TopCom: Index for Shortest Distance Query in Directed Graph

Finding shortest distance between two vertices in a graph is an important problem due to its numerous applications in diverse domains, including geo-spatial databases, social network analysis, and information retrieval. Classical algorithms (such as, Dijkstra) solve this problem in polynomial time, but these algorithms cannot provide real-time response for a large number of bursty queries on a large graph. So, indexing based solutions that pre-process the graph for efficiently answering (exactly or approximately) a large number of distance queries in real-time is becoming increasingly popular. Existing solutions have varying performance in terms of index size, index building time, query time, and accuracy. In this work, we propose T OP C OM , a novel indexing-based solution for exactly answering distance queries. Our experiments with two of the existing state-of-the-art methods (IS-Label and TreeMap) show the superiority of T OP C OM over these two methods considering scalability and query time. Besides, indexing of T OP C OM exploits the DAG (directed acyclic graph) structure in the graph, which makes it significantly faster than the existing methods if the SCCs (strongly connected component) of the input graph are relatively small

Blended E85-diesel fuel droplet heating and evaporation

The multidimensional quasi-discrete (MDQD) model is applied to the analysis of heating and evaporation of mixtures of E85 (85 vol % ethanol and 15 vol % gasoline) with diesel fuel, commonly known as “E85–diesel” blends, using the universal quasi-chemical functional group activity coefficients model for the calculation of vapor pressure. The contribution of 119 components of E85–diesel fuel blends is taken into account, but replaced with smaller number of components/quasi-components, under conditions representative of diesel engines. Our results show that high fractions of E85–diesel fuel blends have a significant impact on the evolutions of droplet radii and surface temperatures. For instance, droplet lifetime and surface temperature for a blend of 50 vol % E85 and 50 vol % diesel are 23.2% and up to 3.4% less than those of pure diesel fuel, respectively. The application of the MDQD model has improved the computational efficiency significantly with minimal sacrifice to accuracy. This approach leads to a saving of up to 86.4% of CPU time when reducing the 119 components to 16 components/quasi-components without a sacrifice to the main features of the model

Neural‑Brane: Neural Bayesian Personalized Ranking for Attributed Network Embedding

Network embedding methodologies, which learn a distributed vector representation for each vertex in a network, have attracted considerable interest in recent years. Existing works have demonstrated that vertex representation learned through an embedding method provides superior performance in many real-world applications, such as node classification, link prediction, and community detection. However, most of the existing methods for network embedding only utilize topological information of a vertex, ignoring a rich set of nodal attributes (such as user profiles of an online social network, or textual contents of a citation network), which is abundant in all real-life networks. A joint network embedding that takes into account both attributional and relational information entails a complete network information and could further enrich the learned vector representations. In this work, we present Neural-Brane, a novel Neural Bayesian Personalized Ranking based Attributed Network Embedding. For a given network, Neural-Brane extracts latent feature representation of its vertices using a designed neural network model that unifies network topological information and nodal attributes. Besides, it utilizes Bayesian personalized ranking objective, which exploits the proximity ordering between a similar node pair and a dissimilar node pair. We evaluate the quality of vertex embedding produced by Neural-Brane by solving the node classification and clustering tasks on four real-world datasets. Experimental results demonstrate the superiority of our proposed method over the state-of-the-art existing methods

The potential of Empty Fruit Bunch (EFB) biochar produced from Modification of Gas Burner (MGB) as a soil amendment at different temperature level as compared to Top Lid Updraft (TLUD) and muffle furnace heating / Syed Ahmad Ibrahim Al-Kired S. Hasan

Top-Lid Updraft (TLUD) stove and muffle furnace heating is one of the methods to produce biochar trough slow pyrolysis process. However, there is a limitation involve the TLUD stove and muffle furnace heating which are poorly designed may result in high gas emission and more costing in term of operation and maintenance respectively. The new method of slow pyrolysis to produce biochar which is “Modification of Gas Burner (MGB)” has innovated to overcome the problem. The purposes of this study were to investigate the physco-chemical properties of Empty Fruit Bunch (EFB) biochar produce from MGB at three different temperatures and to compare their properties with TLUD stove and muffle furnace heating. Sample of EFB used in this study was collected from Serting Hillir Oil Palm Mill, Negeri Sembilan, Malaysia. Biochar from dried EFB was produce from MGB at three different range of temperature and different type of slow pyrolysis process (MGB, TLUD stove and muffle furnace). The experiment outcome show the increasing temperature of biochar produced from MGB will increase the total ash, pH and EC value of the biochar. For the three different type of pyrolysis process, the results showed that biochar produce form MGB, TLUD stove and muffle furnace heating has high in pH ranging from 9.1 to 10.4. Between these three method , TLUD stove has a lower ash content, pH and EC value, phosphorus and potassium compared to MGB400 and muffle furnace heating. It is was found that EFB biochar that produced from MGB400 which is compatible with muffle furnace in term of physical and chemical properties has a potential as a new soil amendment with low cost of production

