Line Graphs Research Articles

Effect of some design parameters on the performance of side access nozzle and grooved plate of centre pivot sprinkler

This research evaluates the effect of various heights, pressures, nozzle size and groove numbers on the uniformity distribution of a developed pivot sprinkler. The experiment was set up at an open space in Niger State College of Agriculture Mokwa. The wind speed and temperature during the experiment were at an average of 2.2mph and 25°C. A nozzle of size 6mm, and deflection plate of 6 curved grooves were used in a centre pivot spray unit. In evaluating the performance of the spray unit components i.e. the nozzle and deflection plate, three different pressures of 5, 10, and 15 Psi and heights of 1, 1.2, and 1.5mm were used. The support structure for the experiment is an inverted U-shaped frame designed to support a spray sprinkler at different heights. The water source was a reservoir with a capacity of 3m3and a 1.5hp electric centrifugal pump was used. The volumetric or bucket method was used to measure flow rate. The application rate was measured using catch can method. The duration of each test was approximately 30min. The data obtained from the measurements were statistically analyzed with line graphs using Excel. Relationships were also established between discharge and pressure. The highest index of jet break up was 4.89 at 15Psi and 3.74 at 10Psi. The result shows that as the pressure increases with height the CU also increases. The highest uniformity of 79.4% was obtained at 1.5m height and 15Psi.

GCFExplainer: Global Counterfactual Explainer for Graph Neural Networks

Graph neural networks (GNNs) find applications in various domains such as computational biology, natural language processing, and computer security. Owing to their popularity, there is an increasing need to explain GNN predictions since GNNs are black-box machine learning models. One way to address this issue involves using counterfactual reasoning where the objective is to alter the GNN prediction by minimal changes in the input graph. Existing methods for counterfactual explanation of GNNs are limited to instance-specific local reasoning. This approach has two major limitations of not being able to offer global recourse policies and overloading human cognitive ability with too much information. In this work, we study the global explainability of GNNs through global counterfactual reasoning. Specifically, we want to find a small set of representative counterfactual graphs that explains all input graphs. Towards this goal, we propose GCF Explainer , a novel algorithm powered by vertex-reinforced random walks on an edit map of graphs with a greedy summary . Extensive experiments on real graph datasets show that the global explanation from GCF Explainer provides important high-level insights of the model behavior and achieves a 46.9% gain in recourse coverage, a 9.5% reduction in recourse cost compared to the state-of-the-art local counterfactual explainers. We also demonstrate that GCF Explainer generates explanations that are more consistent with input dataset characteristics, and is robust under adversarial attacks. In addition, K-GCF Explainer , which incorporates a graph clustering component into GCF Explainer , is introduced as a more competitive extension for datasets with a clustering structure, leading to superior performance in three out of four datasets in the experiments and better scalability.

The 5-Fluorouracil RNA Expression Viewer (5-FUR) Facilitates Interpreting the Effects of Drug Treatment and RRP6 Deletion on the Transcriptional Landscape in Yeast.

Saccharomyces cerevisiae is an excellent model to study the effect of external cues on cell division and stress response. 5-Fluorocuracil (5-FU) has been used to treat solid tumors since several decades. The drug was initially designed to interfere with DNA replication but was later found to exert its antiproliferative effect also via RNA-dependent processes. Since 5-FU inhibits the activity of the 3'-5'-exoribonuclease Rrp6 in yeast and mammals, earlier work has compared the effect of 5-FU treatment and RRP6 deletion at the transcriptome level in diploid synchronized yeast cells. To facilitate interpreting the expression data we have developed an improved 5-Fluorouracil RNA (5-FUR) expression viewer. Users can access information via genome coordinates and systematic or standard names for mRNAs and Xrn1-dependent-, stable-, cryptic-, and meiotic unannotated transcripts (XUTs, SUTs, CUTs, and MUTs). Normalized log2-transformed or linear data can be displayed as filled diagrams, line graphs or color-coded heatmaps. The expression data are useful for researchers interested in processes such as cell cycle regulation, mitotic repression of meiotic genes, the effect of 5-FU treatment and Rrp6 deficiency on the transcriptome and expression profiles of sense/antisense loci that encode overlapping transcripts. The viewer is accessible at http://5fur.genouest.org.

