Education•Winnipeg, Manitoba, Canada•
About: University of Manitoba is a(n) education organization based out in Winnipeg, Manitoba, Canada. It is known for research contribution in the topic(s): Population & Health care. The organization has 31888 authors who have published 66592 publication(s) receiving 2095493 citation(s).
Abstract: The path from perspective-taking to prosocial behavior is not as straightforward or robust as it is often assumed to be. In some contexts, imagining the viewpoint of other person leads the perspective taker to thoughts about how that person might have negative thoughts or intentions toward them. It can also prompt other kinds of counter-productive egocentric projection. In this review, we consider how prosocial processes potentially stimulated by perspective-taking can be derailed in such contexts. We also identify methodological limitations in current (social-) psychological evidence for a causal link between perspective-taking and prosocial outcomes. Increased appreciation of factors moderating the path from perspective-taking to prosocial behavior can enhance the explanatory power of perspective-taking as social cognitive process.
••31 Mar 2022
Abstract: Nowadays graphical datasets are having a vast amount of applications. As a result, graph mining—mining graph datasets to extract frequent subgraphs—has proven to be crucial in numerous aspects. It ...
Abstract: We discuss a fast approximate solution to the associated classical–classical orthogonal polynomial connection problem. We first show that associated classical orthogonal polynomials are solutions to a fourth-order quadratic eigenvalue problem with polynomial coefficients such that the differential operator is degree-preserving. Upon linearization, the discretization of this quadratic eigenvalue problem is block upper-triangular and banded. After a perfect shuffle, we extend a divide-and-conquer approach to the upper-triangular and banded generalized eigenvalue problem to the blocked case, which may be accelerated by one of a few different algorithms. Associated orthogonal polynomials arise from iterated Stieltjes transforms of orthogonal polynomials; hence, fast approximate conversion to classical cases combined with fast discrete sine and cosine transforms provides a modular mechanism for synthesis of singular integral transforms of classical orthogonal polynomial expansions.
Abstract: Structural Reliability analysis (SRA) is one of the prominent fields in civil and mechanical engineering. However, an accurate SRA in most cases deals with complex and costly numerical problems. Machine learning-based (ML) techniques have been introduced to the SRA problems to deal with this huge computational cost and increase accuracy. This paper presents a review of the development and use of ML models in SRA. The review includes the most common types of ML methods used in SRA. More specifically, the application of artificial neural networks (ANN), support vector machines (SVM), Bayesian methods and Kriging estimation with active learning perspective in SRA are explained, and a state-of-the-art review of the prominent literature in these fields is presented. Aiming towards a fast and accurate SRA, the ML techniques adopted for the approximation of the limit state function with Monte Carlo simulation (MCS), first/second-order reliability methods (FORM/SORM) or MCS with importance sampling well as the methods for efficiently computing the probabilities of rare events in complex structural systems. In this regard, the focus of the current manuscript is on the different models’ structures and diverse applications of each ML method in different aspects of SRA. Moreover, imperative considerations on the management of samples in the Monte Carlo simulation for SRA purposes and the treatment of the SRA problem as pattern recognition or classification task are provided. This review helps the researchers in civil and mechanical engineering, especially those who are focused on reliability and structural analysis or dealing with product assurance problems.
Abstract: Use of pesticides has drastically increased in many countries. Unfortunately, excessive administration of pesticides may cause adverse health and environment effects. The present study assessed pesticide residues in 5560 vegetable samples that entered the United Arab Emirates (UAE) via ports of the Dubai Emirate during 2018 and 2019. Pesticide residues in vegetables were determined by liquid chromatography in tandem with mass spectrometry (LC-MS/MS) and gas chromatography in tandem with mass spectrometry (GC-MS/MS). The presentation of the monitoring results was based on the maximum residue limit (MRL) criteria defined by European regulations for each pesticide in each product. A total of 79 different pesticides were identified in the tested samples. Pesticide residues at levels above the MRLs were detected in 30.5% of the samples. Overall, 15 pesticides (acephate, bifenthrin, carbendazim, chlorfenapyr, chlorpyrifos, deltamethrin, dimethoate, hexaconazole, metalaxyl, methamidophos, monocrotophos, omethoate, profenofos, tebuconazole, and triazophos) were detected above their MRLs in more than 50 samples. The results underline the need for continuous monitoring of pesticides in vegetables imported into the UAE.
Showing all 31888 results
|George Davey Smith||224||2540||248373|
|David A. Weitz||178||1038||114182|
|Robert E. W. Hancock||152||775||88481|
|Peter B. Jones||145||1857||94641|
|James J. Gross||139||529||100206|
|Steven J.M. Jones||137||594||146609|
|Jeff A. Sloan||129||656||65308|
|Dafna D. Gladman||129||1036||75273|
|Murray B. Stein||128||745||89513|
|Robert W. Heath||128||1049||73171|
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