Pseudo Graph Research Articles

Comparison of natural corn cob and carbon corn cob in the purification of used cooking oil with shaker operation

The research aims to determine the adsorption ability and modeling of adsorption kinetics to purify used cooking oil using natural and carbonized corn cob adsorbent, which is economical, efficient, and sustainable due to its low price, renewability, and simplicity. Samples of used cooking oil were taken as much as 100 ml with an adsorbent mass of 3 g and particle size of 50 mesh and stirred using a shaker. Based on the results, it was found that the effect of carbonization was able to reduce the turbidity of used cooking oil, whereas for the use of natural corn cob adsorbent, at 20 minutes, turbidity of 59.0 NTU was obtained and 45.2 NTU was obtained after the adsorption process for 5 hours, while for the use of corn cob carbon adsorbent, a turbidity of 62.8 NTU was obtained for the same adsorbent size and 39.8 NTU was obtained after the adsorption process for 5 hours. Chemical interaction trends were also obtained, as shown in the second-order pseudo graph with coefficient of determination (R2) values close to 1 using natural corn cob and corn cob carbon adsorbents, namely 0.9965 and 0.9959.

Entanglement, Quantum Correlators, and Connectivity in Graph States

AbstractThis work presents a comprehensive exploration of the entanglement and graph connectivity properties of Graph States (GSs). Qubit entanglement in Pseudo Graph States (PGSs) is quantified using the Entanglement Distance (ED), a recently introduced measure of bipartite entanglement. In addition, a new approach is proposed for probing the underlying graph connectivity of genuine GSs, using Pauli matrix quantum correlators. These findings also reveal interesting implications for measurement processes, demonstrating the equivalence of some projective measurements. Finally, the emphasis is placed on the simplicity of data analysis in this framework. This work contributes to a deeper understanding of the entanglement and connectivity properties of GSs, offering valuable information for quantum information processing and quantum computing applications. The famous stabiliser formalism, which is the typically preferred framework for the study of this type of states, is not used in this work; on the contrary, this approach is based exclusively on the concepts of expectation values, quantum correlations, and projective measurement, which have the advantage of being very intuitive and fundamental tools of quantum theory.

FedGL: Federated graph learning framework with global self-supervision

Graph data are ubiquitous in the real world. Graph learning (GL) attempts to mine and analyze graph data so that valuable information can be discovered. Existing GL methods are designed for centralized scenarios. However, in practical scenarios, graph data are usually distributed across different organizations, resulting in isolated data silos. To address this problem, we incorporate federated learning into GL and propose a general Federated Graph Learning framework called FedGL. FedGL is capable of obtaining a high-quality global graph model while protecting data privacy by discovering the global self-supervision information during the federated training. Specifically, we propose uploading prediction results and node embeddings to the server for discovering the global pseudo labels and global pseudo graph. These are then distributed to each client to enrich the training labels and complement the graph structure respectively, thereby improving the quality of each local model. Moreover, the global self-supervision enables information of each client to flow and be shared in a privacy-preserving manner, thus alleviating the heterogeneity and utilizing the complementarity of graph data among different clients. Finally, experimental results show that FedGL significantly outperforms baselines on four widely used graph datasets.

Extended Graph of Fuzzy Topographic Topological Mapping Model: G04(FTTMn4)

Fuzzy topological topographic mapping (FTTM) is a mathematical model that consists of a set of homeomorphic topological spaces designed to solve the neuro magnetic inverse problem. The key to the model is its topological structure that can accommodate electrical or magnetic recorded brain signal. A sequence of FTTM, FTTMn, is an extension of FTTM whereby its form can be arranged in a symmetrical form, i.e., polygon. The special characteristic of FTTM, namely, the homeomorphisms between its components, allows the generation of new FTTM. The generated FTTMs can be represented as pseudo graphs. A pseudo-graph consists of vertices that signify the generated FTTM and edges that connect their incidence components. A graph of pseudo degree zero, G0(FTTMnk ), however, is a special type of graph where each of the FTTM components differs from its adjacent. A researcher posted a conjecture on G03(FTTMn3) in 2014, and it was finally proven in 2021 by researchers who used their novel grid-based method. In this paper, the extended G03(FTTMn3), namely, the conjecture on G04(FTTMn4) that was posed in 2018, is narrated and proven using simple mathematical induction.