Overlapping Structure Detection in Protein-Protein Interaction Networks Using a Modified Version of Particle Swarm Optimization

In today's world, the science of bioinformatics is developing rapidly, especially with regard to the analysis and study of biological networks. Scientists have used various nature-inspired algorithms to find protein complexes in protein-protein interaction (PPI) networks. These networks help scientists guess the molecular function of unknown proteins and show how cells work regularly. It is very common in PPI networks for a protein to participate in multiple functions and belong to many complexes, and as a result, complexes may overlap in the PPI networks. However, developing an efficient and reliable method to address the problem of detecting overlapping protein complexes remains a challenge since it is considered a complex and hard optimization problem. One of the main difficulties in identifying overlapping protein complexes is the accuracy of the partitioning results. In order to accurately identify the overlapping structure of protein complexes, this paper has proposed an overlapping complex detection algorithm termed OCDPSO-Net, which is based on PSO-Net (a well-known modified version of the particle swarm optimization algorithm). The framework of the OCDPSO-Net method consists of three main steps, including an initialization strategy, a movement strategy for each particle, and enhancing search ability in order to expand the solution space. The proposed algorithm has employed the partition density concept for measuring the partitioning quality in PPI network complexes and tried to optimize the value of this quantity by applying the line graph concept of the original graph representing the protein interaction network. The OCDPSO-Net algorithm is applied to a Collins PPI network and the obtained results are compared with different state-of-the-art algorithms in terms of precision ( ), recall ( ), and F-measure ( ). Experimental results confirm that the proposed algorithm has good clustering performance and has outperformed most of the existing recent overlapping algorithms. .

Extremal values on the Kirchhoff index of the line graph of trees

The computation of resistance distance and the Kirchhoff index is a classical problem that has been extensively investigated by numerous mathematicians, physicists, and scientists. Consider a simple connected graph G with vertex set V(G) and edge set E(G). If we replace each edge of G with a resistor of 1 ohm resistance, we create an electrical network N; in that case, the distance between any two nodes between network N is called resistance distance. The Kirchhoff index is a mathematical term that quantifies the complexity of a graph; it is defined as the total resistance distance among each pair of vertices in G. The line graph LG is constructed from G by swapping out the edges for vertices; in LG, if two vertices share endpoints in G, then they are connected in LG. In this study, extremal values for the Kirchhoff index of the line graph of trees are calculated. Further, we will also calculate the Kirchhoff index for the line graph of some special trees and establish the relationship between them.

The effect of manganese and carbon on the mechanical properties of the welded layer of the bucket teeth of the Hadfield steel excavator

The paper investigates the effect of the surfacing composition’s chemical structure (the manganese and carbon concentrations’ ratio) on the mechanical properties of the surfacing layer on Hadfield steel for increasing the durability of the products made from it, namely the excavator bucket’s teeth. Two criteria, namely the impact toughness and wear resistance of the coating, were chosen as optimization parameters. The assessment of the impact strength of the deposited coating was carried out on the Sharpie samples on the pendulum copra, and the wear test was carried out according to the Brinly-Haworth scheme in the conditions of samples’ abrasion with quartz sand. Wear resistance of Hadfield steel, containing 1.1% C and 13% Mn, was taken as a unit of wear resistance in these experiments. To determine the optimal coating modes, the active experiment with the application of mathematical planning methods was conducted. The obtained response surfaces and graphs of equal output lines make it possible to establish the level of studied factors’ influence on the optimization parameter. In order to analyze the influence of given factors on the optimization criteria, scatter plots with histograms were constructed, which make it possible to determine the rational values ​​of the selected optimization criteria graphically - impact toughness and wear resistance of the coating

Independent Domination Topology of the Friendship Graph and Its Line Graph

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Combination of control factors influencing application uniformity of a low-pressure pivot sprinkler spray unit

