Line Graphs Research Articles

Conjugate Impacts of Linear Variable Thermal Conductivity and Thermal Radiation on Nonlinear Electrically Conducting Fluid

This study investigates the conjugate impacts of variable thermal conductivity and thermal radiation on nonlinear heat conducting fluid moving vertically through parallel channels. The dimensional governing equations were reduced to dimensionless partial differential equations and consequently transformed to ordinary differential equations. The resulting ordinary differential equations were solved using the homotopy perturbation method. The study aimed at discovering the possible effects of thermal radiation parameter , thermal conductivity parameter , temperature difference parameter and Magnetic parameter through the help of line graphs. It was found that, Velocity and temperature distributions were increasing functions of , while Velocity and temperature distributions are decreasing functions of

Development and validation of a novel nomogram to predict worsening of gastroesophageal reflux symptoms after laparoscopic sleeve gastrectomy using Lasso-logistic regression

Background Gastroesophageal reflux disease (GERD) is among the most common complications of bariatric surgery. This study aimed to analyse the risk factors affecting the worsening of GERD symptoms after laparoscopic sleeve gastrectomy (LSG), and to establish and validate a related nomogram model. Methods The study recruited 236 participants and randomly divided them into training and validation sets in a ratio of 7:3. LASSO regression technique was used to select the optimal predictive features, and multivariate logistic regression was used to construct the column line graphs. The performance of the nomogram was evaluated and validated by analyzing the area under the receiver operating characteristic (ROC) curve, calibration curve, and decision curve. Results In this study, Lasso-logistic regression was applied to select 5 predictors from the relevant variables, which were body mass index (BMI), diabetes, hiatal hernia, GERD, and triglyceride levels. These 5 predictor variables constructed a model with moderate predictive power, with an area under the ROC curve of 0.779 for the training set and 0.796 for the validation set. Decision curve analysis showed that in external validation, if the risk thresholds were between 4 and 98% and 14–95%, then the nomogram can be applied to the clinic. Conclusions We have developed and validated a nomogram that effectively predicts the risk of worsening gastroesophageal reflux symptoms following LSG.

Mixed Convection Flow of a Viscous Dissipative and Heat Generating/Absorbing Fluid Through a Slit Microchannel

This paper is aimed at investigating the consequences of viscous dissipation and super-hydrophobicity on a magnetized mixed convection flow of an electrically conducting fluid across an up facing microchannel influenced by a transverse magnetic field. The plates were alternatively heated and incorporated with heat source/sink effect. The modeled governing equations have been obtained using a semi-analytical (regular perturbation) method. Various line graphs have been plotted to demonstrate the behavior of key parameter dictating the flow. It was found out that the thermal gradient and fluid velocity are significantly enhanced for mounting values of mixed convection Gre, Brickman number Br, Darcy porous number K and heat source S parameters in the superhydrophobic microchannel for constant pressure gradient Г. On the other hand, the velocity deteriorates for increasing levels of magnetic field M and heat sink S factors. Further, the comparison of this present analysis with previously published literature for limiting cases when Г=S=0 and Gre=1 showcased an excellent concurrence, thereby confirming the accuracy and validity of this present investigation. The findings from this research will find relevance in engineering, technological and industrial processes such as for solar collectors, nuclear reactors cooled during emergency shutdown, nuclear power plants, gas turbines and the various propulsion devices for aircraft, missiles, satellites and space vehicles.

Garbage prediction using regression analysis for municipal corporations of Indian cities

AbstractGarbage management is exceptionally critical and poses enormous environmental challenges. It has always been a vital issue in municipal corporations. However, municipal agencies have developed and used garbage management systems. Garbage forecasting still plays a crucial role in the management system and helps improve or create a garbage management system. This research examines the information from 212 cities to suggest a helpful regression model for garbage forecasting and control. To establish a connection between the variables, the descriptive study employs statistical techniques to learn about the composition of data collected from municipal corporations and conduct correlation analysis. Population and garbage depend highly on one another, as evidenced by their correlation coefficient of 0.922,144. The primary research is used to build an alternate hypothesis that shows the chosen variables are highly dependent on one another. The dataset is scaled and divided into a training and testing 80:20 ratio during the pre‐processing data phase. This research aims to do a regression analysis with daily garbage production, urban area, and population as independent variables. This research initiates a variety of regression models, including multiple linear regression (MLR), artificial neural network (ANN), decision tree regression (DTR), and random forest regression (RFR). The MLR model's R2 value of 0.85 indicates that it has the potential to accurately forecast daily garbage production based on just two independent variables and a single dependent variable. Random Forest Regression (RFR) with (MSE: 100,078.749 & MAE: 182.212) shows that it has the lowest MSE among all the models, which provides the most accurate predictions on average and the fit values of 8.85 and 316.23 obtained from the error distribution with a bin value 25. The estimated results from each model are compared to the test data values on line graphs and Taylor plots. The mean square error and the mean absolute error in the analysis and the Taylor plot show that the RFR model is best suited for predicting daily garbage production in a city. This research, therefore, provides a Random Forest model that is optimal for such challenges and is recommended for this class of problem.