Pseudo-Graph Neural Networks On Ordinary Differential Equations

In this paper, we extend the idea of continuous-depth models to pseudo graphs and present pseudo graph ordinary differential equations (PGODE), which are inspired by the neural ordinary differential equation (NODE) for data in the Euclidean domain. All existing graph networks have discrete depth. A pseudo graph neural network (PGNN) is used to parameterize the derivative of hidden node states, and the output states are the solution to this ordinary differential equation (ODE). A memory-efficient framework with precise gradient estimates is then proposed for free-form ODEs. We also introduce the framework of continuous–depth pseudo graph neural networks (PGNNs) on ODE by blending discrete structures and differential equations

Extended Graph of the Fuzzy Topographic Topological Mapping Model

Fuzzy topological topographic mapping (FTTM) is a mathematical model which consists of a set of homeomorphic topological spaces designed to solve the neuro magnetic inverse problem. A sequence of FTTM, FTTMn, is an extension of FTTM that is arranged in a symmetrical form. The special characteristic of FTTM, namely the homeomorphisms between its components, allows the generation of new FTTM. The generated FTTMs can be represented as pseudo graphs. A graph of pseudo degree zero is a special type of graph where each of the FTTM components differs from the one adjacent to it. Previous researchers have investigated and conjectured the number of generated FTTM pseudo degree zero with respect to n number of components and k number of versions. In this paper, the conjecture is proven analytically for the first time using a newly developed grid-based method. Some definitions and properties of the novel grid-based method are introduced and developed along the way. The developed definitions and properties of the method are then assembled to prove the conjecture. The grid-based technique is simple yet offers some visualization features of the conjecture.

Minimizing information loss in shared data: Hiding frequent patterns with multiple sensitive support thresholds

AbstractPrivacy preserving data mining (PPDM) is the process of protecting sensitive knowledge from being discovered by data mining techniques in case of data sharing. Privacy preserving frequent itemset mining (PPFIM) is a subtask and NP‐hard problem of PPDM. Its objective is to modify a given database in such a way that none of the sensitive itemsets of the database owner can be obtained by any frequent itemset mining technique from the modified database. The main challenge of PPFIM is to minimize the distortion given to the data and nonsensitive knowledge while sanitizing all given sensitive itemsets. Distortion‐based sensitive itemset hiding algorithms decrease the support of each sensitive itemset under a predefined sensitive threshold through sanitization. Most of the distortion‐based itemset hiding algorithms allow database owner to define a single sensitive threshold for each sensitive itemset. However, this is a limitation to the database owner since the importance of each sensitive itemset varies. In this paper we propose a distortion‐based itemset hiding algorithm that allows database owner to assign multiple sensitive thresholds, namely itemset oriented pseudo graph based sanitization (IPGBS) algorithm. The purpose of IPGBS algorithm is to give minimum distortion to the nonsensitive knowledge and data while hiding all sensitive itemsets. For this reason, the IPGBS algorithm modifies least amount of transaction and transaction content. The performance evaluation of the IPGBS algorithm is conducted by using two different counterparts on four different databases. The results show that the IPGBS algorithm is more efficient in terms of nonsensitive frequent itemset loss on both dense and sparse databases. It has considerable good results in terms of number of transactions modified, number of items deleted, execution time and total memory allocation as well.

PGCN-TCA: Pseudo Graph Convolutional Network With Temporal and Channel-Wise Attention for Skeleton-Based Action Recognition

Skeleton-based human action recognition has become an active research area in recent years. The key to this task is to fully explore both spatial and temporal features. Recently, GCN-based methods modeling the human body skeletons as spatial-temporal graphs, have achieved remarkable performances. However, most GCN-based methods use a fixed adjacency matrix defined by the dataset, which can only capture the structural information provided by joints directly connected through bones and ignore the dependencies between distant joints that are not connected. In addition, such a fixed adjacency matrix used in all layers leads to the network failing to extract multi-level semantic features. In this paper we propose a pseudo graph convolutional network with temporal and channel-wise attention (PGCN-TCA) to solve this problem. The fixed normalized adjacent matrix is substituted with a learnable matrix. In this way, the matrix can learn the dependencies between connected joints and joints that are not physically connected. At the same time, learnable matrices in different layers can help the network capture multi-level features in spatial domain. Moreover, Since frames and input channels that contain outstanding characteristics play significant roles in distinguishing the action from others, we propose a mixed temporal and channel-wise attention. Our method achieves comparable performances to state-of-the-art methods on NTU-RGB+D and HDM05 datasets.