The objective of any pivot irrigation system is to ensure uniformity of application of water which can be achieved when responsible factors such as pressure, drop tube lengths, nozzle size etc. are adequately designed. This indoor experiment conducted in a 44m2 laboratory facility tends to evaluate the extent of the influence of some sprinkler design parameters on the performance of a low pressure single pivot sprinkler spay unit. The parameters considered are; pressures of 9,12, 15 and 19Psi, nozzle sizes of 4.17, 5.56, 6.95 and 8.14 mm and drop tube lengths of 1, 1.2 and 1.5m. In this experiment an inverted U-shaped frame designed to support a spray sprinkler at different heights was used. The hydraulic installation with manual throttle valves for controlling water distribution was used to supply pressurized water to the spray model sprinkler. A number of 64 graduated catch-cans with 0.16 m diameter and 0.2m height were used. The cans were arranged in eight radial legs with a distance of 1m apart. The system was run for one hour then the caught volumes were measured manually using a graduated cylinder with a capacity of 500 mL. The catch-cans data were used to determine uniformity coefficients. Line graphs were used to show relationships between the parameters measured. The experiment shows that there is strong relationship between the parameters considered. It indicated that the coefficient of uniformity (CU) and distribution uniformity of lower quarter DUlq increases with increase in nozzle size and pressure. It also shows that the largest nozzle size and pressure within the limits of this experiment gave the best CU and (DUlq) values of 96% and 90% at 1.5m height. Lager nozzle sizes with lower pressure reduces the CU and DUlq values. It is concluded from this experiment that though pressure, nozzle size and height of application influences the uniformity of application, larger nozzle sizes accompanied by higher pressure and height of application resulted in good uniformity of application. It is therefore imperative that while operating Pivot Sprinkler systems, careful selection of nozzle diameters, operating pressure and drop tube heights, sprinkler can be used to apply the ideal amount of irrigation water needed to refill the crop root zone that can neither cause runoff nor harm the crop and also provide the best uniformity possible under the prevailing wind and management conditions.

Multi-Granularity and Multi-Interest Contrast-Enhanced Hypergraph Convolutional Networks for Session Recommendation

Aiming at the problems of the incomplete recommendation and sparsity of session data in session recommendation, a new multi-granularity and multi-interest contrast-enhanced hypergraph convolutional network (MGMI-CEHCN) model for session recommendation is proposed. The session data are modeled as a heterogeneous hypergraph, the information is embedded by the hypergraph using two granularities of item price and category, and then the information between different granularities is fused, while the final item embedding is obtained through multi-layer convolution. Finally, an interest perceptron is used to detect multiple potential interests for each item, and a decentralized interest extraction network based on a gated recurrent unit (GRU) is used to integrate the user’s final interests and obtain a global session representation through a soft attention mechanism; a local session representation is generated with the help of a weighted line graph convolutional network. A further joint contrast enhancement strategy is used to maximize the mutual information between the global session representation and local session representation, to improve the recommendation performance. Experiments on several real datasets showed that the recommendation performance of the MGMI-CEHCN model outperformed the current mainstream models. On Cosmetics, the P@20 reached up to 55.25% and M@20 reached up to 38.26%, improvements of 3.06% and 3.09%, respectively; on Diginetica-buy, the P@20 reached up to 65.60% and M@20 reached up to 27.47%, improvements of 2.47% and 6.64%, respectively, which proved the validity of the model.

B - 90 Establishing Cutoffs for Impaired Cognition in Virtual Reality Assessment: the Virtual Kitchen Protocol for Learning and Memory

