calculated using Miner’s rule. Expected lifetime is predicted by linear extrapolation of the accumulated fatigue damage to the fatigue limit. Results show that the model can capture the torsional and electrical dynamics and that the model results agree with the reference input. The radial bearing loads match well with literature where additional sensors are used to determine the loads.

Dynamic modelling of PEM fuel cell system for simulation and sizing of marine power systems

This article presents a model of a proton exchange membrane fuel cell (PEMFC) system for marine power systems. PEMFC in marine hybrid power sources can have various power ranges and capacities in contrast with vehicle applications. Investigating PEMFCs behaviour and performance for various conditions and configurations is demanded for proper sizing and feasibility studies. Hence, modelling and simulation facilitate understanding the performance of the PEMFC behaviour with various sizes and configurations in power systems. The developed model in this work has a system level fidelity with real time capabilities, which can be utilized for simulator approaches besides quasi-static studies with a power-efficiency curve. Moreover, the model can be used for scaling the PEMFC power range by considering transient responses and corresponding efficiencies. The Bond graph approach as a multi-disciplinary energy based modelling strategy is employed for the PEMFC as a multi domains system. In the end, various PEMFC cell numbers and compressor sizes have been compared with power-efficiency curves and transient responses in a benchmark.

Tracking Control of Physical Systems with Application to a System with a DC Motor: A Bond Graph Approach

In this paper, the bond graph modeling for the control of tracking systems has been applied. The closed loop system is built by the bond graph model of the system to be controlled, an additional bond graph according to the tracking input signal, and feedback gains in the physical domain. Hence, a procedure to obtain the closed loop tracking system is proposed. The proposal of modeling and tracking control systems in this paper determines symmetries in the bond graph approach with respect to the traditional algebraic approach. The great advantage of this graphical approach is that the mathematical determination of the system model is not necessary. Moreover, the coefficients of the characteristic polynomial using unidirectional causal loops of the closed loop system modeled in bond graphs are obtained. A case of study of a DC motor connected to an electrical supply network and a mechanical load is considered. Tracking control for the step, ramp, and acceleration type input signals in a bond graph approach are applied. In order to show the effectiveness of the proposed procedure, the simulation results are shown.

Purchasing Power Parity Between Vietnam and United States

This study uses the annual data of consumer price index, exchange rate and inflation rates spanned from 1986 to 2014 in order to observe whether the PPP hypothesis holds between Vietnam and United States. First, the findings come out based on the graph aproach which is used in order to examine both short-run and long-run PPP. Second, the Engle-Granger approach is applied in order to test for the long – run PPP again. In the short-run, the result from the graph approach indicates that PPP holds during hyperinflation years. After hyperinflation period, the exchange rate and relative price tend to be close together in several years. This finding in the short-run seems to reveal a good guide in the long-run PPP. Both graph and the Engle-Granger approach show the same result in the long –run. Upon the stationary testing, the finding of the Engle-Granger approach demonstrates that residuals from the estimation are stationary, therefore, there is an existence of a long-run relationship between nominal and real exchange rate. In other way, PPP seems to hold between Vietnam and United States. The result leads to the implication that multinational companies should set the same prices for products in Vietnam and United States market based on PPP. Otherwise, investors can gain profit through arbitrage strategies.

Transmissibility-Based Health Monitoring of the Future Connected Autonomous Vehicles Networks

Transmissibility is a mathematical model that relates a subset of a system’s outputs to another subset of outputs of the same system without knowledge of the external excitation or the dynamics of the system. This study investigates fault detection, localization, and mitigation of connected autonomous vehicles (CAV) platoons using transmissibility operators. A CAV platoon is a network of connected autonomous vehicles that communicate together to move in a specific path with the desired velocity. Failure in a physical component of a vehicle, or failure in the form of an internal delay, a cyber-attack, or a communication time-delay affects the safety and security of the CAV platoons. In this paper, we use measurements from sensors available in CAV platoons to identify transmissibility operators, which are used for health monitoring, fault localization, and fault mitigation in the platoon. We first consider the case of vehicle-to-cloud communication (V2C) to monitor the platoon’s health. Then, we assume that the platoon loses communication with the cloud, and we monitor the health of the platoon based on vehicle-to-vehicle (V2V) communication. We apply the proposed technique to a model of the platoon obtained using the bond graph approach, and an experimental setup consisting of three connected autonomous robots.

An extended approach of weight collective influence graph for detection influence actor

Over the last decade, numerous methods have been developed to detect the influential actors of hate speech in social networks, one of which is the Collective Influence (CI) method. However, this method is associated with unweighted datasets, which makes it inappropriate for social media, significantly using weight datasets. This study proposes a new CI method called the Weighted Collective Influence Graph (WCIG), which uses the weights and neighbor values to detect the influence of hate speech. A total of 49, 992 Indonesian tweets were and extracted from Indonesian Twitter accounts, from January 01 to January 22, 2021. The data collected are also used to compare the results of the proposed WCIG method to determine the influential actors in the dissemination of information. The experiment was carried out two times using parameters ∂=2 and ∂=4. The results showed that the usernames bernacleboy and zack_rockstar are influential actors in the dataset. Furthermore, the time needed to process WCIG calculations on HPC is 34-75 hours because the larger the parameter used, the greater the processing time.

Timetable optimization for a moving block system

We present an optimization model which is capable of routing and ordering trains on a microscopic level under a moving block regime. Based on a general timetabling definition (GTTP) that allows the plug in of arbitrarily detailed methods to compute running and headway times, we describe a layered graph approach using velocity expansion, and develop a mixed integer linear programming formulation. Finally, we present promising results for a German corridor scenario with mixed traffic, indicating that applying branch-and-cut to our model can solve reasonably sized instances with up to a hundred trains to optimality.

Context mining and graph queries on giant biomedical knowledge graphs

Contextual information is widely considered for NLP and knowledge discovery in life sciences since it highly influences the exact meaning of natural language. The scientific challenge is not only to extract such context data, but also to store this data for further query and discovery approaches. Classical approaches use RDF triple stores, which have serious limitations. Here, we propose a multiple step knowledge graph approach using labeled property graphs based on polyglot persistence systems to utilize context data for context mining, graph queries, knowledge discovery and extraction. We introduce the graph-theoretic foundation for a general context concept within semantic networks and show a proof of concept based on biomedical literature and text mining. Our test system contains a knowledge graph derived from the entirety of PubMed and SCAIView data and is enriched with text mining data and domain-specific language data using Biological Expression Language. Here, context is a more general concept than annotations. This dense graph has more than 71M nodes and 850M relationships. We discuss the impact of this novel approach with 27 real-world use cases represented by graph queries. Storing and querying a giant knowledge graph as a labeled property graph is still a technological challenge. Here, we demonstrate how our data model is able to support the understanding and interpretation of biomedical data. We present several real-world use cases that utilize our massive, generated knowledge graph derived from PubMed data and enriched with additional contextual data. Finally, we show a working example in context of biologically relevant information using SCAIView.