Pseudo bond graph tunnel greenhouse model with accurate longwave/shortwave radiations model

ABSTRACT This research work is focused on the construction of an accurate longwave/shortwave radiation model on a tunnel greenhouse pseudo-bond graph model, widely used in Tunisia. This model includes sun position, useful incoming solar radiation model, sky longwave radiation model, inside longwave and shortwave radiation model. The key idea is to use bond graphs allowing a lumped modelling approach which is suitable for control applications. Furthermore, an evaluation of some longwave radiative model components was made, noting that these components are particularly sensitive regarding to the thermal behaviour of the model. Experimental tunnel greenhouse data are used as validation elements for the present model with globally good results. A comparative study was also performed between the present model and a previous bond graph model containing a simplistic radiative model. Practical simulation results show a clear improvement compared with the previous model.

Newton flows for elliptic functions III & IV

An elliptic Newton flow is a dynamical system that can be interpreted as a continuous version of Newton’s iteration method for finding the zeros of an elliptic function f. Previous work focuses on structurally stable flows (i.e., the phase portraits are topologically invariant under perturbations of the poles and zeros for f), including a classification/representation result for such flows in terms of Newton graphs (i.e., cellularly embedded toroidal graphs fulfilling certain combinatorial properties). The present paper deals with non-structurally stable elliptic Newton flows determined by pseudo Newton graphs (i.e., cellularly embedded toroidal graphs, either generated by a Newton graph, or the so-called nuclear Newton graph, exhibiting only one vertex and two edges). Our study results into a deeper insight in the creation of structurally stable Newton flows and the bifurcation of non-structurally stable Newton flows. As it requires the classification of all third order Newton graphs, we present this classification.

Pseudo Bond Graph Model of Thermal Transfers Supported by an Outside Cold Airflow of a Refrigerator to Improve Its Performance

This paper aims to prove the efficiency of using the naturally free and abundant cold airflow to improve the cooling inside a household refrigerator and to reduce the energy consumption in Nordic and high altitude countries. The cold airflow is spread out in a cavity covering the side wall of the appliance. This cavity is also connected to the cold outdoor environment through inlet and outlet ducts. For that purpose, a pseudo bond graph (BG) model is proposed to model this installation. It represents the thermal transfers by forced convection in the cavity and the ducts with the ambient and outdoor surroundings. The internal air temperature is computed according to the evaporator temperature and the outside cold airflow that is also computed according to the outside temperature. The simulation results show that the cooling process was speeded up by about 39% and the duration of the compressor functioning was reduced by about 10[Formula: see text]h and 8[Formula: see text]min, which allows reducing the total energy use by 0.88[Formula: see text]kWh, and thus achieving an energy saving of about 34.61%. Experimental data of the outside airflow temperature in Canada during 28 hours in January 2017 are used in the model and allow an energy saving of about 40%.

Study on Strongly Pseudo Irregular Fuzzy Graphs

Study on Strongly Pseudo Irregular Fuzzy Graphs

Pseudo Bond Graph Modeling and Experimental Investigation of Thermal Transfers Inside a Household Refrigerator

This paper proposes a pseudo bond graph model of thermal transfers by natural convection inside a household refrigerator. It has two inputs: the ambient temperature and the temperature at the level of the evaporator wall. The latter assesses the functioning of the compressor cycles. A performance comparison, with the experimental data, was carried out in order to verify the model, in which, real measurements are used to modulate the evaporator temperature source. The simulation results show the effectiveness of the proposed approach.

Bond graph modeling for fault diagnosis: the continuous stirred tank reactor case study

Supervision of chemical and biochemical processes is difficult due to various factors, for example the evolution of nonlinear state variables, uncertainties in parameters and disturbances, and system operating conditions. The complexities arise out of the energetic couplings in the model. In this work, the bond graph representation, as a unified and multidisciplinary tool, is used for dynamic modeling and diagnosis for process engineering, incorporating the chemical kinetics of an equilibrated reaction, heat and mass transport phenomena. A pseudo bond graph model technique is applied to a pilot reactor fitted with a mono-fluid heating/cooling system. A modeling structure has been implemented through process sub-units and validated through experimental observations. This technique is proposed to indicate physico-chemical failures, for example matter leakage, caloric isolation, or the occurrence of a secondary reaction or temperature runaway when a very exothermic reaction occurs. It provides the computational algorithms to eliminate unknown variables from coupled thermochemical models and thus generate analytical redundancy relations (ARRs) in terms of measurements and parameters for online diagnosis.