Abstract Objective The Virtual Kitchen Protocol for Learning and Memory (VKP-LM) is a virtual-reality-based measure of everyday functioning through procedural memory tasks. Previous literature has introduced the VKP-LM and begun validation processes. This study explores cutoff scores to determine cognitive impairment across the Immediate (VKP-IR) and Delayed-Recall (VKP-DR) portions of the VKP-LM. Method Adults (N = 126; Mage = 70.29; 56.3% Female) completed the VKP-LM as a part of a neuropsychological evaluation in a community-based assessment center. Participants were categorized binarily as to whether they received a neurocognitive disorder (NCD) diagnosis. Discriminatory abilities and cutoffs were determined by the Receiver Operator Curve’s (ROC) Area Under the Curve (AUC) and sensitivity-specificity charts. Classification tables determined the Positive Predictive Power (PPP) and Negative Predictive Power (NPP) given the established cutoffs. Results ROC analysis showed the VKP-IR (Max = 70; Cutoff = 28) to have good discriminatory abilities (AUC = 0.788) and the VKP-DR (Max = 70; Cutoff = 26;) to have excellent discriminatory abilities (AUC = 0.855). The classification tables showed that the VKP-IR had a PPP = 63.0% and NPP = 87.9%, while the VKP-DR had a PPP = 86.4% and NPP = 90.4%. Conclusion Due to the multifaceted nature of diagnosing NCDs, single-domain measures (i.e., procedural memory) will always struggle to obtain excellent PPP, as not all NCDs have amnestic cognitive profiles (e.g., cerebral vascular, Lewy body disease, etc.). Further research should explore comparing discriminatory abilities against other measures, increasing the sample size (both groups), and comparing discriminatory abilities among only NCDs with amnestic cognitive profiles, such as Alzheimer’s disease.

Method for determining reagent optimum volume for acid treatments of carbonate reservoirs based on a pressure build-up curve method

Relevance. The need to increase the efficiency of acid treatments of carbonate reservoirs. Often, the design of treatments does not take into account the actual operating conditions of production wells, which leads to an overestimation or underestimation of the required volume of the reagent. Aim. To propose a method for determining the optimal amount of reagent for carrying out acid treatments of carbonate reservoirs in order to restore the permeability of the rock in the near-well zone, taking into account the actual operating conditions of the well, which allows rationalizing the use of subsoil user resources. Objects. Producing oil wells exploiting oil deposits in carbonate deposits of the Perm region, as well as the results of hydrodynamic studies carried out on these wells. Methods. Theoretical substantiation of the possibility of determining the zone of deteriorated permeability around the well using the pressure recovery curve graph, graphical processing of diagnostic graphs of the pressure recovery curve, mathematical calculation of technological indicators based on hydrodynamic testing data of wells, integration of inflow volumes, analysis of technological data and conditions of applicability of the methodology. Results. The authors have developed the methodology for determining the optimal volume of reagent for acid treatments of carbonate reservoirs based on the pressure recovery curve method. The optimal volume of reagent for 21 production wells was calculated. Using the example of 13 wells, the efficiency of the injected volume of reagent was determined and an overestimated volume of reagent injected into the formation was demonstrated. The authors assessed the subsoil user's costs taking into account the excess consumption of the reagent. The factors influencing the success of the method application depending on the operating conditions of the wells were identified. Criteria inappropriateness of acid treatment were identified.

Visualization and Analysis of Three-Way Data Using Accumulated Concept Graphs

This paper introduces the Accumulated Concept Graph (ACG), a visualization tool based on the quantile method designed to analyze three-way data, including distributional data. Such data often have complex structures that make it difficult to identify patterns using conventional visualization techniques. The ACG represents each object with one or more monotonic line graphs. As a result, the three-way data are visualized as a set of parallel monotonic line graphs that never intersect. This non-intersecting property allows for the easy identification of both macroscopic and microscopic patterns within the data. We demonstrate the usefulness of ACGs and principal component analysis in the analysis of real three-way datasets.

Sustainable Development Interaction between Humans and the Interior Architecture

This research explores the evolution of sustainability in interior architectural environments by analysing four distinct settings: Egyptian, African, Japanese, and Chinese. These environments were chosen due to their climatic similarities and shared characteristics. The study examines representative dwelling models from each environment to understand how advanced sustainability can be tailored to each context. The research follows a structured approach, beginning with analysing human-environment interactions and the influence of culture on these dynamics. It delves into the evolution of interior architecture through various historical stages, considering factors like urban expansion, globalization, contemporary design, and technological advancements. The study also looks at the economic and social changes that have shaped interior architecture in these diverse environments. By creating a line graph illustrating these developments, the research highlights the progression of interior architecture across different historical periods. The findings suggest that sustainable development in interior architecture can be enhanced by drawing inspiration from ancient cultures. Traditional design elements, such as natural materials, symbolic colours, and energy-efficient layouts, offer valuable lessons for contemporary architects and designers. By integrating these time-tested techniques, modern interior architecture can achieve a balance between cultural heritage preservation and ecological sustainability, leading to designs that are harmonious with the environment.

