Applying Satellite Observations of Tropical Cyclone Internal Structures to Rapid Intensification Forecast With Machine Learning

Tropical cyclone (TC) intensity change is controlled by both environmental conditions and internal storm processes. We show that TC 24‐hr subsequent intensity change (DV24) is linearly correlated with the departures in satellite observations of inner‐core precipitation, ice water content, and outflow temperature from respective threshold values corresponding to neutral TCs of nearly constant intensity. The threshold values vary linearly with TC intensity. Using machine learning with the inner‐core precipitation and the predictors currently employed at the National Hurricane Center (NHC) for probabilistic rapid intensification (RI) forecast guidance, our model outperforms the NHC operational RI consensus in terms of the Peirce Skill Score for RI in the Atlantic basin during 2009–2014 by 37%, 12%, and 138% for DV24 ≥ 25, 30, and 35 kt, respectively. Our probability of detection is 40%, 60%, and 200% higher than the operational RI consensus, while the false alarm ratio is only 4%, 7%, and 6% higher

Identifying future research directions for biodiversity, ecosystem services and sustainability: perspectives from early-career researchers

We aimed to identify priority research questions in the field of biodiversity, ecosystem services and sustainability (BESS), based on a workshop held during the NRG BESS Conference for Early Career Researchers on BESS, and to compare these to existing horizon scanning exercises. This work highlights the need for improved data availability through collaboration and knowledge exchange, which, in turn, can support the integrated valuation and sustainable management of ecosystems in response to global change. In addition, clear connectivity among different research themes in this field further emphasizes the need to consider a wider range of topics simultaneously to ensure the sustainable management of ecosystems for human wellbeing. In contrast to other horizon scanning exercises, our focus was more interdisciplinary and more concerned with the limits of sustainability and dynamic relationships between social and ecological systems. The identified questions could provide a framework for researchers, policy makers, funding agencies and the private sector to advance knowledge in biodiversity and ES research and to develop and implement policies to enable sustainable future development

Streamlining Digital Modeling and Building Information Modelling (BIM) Uses for the Oil and Gas Projects

The oil and gas industry is a technology-driven industry. Over the last two decades, it has heavily made use of digital modeling and associated technologies (DMAT) to enhance its commercial capability. Meanwhile, the Building Information Modelling (BIM) has grown at an exponential rate in the built environment sector. It is not only a digital representation of physical and functional characteristics of a facility, but it has also made an impact on the management processes of building project lifecycle. It is apparent that there are many similarities between BIM and DMAT usability in the aspect of physical modeling and functionality. The aim of this study is to streamline the usage of both DMAT and BIM whilst discovering valuable practices for performance improvement in the oil and gas projects. To achieve this, 28 BIM guidelines, 83 DMAT academic publications and 101 DMAT vendor case studies were selected for review. The findings uncover (a) 38 BIM uses; (b) 32 DMAT uses and; (c) 36 both DMAT and BIM uses. The synergy between DMAT and BIM uses would render insightful references into managing efficient oil and gas’s projects. It also helps project stakeholders to recognise future investment or potential development areas of BIM and DMAT uses in their projects

Simultaneous kinetic determination of paracetamol and caffeine using Cu(II)-neocuproine in presence of dodecyl sulfate by H-point standard addition method

812-816A simple, feasible and selective kinetic spectrophotometric method for simultaneous determination of paracetamol and caffeine using H-point standard addition method is described. The method is based on difference in the rate of oxidation of the compounds with Cu(II)-neocuproine system and formation of Cu(I)–neocuproine complex, which is monitored at 453 nm and at pH 5.0 in the presence of sodium dodecyl sulfate. Experimental conditions such as pH, reagents concentrations, ionic strength and temperature have been optimized. Paracetamol and caffeine can be determined in the range 1.5-7.0 and 0.1-3.0 μg ml-1 respectively. The proposed method has been applied for the determination of paracetamol and caffeine in pharmaceutical samples with satisfactory results

One-shot learning of stochastic differential equations with data adapted kernels

We consider the problem of learning Stochastic Differential Equations of the form dXt = f(Xt)dt + σ(Xt)dWt from one sample trajectory. This problem is more challenging than learning deterministic dynamical systems because one sample trajectory only provides indirect information on the unknown functions f, σ, and stochastic process dWt representing the drift, the diffusion, and the stochastic forcing terms, respectively. We propose a method that combines Computational Graph Completion [46] and data adapted kernels learned via a new variant of cross validation. Our approach can be decomposed as follows: (1) Represent the time-increment map Xt → Xt+dt as a Computational Graph in which f, σ and dWt appear as unknown functions and random variables. (2) Complete the graph (approximate unknown functions and random variables) via Maximum a Posteriori Estimation (given the data) with Gaussian Process (GP) priors on the unknown functions. (3) Learn the covariance functions (kernels) of the GP priors from data with randomized cross-validation. Numerical experiments illustrate the efficacy, robustness, and scope of our method

Preconcentration and Determination of Zinc and Lead Ions by a Combination of Cloud Point Extraction and Flame Atomic Absorption Spectrometry

The phase-separation phenomenon of non-ionic surfactants occurring in aqueous solution was used for the extraction of lead(II) and zinc(II). After complexation with 3-[(4-bromophenyl) (1-H-inden-3-yl)methyl]-1 H-indene (BPIMI), the analytes were quantitatively extracted to a phase rich in Triton X-114 after centrifugation. Methanol acidified with 1 mol/L HNO(3) was added to the surfactant rich phase prior to its analysis by flame atomic absorption spectrometry (FAAS). The concentration of bis((1H-benzo [d] imidazol-2y)ethyl)sulfane, Triton X-114, pH and amount of surfactant were all optimized. Detection limits (3 SDb/m) of 2.5 and 1.6 ng/mL for Pb(2+) and Zn(2+) along with preconcentration factors of 30 and an enrichment factor of 32 and 48 for Pb(2+) and Zn(2+) ions were obtained, respectively. The proposed cloud point extraction was been successfully applied for the determination of these ions in real samples with complicated matrices such as food and soil samples, with high efficiency