POI VizNet: New QGIS Tool to construct Visibility Networks in Cities

Abstract. Network science and graph theory offer attractive models to describe urban phenomena and examine cases of urban planning and design. However, none of the existing network analysis tools consider simultaneously human visual perception and locations of urban activities. In this paper we introduce POI VizNet, a new QGIS plug-in that constructs various 2-dimentianal undirected graphs of unobstructed lines of sight. The plug-in integrates an increasing amount of available GIS-based data of Point Of Interest (POI) and visibility into one readily accessible analytical framework. Graphs are created between two types of decision points during urban travel – street intersections and POIs, origins, and destinations of travel, by connecting these potential observer’s decision locations in an open space between buildings. In addition, POI VizNet provides advanced options to build graphs using a predefined viewing distance and perceptual perspective. Visibility graphs are constructed and visualised as new layers in QGIS and delivered as network files suitable for further exploration, analysis, and visualisation in various network software packages.

Network routing on regular digraphs and their line graphs

Network routing on regular digraphs and their line graphs

Compositionally restricted atomistic line graph neural network for improved thermoelectric transport property predictions

Graph neural networks (GNNs) have evolved many variants for predicting the properties of crystal materials. While most networks within this family focus on improving model structures, the significance of atomistic features has not received adequate attention. In this study, we constructed an atomistic line GNN model using compositionally restricted atomistic representations which are more elaborate set of descriptors compared to previous GNN models, and employing unit graph representations that account for all symmetries. The developed model, named as CraLiGNN, outperforms previous representative GNN models in predicting the Seebeck coefficient, electrical conductivity, and electronic thermal conductivity that are recorded in a widely used thermoelectric properties database, confirming the importance of atomistic representations. The CraLiGNN model allows optional inclusion of additional features. The supplement of bandgap significantly enhances the model performance, for example, more than 35% reduction of mean absolute error in the case of 600 K and 1019 cm−3 concentration. We applied CraLiGNN to predict the unrecorded thermoelectric transport properties of 14 half-Heusler and 52 perovskite compounds, and compared the results with first-principles calculations, showing that the model has extrapolation ability to identify the thermoelectric potential of materials.

Automated pricing and replenishment forecasting for vegetable items

Firstly, the thesis extensively analysed and processed the vegetable sales data of a fresh food superstore. It integrated the product information and sales data of each vegetable category, and carried out data cleaning and analysis through Excel tools, including sorting out the outliers, listing the sales volume of each of the six categories, using pivot tables for statistical analysis, and establishing line graphs, heat maps, scatter plots, and other visual displays of the data characteristics and correlations. Secondly, the thesis investigated the relationship between sales volume and pricing, fitted the correlation between total sales volume and pricing using a random forest regression model, and predicted the daily replenishment volume for the coming week using an LSTM time series model. It also fitted quantitative fitting formulas for metrics for each vegetable category, classified profits into normal sales volume profits and discounted sales volume profits, and ultimately maximised profits through dynamic programming models and particle swarm optimisation algorithms. Finally, the thesis considered the impact of other factors such as weather, holidays, seasons and market environment on sales volume with specific data collection and analyses that help guide better pricing strategies. The findings will contribute to a better understanding of vegetable sales behaviour and optimise superstore operational strategies.

The influence of a non-uniform heat source/sink and Joule heating on the convective motion of a micropolar fluid in a chemically radiative MHD medium across a stretched sheet

The objective of the present exploration is to examine impactions of radiation, a non-uniform intensity source, and a permeable medium on a temperamental MHD blended convective micropolar liquid over an extended sheet subject to Joule heating. To transform the formulated problem into ordinary differential equations, the applicable similarity transformation is implemented. By utilizing R-K-F 4th -5th order approach with shooting method with MATLAB, the numerical solution is obtained. For the relevant profiles, the dimensionless parameters are visually displayed and described. Skin friction, the Nusselt number, and the Sherwood number have all been calculated using the answer found for the velocity, temperature, and concentration. With the assistance of line graphs, the impact of different flow factors being introduced into the problem is addressed. This research is conducted on the implications of MHD, porous, thermal radiation, viscous dissipation, Joule heating, non-liner thermal radiation and chemical reaction. For large values of micropolar parameter, the temperature is reduced and velocity and angular momentum distributions are raised. With the thermal radiation parameter, the temperature distribution gets better and thermal boundary layer is improved while the large values of Eckert number and non-uniform heat source or sink parameters, thermal boundary layer is improved. The higher thermal conductivity is proportional to the thickness of the thermal boundary layer. The concentration profile degrades with higher Schmidt number and chemical reaction parameter values. The current examination pertains to the significant subject matter of cooling of systems, artificial heart identification, oil-pipelined frictions, flow-tracers.

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.