Automated classification of simulated wind field patterns from multiphysics ensemble forecasts

In this study, we have proposed an automated classification approach to identify meaningful patterns in wind field data. Utilizing an extensive simulated wind database, we have demonstrated that the proposed approach can identify low‐level jets, near‐uniform profiles, and other patterns in a reliable manner. We have studied the dependence of these wind profile patterns on locations (eg, offshore vs onshore), seasons, and diurnal cycles. Furthermore, we have found that the probability distributions of some of the patterns depend on the underlying planetary boundary layer schemes in a significant way. The future potential of the proposed approach in wind resource assessment and, more generally, in mesoscale model parameterization improvement is touched upon in this paper.     

Influence of Nano Silica on Mechanical and Durability Characteristic of Mortar made by Partial Replacement of Natural Fine Aggregate with Recycled Fine Aggregate

Silicon - To implement sustainability concepts in the construction industry, the possibility of utilizing the recycled fine aggregate obtained from the crushing of coarse aggregate debris of...     

A quantum mutation-based backtracking search algorithm

The exposition of any nature-inspired optimization technique relies firmly upon its executed organized framework. Since the regularly utilized backtracking search algorithm (BSA) is a fixed framework, it is not always appropriate for all difficulty levels of problems and, in this manner, probably does not search the entire search space proficiently. To address this limitation, we propose a modified BSA framework, called gQR-BSA, based on the quasi reflection-based initialization, quantum Gaussian mutations, adaptive parameter execution, and quasi-reflection-based jumping to change the coordinate structure of the BSA. In gQR-BSA, a quantum Gaussian mechanism was developed based on the best population information mechanism to boost the population distribution information. As population distribution data can represent characteristics of a function landscape, gQR-BSA has the ability to distinguish the methodology of the landscape in the quasi-reflection-based jumping. The updated automatically managed parameter control framework is also connected to the proposed algorithm. In every iteration, the quasi-reflection-based jumps aim to jump from local optima and are adaptively modified based on knowledge obtained from offspring to global optimum. Herein, the proposed gQR-BSA was utilized to solve three sets of well-known standards of functions, including unimodal, multimodal, and multimodal fixed dimensions, and to solve three well-known engineering optimization problems. The numerical and experimental results reveal that the algorithm can obtain highly efficient solutions to both benchmark and real-life optimization problems.

     

HSWOA: An ensemble of hunger games search and whale optimization algorithm for global optimization

The search for food stimulated by hunger is a common phenomenon in the animal world. Mimicking the concept, recently, an optimization algorithm Hunger Games Search (HGS) has been proposed for global optimization. On the other side, the Whale Optimization Algorithm (WOA) is a commonly utilized nature-inspired algorithm portrayed by a straightforward construction with easy parameters imitating the hunting behavior of humpback whales. However, due to minimum exploration of the search space, WOA has a high chance of trapping into local solutions, and more exploitation leads it towards premature convergence. The concept of hunger from HGS is merged with the food searching techniques of the whale to lessen the inherent drawbacks of WOA. Two weights of HGS are adaptively designed for every whale using the respective hunger level for balancing search strategies. Performance verification of the proposed hunger search-based whale optimization algorithm (HSWOA) is done by comparing it with 10 state-of-the-art algorithms, including three very recently developed algorithms on 30 classical benchmark functions. Comparison with some basic algorithms, recently modified algorithms, and WOA variants is performed using IEEE CEC 2019 function set. Statistical performance of the proposed algorithm is verified with Friedman's test, boxplot analysis, and Nemenyi multiple comparison test. The operating speed of the algorithm is determined and tested with complexity analysis and convergence analysis. Finally, seven real-world engineering problems are solved and compared with a list of metaheuristic algorithms. Numerical and statistical performance comparison with state-of-the-art algorithms confirms the efficacy of the newly designed algorithm.     

Physicochemical changes occurring during post-harvest hardening of trifoliate yam (Dioscorea dumetorum) tubers

Processing of Dioscorea dumetorum tubers into flour could be a means of adding a longer-term value to this tropical plant with a high nutritional potential but which presents a post-harvest hardening problem. This study was carried out in order to investigate the effect of storage under prevailing tropical ambient conditions (19–28 °C, RH 60–85%) for 56 days on the physicochemical characteristics of flours produced from hardened tubers. With the exception of bulk density, the results showed that all the physicochemical properties measured (water absorption capacity, oil absorption capacity, water solubility index, hydrophilic–lipophilic index, swelling capacity and least gelatinising concentration) were significantly influenced by tuber storage time (P<0.05). In general, the physicochemical indices increased with storage in at least two phases, from days 2 to 21 and from days 28 to 56. Since sprouting of most tubers was observed after 28 days of storage, the results suggest that post-harvest hardening and sprouting influence the above-mentioned indices of flours produced from D. dumetorum tubers.     

