Scaling High-Quality Pairwise Link-Based Similarity Retrieval on Billion-Edge Graphs

SimRank is an attractive link-based similarity measure used in fertile fields of Web search and sociometry. However, the existing deterministic method by Kusumoto et al. [ 24 ] for retrieving SimRank does not always produce high-quality similarity results, as it fails to accurately obtain diagonal correction matrix D . Moreover, SimRank has a “connectivity trait” problem: increasing the number of paths between a pair of nodes would decrease its similarity score. The best-known remedy, SimRank++ [ 1 ], cannot completely fix this problem, since its score would still be zero if there are no common in-neighbors between two nodes. In this article, we study fast high-quality link-based similarity search on billion-scale graphs. (1) We first devise a “varied- D ” method to accurately compute SimRank in linear memory. We also aggregate duplicate computations, which reduces the time of [ 24 ] from quadratic to linear in the number of iterations. (2) We propose a novel “cosine-based” SimRank model to circumvent the “connectivity trait” problem. (3) To substantially speed up the partial-pairs “cosine-based” SimRank search on large graphs, we devise an efficient dimensionality reduction algorithm, PSR # , with guaranteed accuracy. (4) We give mathematical insights to the semantic difference between SimRank and its variant, and correct an argument in [ 24 ] that “if D is replaced by a scaled identity matrix (1-Ɣ)I, their top-K rankings will not be affected much”. (5) We propose a novel method that can accurately convert from Li et al. SimRank ~{S} to Jeh and Widom’s SimRank S . (6) We propose GSR # , a generalisation of our “cosine-based” SimRank model, to quantify pairwise similarities across two distinct graphs, unlike SimRank that would assess nodes across two graphs as completely dissimilar. Extensive experiments on various datasets demonstrate the superiority of our proposed approaches in terms of high search quality, computational efficiency, accuracy, and scalability on billion-edge graphs.

Avanços Recentes na Aplicação de um Método Numérico Espectronodal para Solução de Problemas Unidimensionais de Blindagem de Nêutrons

Neste trabalho, é mostrada a aplicação de um método da classe dos espectronodais (malha grossa) na solução de problemas de blindagem de nêutrons em geometria unidimensional nas formulações de ordenadas discretas e multigrupo de energia. Este método, denominado Modi ed Spectral Deterministic (MSD), representou uma modificação na estrutura de obtenção dos fluxos angulares de nêutrons emergentes no processo de varredura que foi utilizado no Método Espectral Deterministico (do inglês Spectral Deterministic Method, SDM). São apresentados os resultados numéricos para 3 problemas-modelo, com diferentes dimensões, número de grupos de energia, grau de anisotropia no fenômeno de espalhamento e tipos de condição de contorno, usando um aplicativo computacional desenvolvido na linguagem de programação C++.

Mycotoxins occurrence in medicinal herbs dietary supplements and exposure assessment

The multimycotoxin analysis of aflatoxins (AFs), zearalenone (ZEA), ochratoxin A (OTA), enniatins (ENNs) and beauvericin (BEA) was performed in 85 samples of medicinal herbs dietary supplements. The samples were classified in 64 samples of one herbal ingredient and 21 mixed samples. The extraction was performed by QuEChERS method and the determination by liquid chromatography coupled to ion-trap tandem mass spectrometry (LC–MS/MS-IT). Then, the risk characterization to mycotoxins through the consumption of medicinal herbs dietary supplements was assessed. The results showed that ZEA, OTA, ENNs and BEA showed in the samples with incidences between 1 and 34%, being ENNB the most detected mycotoxin. Mycotoxins contents ranged from LOQ to 3850.5 µg/kg while the mean of positives samples were 65.5 µg/kg (ENNA), 82.7 µg/kg (ENNA1), 88.7 µg/kg (ENNB), 324.9 µg/kg (ENNB1), 137.9 µg/kg (BEA) and 1340.11 µg/kg (ZEA), respectively. OTA was detected in one herbal mix tablet for insomnia at concentration of 799 μg/kg. In herbal drugs the European Pharmacopoeia Commission has implemented limits of 2 µg/kg for AFB1 and 4 µg/kg for total AFs. In the present study AFs have not been detected in the analyzed medicinal herbs dietary supplements. The Estimated Daily Intakes (EDIs) values were calculated using a deterministic method, considering two exposure scenarios (lower bound (LB) and upper bound (UB)). The values obtained were in general far below the Tolerable Daily Intakes (TDIs) established. Graphical abstract

Probabilistic Analysis of the Obstruction of Water Sources Due to the Occurrence of Rainfall-Triggered Mass Movements