Line game-perfect graphs

The $[X,Y]$-edge colouring game is played with a set of $k$ colours on a graph $G$ with initially uncoloured edges by two players, Alice (A) and Bob (B). The players move alternately. Player $X\in\{A,B\}$ has the first move. $Y\in\{A,B,-\}$. If $Y\in\{A,B\}$, then only player $Y$ may skip any move, otherwise skipping is not allowed for any player. A move consists of colouring an uncoloured edge with one of the $k$ colours such that adjacent edges have distinct colours. When no more moves are possible, the game ends. If every edge is coloured in the end, Alice wins; otherwise, Bob wins. The $[X,Y]$-game chromatic index $\chi_{[X,Y]}'(G)$ is the smallest nonnegative integer $k$ such that Alice has a winning strategy for the $[X,Y]$-edge colouring game played on $G$ with $k$ colours. The graph $G$ is called line $[X,Y]$-perfect if, for any edge-induced subgraph $H$ of $G$, \[\chi_{[X,Y]}'(H)=\omega(L(H)),\] where $\omega(L(H))$ denotes the clique number of the line graph of $H$. For each of the six possibilities $(X,Y)\in\{A,B\}\times\{A,B,-\}$, we characterise line $[X,Y]$-perfect graphs by forbidden (edge-induced) subgraphs and by explicit structural descriptions, respectively.

On rings whose prime ideal sum graphs are line graphs

Let [Formula: see text] be a commutative ring with unity. The prime ideal sum graph of the ring [Formula: see text] is the simple undirected graph whose vertex set is the set of all nonzero proper ideals of [Formula: see text] and two distinct vertices [Formula: see text], [Formula: see text] are adjacent if and only if [Formula: see text] is a prime ideal of [Formula: see text]. In this paper, we characterize all commutative Artinian rings whose prime ideal sum graphs are line graphs. Finally, we give a description of all commutative Artinian rings whose prime ideal sum graph is the complement of a line graph.

The Small Step Early Intervention Program for Infants at High Risk of Cerebral Palsy: A Single-Subject Research Design Study.

Background/Objectives: Early interventions for infants at high risk of cerebral palsy (CP) are recommended, but limited evidence exists. Our objective was, therefore, to evaluate the effects of the family-centered and interprofessional Small Step early intervention program on motor development in infants at high risk of CP (ClinicalTrials.gov: NCT03264339). Methods: A single-subject research design was employed to investigate participant characteristics (motor dysfunction severity measured using the Hammersmith Infant Neurological Examination (HINE) and Alberta Infant Motor Scale (AIMS) at three months of corrected age (3mCA) related to intervention response. The repeated measures Peabody Developmental Motor Scales-2 fine and gross motor composite (PDMS2-FMC and -GMC) and Hand Assessment for Infants (HAI) were analyzed visually by cumulative line graphs, while the Gross Motor Function Measure-66 (GMFM-66) was plotted against reference percentiles for various Gross Motor Function Classification System (GMFCS) levels. Results: All infants (n = 12) received the Small Step program, and eight completed all five training steps. At two years of corrected age (2yCA), nine children were diagnosed with CP. The children with the lowest HINE < 25 and/or AIMS ≤ 6 at 3mCA (n = 4) showed minor improvements during the program and were classified at GMFCS V 2yCA. Children with HINE = 25-40 (n = 5) improved their fine motor skills during the program, and four children had larger GMFM-66 improvements than expected according to the reference curves but that did not always happen during the mobility training steps. Three children with HINE = 41-50 and AIMS > 7 showed the largest improvements and were not diagnosed with CP 2yCA. Conclusions: Our results indicate that the Small Step program contributed to the children's motor development, with better results for those with an initial higher HINE (>25). The specificity of training could not be confirmed.

