Wave energy resource assessment for the Indian shelf seas

As a renewable energy, the assessment of wave power potential around a country is crucial. Knowledge of the temporal and spatial variations of wave energy is required for locating a wave power plant. This study investigates the variations in wave power at 19 locations covering the Indian shelf seas using the ERA-Interim dataset produced by the European Centre for Medium-Range Weather Forecasts (ECMWF). The ERA-Interim data is compared with the measured wave parameters in the Arabian Sea and the Bay of Bengal. Along the western shelf seas of India, the seasonal oscillations lead to variation of the wave power from the lowest seasonal mean value (2.6 kW/m) in the post-monsoon period (October–January) to the highest value (25.9 kW/m) in the south-west monsoon (June–September) period. Significant (10–20%) inter-annual variations are detected at few locations. The mean annual wave power along the eastern Indian shelf seas (2.6–9.9 kW/m) is lower than the mean annual wave power along the western part (7.9–11.3 kW/m). The total annual mean wave power available along the western shelf seas of India is around 19.5 GW. Along the eastern shelf seas, it is around 8.7 GW. In the Indian Shelf seas, the annual mean wave power is highest (11.3 kW/m) at the southern location (location 11), and the seasonal variation in wave power is also less. Hence, location 11 is a better location for a wave power plant in the Indian shelf seas.     

Simulation of power extraction from tidal currents by flapping foil hydrokinetic turbines in tandem formation

In the present study the power extraction possibility by a number of flapping hydrofoils in tandem formation is investigated. A code is developed to predict power extraction capacity for the various number of flapping hydrofoils based on the kinematic and hydrodynamic models. The selected hydrodynamic model follows two dimensional quasi-steady hydrodynamic instability formulation. It is shown that the power extraction is also possible from water stream with the low Reynolds number. As a result of power extraction at low speed flows, the predicted maximum power efficiency is also in lower flapping frequencies. Furthermore, it is found that there are limited number of required flapping hydrofoils in tandem formation, in which the power influence rate drops notably after the second flapping hydrofoil. The flapping hydrofoils at downstream also experience higher hydrodynamic forces, while the flapping hydrofoil kinematics is the key parameter to harness extracted power. As a result of this investigation, the introduced model and code can be used as one of initial tools to predict power capacity for obtaining vast concept regarding tidal sites with the flapping foil hydrokinetic turbines.     

Kernelized correlation tracker on smartphones

This paper shows the implementation of a KC tracker (high-speed kernelized correlation tracker) on an Android smartphone. The image processing part is implemented with the Android-NDK in C/C++. Some parts of the tracking algorithm, which can be parallelized very well, are partitioned and calculated on the GPU with OpenGL ES and OpenCL. Other parts, such as the Discrete Fourier Transform (DFT), are calculated on the CPU (partly with the ARM-NEON features). With these hardware acceleration steps we could reach real-time performance (at least 20–30 FPS) on up-to-date smartphones, such as Samsung Galaxy S4, S5 or Google Nexus 5.Beyond that, we present some new color features and compare their tracking quality to the HOG features using the KC tracker and show that their tracking quality is mostly superior compared to the HOG features.If an object gets lost by the tracker which is the case e.g. if the object is totally hidden or outside the viewing range, there should be a possibility to perform a re-detection. In this paper, we show a basic approach to determine the tracking quality and search for the tracking object in the entire images of the subsequent video-frames.     

Morphological and biological characterization of density engineered foams fabricated by ultrasonic sonication

The successful manufacture of functionally tailored materials (e.g., density engineered foams) for advanced applications (e.g., structures or in bioengineering) requires an effective control over the process variables. In order to achieve this, density gradation needs to be represented and quantified. Current density measurement techniques offer information on bulk values, but neglect local position as valuable information (i.e., do not associate density scalar values with specific location, which is frequently critical when mechanical properties or functionalities have to be engineered). In this article, we present a method that characterizes the density gradation of engineered foams manufactured by the sonication technique, which allows the generation of sophisticated porous architectures beyond a simple linear gradient. A 3D data capture (μCT) and a flexible analysis software program (ImageJ) are used to obtain “global” density gradation values that can, ultimately, inform, control, and optimize the manufacture process. Polymeric foams, i.e., polyurethane (PU) foams, were used in this study as proof of concept. The measurements performed on the PU foams were validated by checking consistency in the results for both horizontal and vertical image slices. Biological characterization was done to assess the samples’ tailored structure viability as scaffolds for tissue engineering. The comparison between untreated and sonicated samples yielded a 12.7% of increment in living cell count adhered to the walls after treatment. The conclusions drawn from this study may inform the design and manufacture of density-engineered materials used in other fields (e.g., structural materials, optoelectronics, food technology, etc.)     