Addition of momentum and kinetic energy effects in supersonic compressible flow using pseudo bond graph approach

In most of the papers published on compressible fluid using pseudo the bond graph approach, momentum and kinetic energy effects have been neglected due to low speed. However, in convergent–divergent nozzles that the flow is supersonic, these models will lose their validation. For the purpose of considering kinetic energy in compressible fluid flow, this paper introduces a new field (KE-field) to pseudo bond graph. This field can also be used to extract the momentum equation on the gaseous models. Furthermore, Karnopp’s suggested relation for an isentropic nozzle is developed to a convergent–divergent nozzle. The simulation results show that the thrust force obtained from the simulations has a very good agreement to analytical relationships. Therefore, it suggests that the KE-field can be used for modelling the thrust force. As bond graph method has been implemented in many software applications such as MS1, SYMBOLS2000 and 20SIM®, the new field can also be used in these software.

Bi-SOGC: A Graph Matching Approach Based on Bilateral KNN Spatial Orders Around Geometric Centers for Remote Sensing Image Registration

In this letter, Bilateral K Nearest Neighbors Spatial Orders around Geometric Centers (Bi-SOGC) is presented to match feature points for remote sensing images with large affine transformation, similar patterns or multispectral images. In Bi-SOGC, both the bilateral adjacent relations and the spatial angular orders are considered. Bilateral K Nearest Neighbors (BiKNN) descriptors are proposed to describe the adjacent information. The vertices with maximum BiKNN difference are deemed as candidate outliers. The invariant spatial angular orders for affine transformation are used to deal with outliers in pseudo isomorphic structures, geometric centers are taken as the reference points. To increase the correct matching points and eliminate stubborn outliers, a recovery strategy utilizes the addition of fresh inliers to break down the stabilized pseudo graphs of the residual sets. Experimental results demonstrate the superior performance of this algorithm under various conditions for remote sensing images.

Enlarging the classes of super edge-magic 2-regular graphs

A graph G is called super edge-magic if there exists a bijective function f: V(G) ≼ E(G) → {1, 2,…,|V(G)| + |E(G)|} such that f(V(G)) = {1, 2,…,|V(G)|} and f (u) + f(v) + f(uv) is a constant for each uv ∊ E(G). A graph G with isolated vertices is called pseudo super edge-magic if there exists a bijective function f: V(G) → {1, 2,…, |V(G)|} such that the set {f (u) + f(v)|uv ∊ E(G)} ≼ {2f (u)|degG u = 0} consists of |E (G)| + |{u ∊ V(G) |degG u = 0}| consecutive integers. In this paper, we enlarge the classes of super edge-magic 2-regular graphs by presenting some constructions that allow us to generate large classes of super edge-magic 2-regular graphs from previously known super edge-magic 2-regular graphs or pseudo super edge-magic graphs.

On the Jacobsthal and Jacobsthal–Lucas sequences by matrix methods

In the present paper, we define two directed pseudo graph. Then, we investigate the adjacency matrices of the defined graphs and show that the permanents of the adjacency matrices are Jacobsthal and Jacobsthal–Lucas numbers. We also give complex factorization formulas for the Jacobsthal sequence.

Pseudo bond graph modeling of wastewater treatment bioprocesses

The modeling of biotechnological processes is a complex task, because of the high interaction degree of the phenomena, non-linearities, and uncertainties of reaction kinetics. A novel approach for bioprocess modeling, based on pseudo bond graphs, is proposed in this paper. Bond graph method is an appropriate tool for modeling non-linear processes, eventually along with their control systems. Firstly, the pseudo bond graph method is applied in order to model some prototype bioprocesses starting from the reactions schemes and taking into account the biochemical phenomena. Then the rules used for the design of these models are applied on two widespread wastewater treatment processes. The first is an activated sludge bioprocess taking place in a sequential batch reactor, and the second is a wastewater biodegradation with the production of methane gas, which is carried out inside a continuous stirred tank bioreactor. The obtained bond graph models and several simulations are conducted using the 20sim modeling and simulation environment. This modeling procedure represents a valuable illustration of the power of the bond graph technique, and can be used as a base for the development of models of bioprocesses with a high level of complexity.

High Gain Observers for Non-Linear Systems Modelled by Bond Graphs

In the present work, a bond graph approach to build full-order high gain observers is shown. The proposed approach is based on the classical techniques to design high gain observers. Simulation results are used to show the performance of the high gain observer in chemical engineering applications as a distillation column modelled by pseudo bond graphs.