The level of offenses against archaeological and heritage resources, one year after implementation of the new Palestinian antiquities law

This article analyzes the cases related to offenses against cultural heritage property registered by the public prosecution courts throughout the West Bank, focusing on those registered during the first year after the new antiquities law took effect. The article presents some information on the global phenomenon of antiquities looting and the trafficking in antiquities; among others

EEG-based image classification using an efficient geometric deep network based on functional connectivity

To ensure that the FC-GDN is properly calibrated for the EEG-ImageNet dataset, we subject it to extensive training and gather all of the relevant weights for its parameters. Making use of the FC-GDN pseudo-code. The dataset is split into a "train" and "test" section in Kfold cross-validation. Ten-fold recommends using ten folds, with one fold being selected as the test split at each iteration. This divides the dataset into 90% training data and 10% test data. In order to train all 10 folds without overfitting, it is necessary to apply this procedure repeatedly throughout the whole dataset. Each training fold is arrived at after several iterations. After training all ten folds, results are analyzed. For each iteration, the FC-GDN weights are optimized by the SGD and ADAM optimizers. The ideal network design parameters are based on the convergence of the trains and the precision of the tests. This study offers a novel geometric deep learning-based network architecture for classifying visual stimulation categories using electroencephalogram (EEG) data from human participants while they watched various sorts of images. The primary goals of this study are to (1) eliminate feature extraction from GDL-based approaches and (2) extract brain states via functional connectivity. Tests with the EEG-ImageNet database validate the suggested method's efficacy. FC-GDN is more efficient than other cutting-edge approaches for boosting classification accuracy, requiring fewer iterations. In computational neuroscience, neural decoding addresses the problem of mind-reading. Because of its simplicity of use and temporal precision, Electroencephalographys (EEG) are commonly employed to monitor brain activity. Deep neural networks provide a variety of ways to detecting brain activity. Using a Function Connectivity (FC) - Geometric Deep Network (GDN) and EEG channel functional connectivity, this work directly recovers hidden states from high-resolution temporal data. The time samples taken from each channel are utilized to represent graph signals on a topological connection network based on EEG channel functional connectivity. A novel graph neural network architecture evaluates users' visual perception state utilizing extracted EEG patterns associated to various picture categories using graphically rendered EEG recordings as training data. The efficient graph representation of EEG signals serves as the foundation for this design. Proposal for an FC-GDN EEG-ImageNet test. Each category has a maximum of 50 samples. Nine separate EEG recorders were used to obtain these images. The FC-GDN approach yields 99.4% accuracy, which is 0.1% higher than the most sophisticated method presently availabl

Neural network to investigate gaming addiction and its impact on health effects during the COVID-19 Pandemic

The Playing games become a serious issue and may have adverse effects on the quality of life of children. The research aims at identify in the factors and degree of influence which lead to gaming addiction and its impact on the quality of life of world children employing a comprehensive. Our method collects 2,526 children and adults’ data for five significant regions globally contain schools and universities in municipal and non-municipal areas. The research also aims to investigate the effect that gaming addiction has on the quality of life of children. Structural equation test and the (NNM) were uutilized to analyze the data. The results indicate some differences between boys and girls as to what factors lead to gaming addiction. The average Root Means Square Error (RMSE) of the neural network model is relatively low (.0103 for male training data and .0113 for male examining data, while for females it was .0103 for exercising data and .0104 for examining data), But gaming addiction was found to harm the life for both genders. Discussions comprising both academic as well as practical perspectives are also presented

Droplets heating and evaporation: an application to diesel-biodiesel fuel mixtures

The heating and evaporation of automotive fuel droplets are crucial to the design of internal combustion engines and to ensuring their good performance. Accurate modelling is essential to the understanding of these processes and ultimately improving engine design. The interest in fossil-biodiesel fuel blends has been mainly stimulated by depletion of fossil fuels and the need to reduce carbon dioxide emissions that contribute towards climate change. This paper presents an analytical investigation into the application of discrete component model for the heating and evaporation of multi-component fuel droplets to several blended diesel-biodiesel fuels. The model considers the contribution of all groups of hydrocarbons in diesel fuel and methyl esters in biodiesel fuels. The main features of new application to the analysis of blended-fuel droplets in engine-like conditions is described. The model is applied to several blends of diesel, combining 98 components of hydrocarbons, and 19 types biodiesel fuels, combining up to 17 species of methyl ester, considering the differences in their chemical levels of saturation, and thermodynamic and transport properties. One important finding is that some fuel blends, e.g. B5 (5% biodiesel fuel and 95% diesel fuel), can give almost identical droplet lifetimes to the one predicted for pure diesel fuel; i.e. such mixtures can be directly used in conventional diesel engines with minimal, or no, modification to the droplet break-up process