Poly(vinyl chloride) (PVC) hollow fibre membranes for gas separation

Poly(vinyl chloride) (PVC) gas separation hollow fibre membranes were produced from multicomponent dopes using dry/wet forced convection spinning. Membranes spun from a low polymer content solution exhibited disappointing gas separation properties. Their low selectivities were indicative of thick skins and high surface porosities. In contrast, high polymer content spun fibres showed good gas separation properties. Selectivities were high, active layers relatively thin and surface porosities moderate. Coating with poly(dimethylsiloxane) nullified the surface pores. The favourable performance of the high polymer content spun fibres was also related to shear rate and forced convection residence time during spinning. To the knowledge of the authors, this work represents the first reported success in producing PVC hollow fibre membranes with morphologies suitable for gas separation. The development of PVC hollow fibres relates to the ultimate quest to produce membranes capable of reliably separating oxygen and ozone gas mixtures.     

Ground object information extraction from hyperspectral remote sensing images using deep learning algorithm

Mining and utilizing coal resources play an influential role in economic development. In this regard, the feature information extraction in the area is researched to accurately and efficiently assist the production arrangement and deployment in the mining area. First, the detection ability of Hyperspectral Remote Sensing Image (HRSI) technology is analyzed. It has high spectral resolution and many bands. Specific bands can be extracted as needed to highlight target features. According to the characteristics of HRSIs, the data spectrum information and spatial information are comprehensively utilized, and the Convolutional Neural Network (CNN) based on deep learning is employed for feature extraction. CNN allows the machine to automatically obtain data features by learning and guide the classification of features. Taking the Liuyuan research area in Gansu as an example, three CNN models are used to extract and classify the ground features in the area. The VGG-19 model can provide the highest classification accuracy rate, reaching 87.3%; the VGG-16 model has the highest classification accuracy rate of the ground in the mining area, reaching 95.2%. ResNet model has the best effect on road classification. Then, the lithology classification is applied based on Thermal Airborne Hyperspectral Imager (TASI) data. The noise level of the first 20 bands is comparatively stable; afterward, it increases exponentially, showing a higher noise level, and the spectrum curve of the data after denoising becomes smoother. The end-member extraction method is employed to extract 25 end-member spectra of almost all lithology in the research area from the image. The similarity coefficient clustering analysis is employed to group the curves, which are divided into six categories in total. The separability of similar categories can be constrained by the objective function using the dictionary learning method, and the accuracy of the sparse representation of the category spectrum can be improved. The spectral matching method is used to subdivide each group of mapping results, suggesting that in the research area, granite is the most widely distributed, followed by diorite, andesite, and quartzite. Deep learning algorithms are applied to extract ground feature information, which is of great significance to the safety production in the mining area. The hyperspectral remote sensing rock and mineral thematic information extraction module is developed, which preliminarily realizes the quantitative acquisition and high-precision identification of typical mineral information, and provides technical support for the research of remote sensing geological evaluation technology of resource exploration in the new era.     

Corrosion of cemented carbide grades in petrochemical slurries. Part I - Electrochemical adsorption of CN¯, SCN¯ and MBT: A study based on in situ SFG