This paper presents a methodology for the probabilistic estimation of the obstruction of water streams generated by shallow mass movements triggered by rainfall. The study focuses on the Ovejas River, a tributary stream of the Medellín River, in the jurisdiction of the municipality of San Vicente in the department of Antioquia (Colombia). The occurrence of a mass movements was evaluated by deterministic and probabilistic methods based on the automation of processes of Geographic Information Systems (GIS) and spatial modeling. The spatial distribution of the mass movement hazard was estimated in terms of Factor of Safety (FoS) values by the deterministic method with physical basis SLIDE (Slope - Infiltration - Distributed Equilibrium), which allows the hazard zonation by calculating a FoS for rainfall-induced mass movements with different return periods. The rainfall regimes of the study area are estimated by means of a simple scaling Log Normal Model. On the other hand, the Probability of Failure (PF) analysis was performed under Rosenblueth’s punctual estimates method (PEM), which allows incorporating the uncertainty of the soil parameters. Subsequently, the resulting zones with high hazard that could detach and reach the Ovejas River channel are identified as sources for runout modeling by means of the Flow R model, thus estimating the extent of mass movement in probabilistic terms. In all the analyzed scenarios, the sliding material from the critical stability zones has a high probability of spreading to the riverbed of the main river. This analysis makes possible to identify those areas of the riverbed that should be analyzed with more detail and require possible intervention for the protection of the riverbed.

Application of Deterministic Method for Landslide Susceptibility with Deep Learning

A method for estimating landslide susceptibility based on the analytic hierarchy process (AHP) was developed in 2017 as a deterministic method. The objective of this study is to verify the reliability of the proposed method by applying deep learning to improve the applicability of the method. The AHP-based deterministic method comprises eight factors: fines content, soil thickness, porosity, elastic modulus, shear strength, hydraulic conductivity, saturation, and water content. After dividing the testing area into 1 m square grids, eight factors were derived through field and laboratory experiments. The factor of safety was calculated based on the Mohr-Coulomb failure theory. Finally, the input and output values of deep learning were obtained. Bayesian regularization was applied among gradient descents to improve the learning efficiency when applying machine learning. The actual and predicted factors of safety were compared, and they showed excellent reliability in both the training and test phases. This study demonstrates that the AHP-based deterministic method with deep learning is valuable for determining landslide risk areas.

Service Life Evaluation for RC Sewer Structure Repaired with Bacteria Mixed Coating: Through Probabilistic and Deterministic Method

Since a concrete structure exposed to a sulfate environment is subject to surface ion ingress that yields cracking due to concrete swelling, its service life evaluation with an engineering modeling is very important. In this paper, cementitious repair materials containing bacteria, Rhodobacter capsulatus, and porous spores for immobilization were developed, and the service life of RC (Reinforced Concrete) structures with a developed bacteria-coating was evaluated through deterministic and probabilistic methods. Design parameters such protective coating thickness, diffusion coefficient, surface roughness, and exterior sulfate ion concentration were considered, and the service life was evaluated with the changing mean and coefficient of variation (COV) of each factor. From service life evaluation, more conservative results were evaluated with the probabilistic method than the deterministic method, and as a result of the analysis, coating thickness and surface roughness were derived as key design parameters for ensuring service life. In an environment exposed to an exterior sulfate concentration of 200 ppm, using the deterministic method, the service life was 17.3 years without repair, 19.7 years with normal repair mortar, and 29.6 years with the application of bacteria-coating. Additionally, when the probabilistic method is applied in the same environment, the service life was changed to 9.2–16.0 years, 10.5–18.2 years, and 15.4–27.4 years, respectively, depending on the variation of design parameters. The developed bacteria-coating technique showed a 1.47–1.50 times higher service life than the application of normal repair mortar, and the effect was much improved when it had a low COV of around 0.1.

Data Analysis and Forecasting of COVID-19 Pandemic in Kuwait

The first COVID 19 case of Kuwait was announced on 24th February, 2020 and the daily new cases increases exponentially since then until May, 2020 when the first wave started to decline. The same exponential dynamics has been observed between January and March, 2021. The forecast of new cases and death recorded daily is crucial so that health experts and citizens can be guided in order to avoid escalation of the pandemic. We propose a deterministic method to predict the basic reproduction number Ro of first and second wave of COVID-19 cases in Kuwait and also to forecast the daily new cases and death of the pandemic in the country. Forecasting has been done using ARIMA model, Exponential smoothing model, Holts method, Prophet forecasting model and machine learning models like log-linear, polynomial and support vector regressions. The results presented aligned with other methods used to predict Ro in first and second waves and the forecasting clearly shows the trend of the pandemic in Kuwait. The deterministic prediction of Ro is a good forecasting tool available during the exponential phase of the contagion, which shows an increasing trend during the beginning of the first and second waves of the pandemic in Kuwait.