The eigenvalue multiplicity of line graphs

Let m(G,λ), c(G) and p(G) be the multiplicity of an eigenvalue λ, the cyclomatic number and the number of pendant vertices of a connected graph G, respectively. Yang et al. (2023) [10] proved that m(L(T),λ)≤p(T)−1 for any tree T, and characterized all trees T with m(L(T),λ)=p(T)−1, where L(T) is the line graph of T. In this paper, we extend their result from a tree T to any graph G≠Cn, and prove that m(L(G),λ)≤2c(G)+p(G)−1 for any graph G≠Cn. Moreover, all graphs G with m(L(G),−1)=2c(G)+p(G)−1 are completely characterized.

Are All Pictures Worth 1,000 Words? An Investigation of Fit Between Graph Type and Performance on Accounting Data Analytics Tasks

ABSTRACT Despite the increasing prevalence of the use of data analytics and visualizations in accounting settings, little is known about the relative effectiveness of different visualization formats for tasks such as those involving comparisons over time and those involving evaluations of whole-part compositions. In this study, we examine if there is a difference in decision efficiency and effectiveness between bar and line graphs for a comparison task and between pie and stacked column graphs for a compositional task. We also examine if the component complexity of graphs affects performance. We find that line graphs result in significantly greater decision sensitivity (ability to identify true positives) than bar graphs on comparison tasks regardless of the degree of component complexity. We also find that bar graphs result in significantly greater decision specificity (ability to identify true negatives) and overall accuracy.

Exploration on the corrosion inhibition performance of Salvia miltiorrhiza extract as a green corrosion inhibitor for Q235 steel in HCl environment

Salvia Miltiorrhiza, extensively distributed and commonly employed as a traditional Chinese medicinal herb, has garnered significant attention. In this study, the Salvia Miltiorrhiza extract (SME) was prepared by one-step water extraction method, and was firstly used as a novel corrosion inhibitor for Q235 steel in 1 mol/L HCl solution. As identified via liquid chromatography-mass spectrometry, the findings reveal that Salvianic acid A, tanshinone II A, danshenxinkun D, dihydrotanshinone, and methylene tanshinquinone are the primary constituents of SME. The optimum corrosion inhibition efficiency reached 92.8 % at 200 mg/L and maintained at 90.4 % after 72 h. Based on in-situ scanning vibration electrode technology (SVET) monitoring, the adsorption of inhibitor molecules on metal surface greatly retarded the propagation of localized corrosion. AFM examination of the corroded surface reveals that the samples supplemented with SME exhibit a smoother surface compared to the blank group. The force curve graph for the SME-added group demonstrates a more evenly distributed point array and an elevated average adhesion force, indicating that the addition of SME improves the corrosion resistance of the metal surface. XPS characterization illustrated that SME interacted with iron ions to form insoluble precipitate. This work investigated the application of SME as an eco-friendly corrosion inhibitor for carbon steel in acidic medium, providing a new approach for the high value-added utilization of Salvia Milliorrhiza.

Graph-based insider threat detection: A survey

Insider threat detection has been a significant topic in recent years. However, as network technology develops, the intranet becomes more complex. Therefore, simply matching attack patterns or using traditional machine learning methods (Logistic Regression, Gaussian-NB, Random Forest, etc.) does not work well. On the other hand, the graph structure can better adapt to intranet data, thus graph-based insider threat detection methods have become mainstream. In order to study the design and effectiveness of graph-based insider threat detection, in this paper, we conduct a systematic and comprehensive survey of existing related research. Specifically, we provide a framework and a taxonomy based on the detection process, classifying existing work from three aspects: data collection, graph construction, and graph anomaly detection. We conduct a quantitative analysis of existing representative graph methods and find that the models with more information have better performance. In particular, we discuss the scalability of existing methods to large-scale networks and their feasibility in real environments. Based on the survey results, we propose 7 pain points in this field and provide specific future research directions. Our survey will provide future researchers with a complete solution.