Owing to their unique combination of wear resistance and toughness, cemented carbides are developing relevance in a systematically increasing number of applications, many of which involve notably corrosive environments. Petrochemical offshore extraction is one of the most appealing prospective fields of use of cemented carbides for critical general-purpose components (e.g. pumps and valves), as well as specialised devices, such as drilling mud circulation systems, as they undergo extremely severe erosion-corrosion related to the handling of tetraphasic flows comprising condensed and gas-phase hydrocarbons in addition to sand or other solid components of slurries. Moreover, oilfields of current interest bear high concentrations of strongly corrosive impurities. The present study is aimed at contributing to the knowledge of the corrosion and corrosion inhibition processes of a range of alloyed Co- and Ni-based cemented carbide grades in solutions containing cyanide (CN¯) and thiocyanate (SCN¯) - typical corrosive contaminants found in crude oil - and 2-mercaptobenzothiazole (MBT), a prospective corrosion inhibitor in the petrochemical field. To this aim, we employed: (i) linear sweep voltammetry (LSV) to assess the cathodic and anodic activity of the hardmetal grades; (ii) in situ Sum-Frequency Generation (SFG) spectroscopy as a sensitive probe of the potential-dependent interfacial chemistry and corrosion behaviour in terms of both adsorption and interaction of adsorbates with the electronic structure of the substrate; (iii) ex situ spectro-ellipsometry to characterise the films formed on the different grades in the investigated environments. We have found that: (i) the grade with Co‐Ru binder exhibits a generally better corrosion resistance in all investigated ambients; (ii) Ni affords protective action provided the oxidising power is below a given threshold and CN¯ is absent, and (iii) MBT improves the pseudopassivation of all the investigated cemented carbides. Typical potential-dependent SFG spectral scenarios have been pinpointed for the different electrode/electrolyte combinations, accurately matching the electrochemical behaviour assessed by LSV and the optical properties of the corroded surfaces investigated by spectro-ellipsometry. This multi-method study offers a comprehensive and insightful understanding of the corrosion of cemented carbides in contact with additive-containing solutions.     

Using information communication technologies (ICTs) for motivating female adolescents to exercise/run in their leisure time

The aim of this study was to examine the effect of a 12-week intervention program on motivational climate in physical education (PE) lessons, on female adolescents' self-efficacy for independent training in leisure time, and on physical fitness measures. Participants were 154 female high school students divided into three groups: (1) received a program to enhance motivation for physical activity (PA) during PE lessons and in leisure time, using the internet; (2) followed the regular curriculum in addition to self-report at the end of each lesson, using smartphone application; (3) engaged in standard PE lesson activities. A self-efficacy for training questionnaire, a motivational climate questionnaire, and tests of physical fitness were administered. Findings indicated improvement pre-post in motivational climate and in the physical fitness measures, and a decline in the intensity of negative affect towards running, in all three groups. Incorporating innovative ways, and promoting self-accountability using self-documentation encourage students to exercise during their leisure time.     

A community-based approach to sharing knowledge before,during, and after crisis events: A case study from Thailand

This study adopts a structuration perspective to examine the knowledge sharing activities within local communities using social media to combat the 7-month 2011 Thai flood crisis using a qualitative case study. The crisis represented a unique situation wherein social media was used extensively during the most catastrophic flood crisis in Thailand. Data were collected from focus groups and in-depth interviews with flood victims, community leaders, NGOs, politicians, large enterprises, and Army leaders. The study divides the crisis event into three phases: pre-, during-, and post-crisis, treating each as both separate and interrelated, due to the changing information needs. The socialization and structuration theories were used as theoretical lenses to investigate how social media can play an important role in knowledge sharing activities in each phase of a crisis. The case study shows that social media can be adapted to fit the information and knowledge needs in each phase. This study's findings are useful and relevant for crisis managers, and clarify the potential usefulness of social media as a knowledge sharing tool during a crisis.     

Quality changes in chilled Norway lobster (Nephrops norvegicus) tail meat and the effects of delayed icing

The quality deterioration of Norway lobster (Nephrops norvegicus) tail meat was monitored during ice storage. The K‐value started at 0.7% and reached a value of 39.7% on day 14. Muscle pH followed a sigmoidal pattern that reached a plateau on day 6. Bacterial load and trimethylamine (TMA) increased only after a lag phase to reach considerable levels by day 14 (5.3 log cfu and 10.2 mg (100 g)^?1, respectively). These analytical data were compared with sensory data. Principal component analysis (PCA) indicated that laboratory measures were correlated positively with the smell strength of cooked product (increasingly strong) and negatively with the smell character of raw and cooked product (sour‐ammoniacal in raw and neutral in cooked products), flavour and aftertaste (both increasingly bland–bitter). The effects of icing delays on the quality of tail meat were also evaluated. Changes in K‐values, microbial load, muscle pH and TMA indicated that the delay to icing should be no more than 4 h (at 16 °C) to ensure that quality is not compromised during subsequent post‐harvest storage.