Author(s): Adcock B; Brugiapaglia S; Dexter N; Moraga S;

The past decade has seen increasing interest in applying Deep Learning (DL) to Computational Science and Engineering (CSE). Driven by impressive results in applications such as computer vision, Uncertainty Quantification (UQ), genetics, simulations and image processing, DL is increasingly supplanting classical algorithms, and seems poised to revolutionize ...

Article GUID: 39454372

Author(s): Brusseau AJP; Feyten LEA; Crane AL; Brown GE;

No abstract available

Article GUID: 38476138

Author(s): Hou D; Zhan D; Wang L; Hassan IG; Sezer N;

Many factors contribute to the inherent uncertainty of energy consumption modeling in buildings. It is essential to perform a calibration and sensitivity analysis in order to manage these uncertainties. Despite the availability of several calibration methods, they are often deterministic and lack quantified uncertainties. Moreover, the selection of parame ...

Article GUID: 37936825

Author(s): He S; Rucker DD;

Uncertainty is an inherent part of consumers' environment. A large literature in marketing and related disciplines has found a positive relationship between uncertainty and information search: as consumers' uncertainty about a brand, product, or service increases, so does their inclination to seek out and engage with information. In contrast to th ...

Article GUID: 36471868

Author(s): Abdar M; Salari S; Qahremani S; Lam HK; Karray F; Hussain S; Khosravi A; Acharya UR; Makarenkov V; Nahavandi S;

The COVID-19 (Coronavirus disease 2019) pandemic has become a major global threat to human health and well-being. Thus, the development of computer-aided detection (CAD) systems that are capable of accurately distinguishing COVID-19 from other diseases using chest computed tomography (CT) and X-r ...

Article GUID: 36217534

Author(s): Zhu Y; Chen Z;

Source identification plays a vital role in implementing control measures for sudden river pollution incidents. In contrast to single-point source identification problems, there have been no investigations into inverse identification of multi-point emissions. In this study, an inverse model is developed based on the observed time series of pollutant conce ...

Article GUID: 36191500

Author(s): Chung S; Lee T; Hong Y; Ahmed O; Silva WAD; Gouin JP;

Introduction: The aims of this study were to examine the mediation effect of viral anxiety of healthcare workers on the influence of their intolerance of uncertainty on the adherence to physical distancing during the COVID-19 pandemic. Methods: An online survey was conducted among 329 healthcare workers (female: 81.4%, nursing professionals: 59.0%, and s ...

Article GUID: 35733798

Author(s): Morgan K; Collier ZA; Gilmore E; Schmitt K;

An emerging risk is characterized by scant published data, rapidly changing information, and an absence of existing models that can be directly used for prediction. Analysis may be further complicated by quickly evolving decision-maker priorities and the potential need to make decisions quickly as new information comes available. To provide a forum to dis ...

Article GUID: 35104915

Author(s): Sun X; Zhang X; Wang L; Li Y; Muir DCG; Zeng EY;

Deep convolutional neural network (DCNN) has proved to be a promising tool for identifying organic chemicals of environmental concern. However, the uncertainty associated with DCNN predictions remains to be quantified. The training process contains many random configurations, including dataset segmentation, input sequences, and initial weight, etc. Moreov ...

Article GUID: 34388923

Author(s): Cai M; An C; Guy C; Lu C; Mafakheri F;

As a volatile organic compound existing in the atmosphere, methanol plays a key role in atmospheric chemistry due to its comparatively high abundance and long lifetime. Croplands are a significant source of biogenic methanol, but there is a lack of systematic assessment for the production and emission of methanol from crops in various phases. In this stud ...

Article GUID: 34182392

Author(s): Aslani N; Kuzgunkaya O; Vidyarthi N; Terekhov D;

Tactical capacity planning is a key element of planning and control decisions in healthcare settings, focusing on the medium-term allocation of a clinic's resources to appointments of different types. One of the most scarce resources in healthcare is physician time. Due to uncertainty in demand for appointments, it is difficult to provide an exact mat ...

Article GUID: 33215335