Mesoscale modeling of coastal low‐level jets: implications for offshore wind resource estimation

Detailed and reliable spatiotemporal characterizations of turbine hub height wind fields over coastal and offshore regions are becoming imperative for the global wind energy industry. Contemporary wind resource assessment frameworks incorporate diverse multiscale prognostic models (commonly known as mesoscale models) to dynamically downscale global‐scale atmospheric fields to regional‐scale (i.e., spatial and temporal resolutions of a few kilometers and a few minutes, respectively). These high‐resolution model solutions aim at depicting the expected wind behavior (e.g., wind shear, wind veering and topographically induced flow accelerations) at a particular location. Coastal and offshore regions considered viable for wind power production are also known to possess complex atmospheric flow phenomena (including, but not limited to, coastal low‐level jets (LLJs), internal boundary layers and land breeze–sea breeze circulations). Unfortunately, the capabilities of the new‐generation mesoscale models in realistically capturing these diverse flow phenomena are not well documented in the literature. To partially fill this knowledge gap, in this paper, we have evaluated the performance of the Weather Research and Forecasting model, a state‐of‐the‐art mesoscale model, in simulating a series of coastal LLJs. Using observational data sources we explore the importance of coastal LLJs for offshore wind resource estimation along with the capacity to which they can be numerically simulated. We observe model solutions to demonstrate strong sensitivities with respect to planetary boundary layer parameterization and initialization conditions. These sensitivities are found to be responsible for variability in AEP estimates by a factor of two. Copyright © 2013 John Wiley & Sons, Ltd.     

Technical note: Lineament detection on Mount Cameroon during the 1999 volcanic eruptions using Landsat ETM

A study of the active Mount Cameroon volcano, located south west along the Cameroon Volcanic Line, was carried out during the 1999 volcanic eruption, using Landsat Enhanced Thematic Mapper (ETM) imagery. It investigated geologic deformations expressed as faults, cracks and lineaments resulting from the eruption. Various image processing techniques were tested and compared in order to detect the most effective output. Principal Component Analysis was found to be useful to determine the extent of deformations caused during the 1999 volcanic eruptions. Landsat ETM revealed the formation of faults and lineaments with proximity to eruption vents. The interpretation obtained from satellite imagery was cross checked with field and ground truthing data. This paper identifies the dimensions and orientations of the newly formed geologic features.     

Innovative Utilization of Improved n-doped μc-SiOx:H Films to Amplify the Performance of Micromorph Solar Cells

Silicon - Due to in-situ deposition process doped SiOx material attracts the PV community as intermediate reflecting layer (IRL) for the less hazardous deposition process. Previously we have been...     

Machine learning approach to recognize ventricular arrhythmias using VMD based features

The occurrence of life-threatening ventricular arrhythmias (VAs) such as Ventricular tachycardia (VT) and Ventricular fibrillation (VF) leads to sudden cardiac death which requires detection at an early stage. The main aim of this work is to develop an automated system using machine learning tool for accurate prediction of VAs that may reduce the mortality rate. In this paper, a novel method using variational mode decomposition (VMD) based features and C4.5 classifier for detection of ventricular arrhythmias is presented. The VMD model was used to decompose the electrocardiography (ECG) signals to extract useful informative features. The method was tested for ECG signals obtained from PhysioNet database. Two standard databases i.e. CUDB (Creighton University Ventricular Tachyarrhythmia Database) and VFDB (MIT-BIH Malignant Ventricular Ectopy Database) were considered for this work. A set of time–frequency features were extracted and ranked by the gain ratio attribute evaluation method. The ranked features are subjected to support vector machine (SVM) and C4.5 classifier for classification of normal, VT and VF classes. The best detection was obtained with sensitivity of 97.97%, specificity of 99.15%, and accuracy of 99.18% for C4.5 classifier with a 5 s data analysis window. These results were better than SVM classifier result having an average accuracy of 86.87%. Hence, the proposed method demonstrates the efficiency in detecting the life-threatening VAs and can serve as an assistive tool to clinicians in the diagnosis process.

     

High-yield production of graphene by liquid-phase exfoliation of graphite

Fully exploiting the properties of graphene will require a method for the mass production of this remarkable material. Two main routes are possible: large-scale growth or large-scale exfoliation. Here, we demonstrate graphene dispersions with concentrations up to approximately 0.01 mg ml(-1), produced by dispersion and exfoliation of graphite in organic solvents such as N-methyl-pyrrolidone. This is possible because the energy required to exfoliate graphene is balanced by the solvent-graphene interaction for solvents whose surface energies match that of graphene. We confirm the presence of individual graphene sheets by Raman spectroscopy, transmission electron microscopy and electron diffraction. Our method results in a monolayer yield of approximately 1 wt%, which could potentially be improved to 7-12 wt% with further processing. The absence of defects or oxides is confirmed by X-ray photoelectron, infrared and Raman spectroscopies. We are able to produce semi-transparent conducting films and conducting composites. Solution processing of graphene opens up a range of potential large-area applications, from device and sensor fabrication to liquid-phase chemistry.