Monitoring HABs in the shallow Arabian Gulf using a qualitative satellite-based index

In this study, a harmful algal bloom (HAB) index, commonly known as a red tide index (RI), was developed and tested using in situ measurements in the shallow Arabian Gulf where periodical occurrence of red tide has been reported. The index is based on a combination of ratio and difference of bands centred at 443, 531, and 547 nm. The index was applied to Moderate Resolution Imaging Spectroradiometer (MODIS)/Aqua imagery and compared with other proxies of HABs such as chlorophyll-a (chl-a) concentrations and fluorescence line height (FLH). Validation of the proposed index produced a positive correlation between the index and chlorophyll-a concentration with a determination coefficient (R²) of 0.61. Seasonality analysis of RI indicated that HABs in the Gulf region occur mainly in fall and winter. The inter-annual variations of RI showed similar patterns with the conventional chl-a product. This new index can provide insights into the dynamics of red tides and guidance for mitigation efforts, and assist in establishing an effective and timely forecasting and warning system for red tide.     

Maintainability defects detection and correction: a multi-objective approach

Software defects often lead to bugs, runtime errors and software maintenance difficulties. They should be systematically prevented, found, removed or fixed all along the software lifecycle. However, detecting and fixing these defects is still, to some extent, a difficult, time-consuming and manual process. In this paper, we propose a two-step automated approach to detect and then to correct various types of maintainability defects in source code. Using Genetic Programming, our approach allows automatic generation of rules to detect defects, thus relieving the designer from a fastidious manual rule definition task. Then, we correct the detected defects while minimizing the correction effort. A correction solution is defined as the combination of refactoring operations that should maximize as much as possible the number of corrected defects with minimal code modification effort. We use the Non-dominated Sorting Genetic Algorithm (NSGA-II) to find the best compromise. For six open source projects, we succeeded in detecting the majority of known defects, and the proposed corrections fixed most of them with minimal effort.     

Numerical prediction of flow and heat transfer of power-law fluids in a plane channel with a built-in heated square cylinder

Structure of unsteady laminar flow and heat transfer of power-law fluids in two-dimensional horizontal plane channel with a built-in heated square cylinder is studied numerically. The governing equations are solved using a control volume finite element method (CVFEM) adapted to the staggered grid. Computations are performed over a range of Reynolds and Richardson numbers from Re = 20 to 200 and from Ri = 0 to 8, respectively at fixed Prandtl number Pr = 50 and blockage ratio value β′ = 1/8. Three different values of the power-law index (n = 0.5, 1 and 1.4) are considered in this study to show its effect on the value of the critical Reynolds number defining the transition between two different flow regimes (symmetrical and periodic flows), the variations of Strouhal number, drag and lift coefficients and the heat transfer from the square cylinder as function of Reynolds number. Heat transfer correlations are obtained through forced convection. A discussion about the buoyancy effect on the flow pattern and the heat transfer for different power-law index is also presented.     

Small scale biodiesel synthesis from waste frying oil and crude methanol in Morocco

Biodiesel was produced at small scale by transesterification of used frying oil(UFO) recovered from Moroccan pastry shops and fish frying restaurants. Biodiesel was first synthesised at laboratory scale in order to optimize the transesterification parameters. The cost of the final product was also optimized using low-cost raw materials.The UFO and the produced biodiesel were characterized with several techniques including gas chromatography,1H NMR,13 C NMR, FTIR, and TGA–TDA techniques.1H NMR gas chromatographic analyses of the final product confirmed that the transesterification in the chosen experimental conditions was completed. These results were confirmed by TGA–TDA analysis used as new techniques to monitoring triglyceride conversion. The biodiesel did not contain any trace of glycerol, and it did meet the international standards. The transesterification at low cost in small scale conditions was performed at 60 °C using 1.2% of KOH and a methanol/oil molar ratio of 6:1. A yield of 80.8% was achieved. The properties of the produced biodiesel were found to be as good as those of biodiesels obeying to European standards. The biodiesel production was also performed at small-scale for individual utilisation. Thus, the product was tested in a kerosene stove for heating and non-modified commercial diesel engine producing electricity.     

Fast mode decision algorithm for H.264/SVC enhancement layer

Scalable video coding (SVC) has been standardized to extend the capabilities of the H.264 advanced video coding (AVC). The SVC can compress several video sequences of various resolutions as a single bit-stream. In the SVC enhancement layer, for Joint Scalable Video Model (JSVM) software implementation, an exhaustive mode decision process based on the base layer mode predictions is performed to obtain the best mode for each macroblock (MB). This technique may achieve a higher coding efficiency; however, it induces a significant computational complexity in the encoding engine. In order to speedup the SVC encoder, a fast mode decision algorithm was proposed in this paper. In other words, our aim was to decrease the number of candidate modes to reduce the computational complexity and maintain the same level of coding efficiency, this approach used the spatial and temporal correlation between MB situated at the enhancement layer and its co-located MB at the base layer. Our statistical analyses were made using several HD sequences with different motion characteristics. Experimental results show a significant improvement in terms of time encoding which is a major constraint for any real-time implementation. However, this gain is accompanied with an acceptable loss in video quality and a tolerable bit rate increase for most media supports. In fact, our proposed algorithm permits a major improvement that can reach up to 64.9 % in terms of computational effort. This gain will induce an average loss yield to 10.5 or 13.87 % that is comparable to the 13.12 % of the He Li’s algorithm with an acceptable loss in terms of subjective video quality.     

What you like in design use to correct bad-smells

Over the past decades, many techniques and tools have been developed to support maintenance activities in order to improve software quality. One of the most efficient ones is software refactoring to eliminate bad-smells. A majority of existing work propose “standard” refactoring solutions that can be applied by hand for each kind of defect. However, it is difficult to prove or ensure the generality of these solutions to any kind of bad-smells or software codes. In this paper, we propose an approach to correct bad-smells using well-designed code. We use genetic algorithms to generate correction solutions defined as a combination of refactoring operations that maximize, as much as possible, the similarity between the corrected bad-smells and examples of well-designed code. We report the results of an evaluation of our approach using four open-source projects. Our proposal achieved high correction scores by fixing the majority of expected bad-smells.     

Re-ranking Person Re-identification using Attributes Learning

Neural Computing and Applications - Person re-identification (Re-ID) across multi-camera views remains a very challenging task. In fact, the use of global convolutional neural network (CNN)...     

Example-based model-transformation testing

A major concern in model-driven engineering is how to ensure the quality of the model-transformation mechanisms. One validation method that is commonly used is model transformation testing. When using this method, two important issues need to be addressed: the efficient generation/selection of test cases and the definition of oracle functions that assess the validity of the transformed models. This work is concerned with the latter. We propose a novel oracle function for model transformation testing that relies on the premise that the more a transformation deviates from well-known good transformation examples, the more likely it is erroneous. More precisely, the proposed oracle function compares target test cases with a base of examples that contains good quality transformation traces, and then assigns a risk level to them accordingly. Our approach takes inspiration from the biological metaphor of immune systems, where pathogens are identified by their difference with normal body cells. A significant feature of the approach is that one no longer needs to define an expected model for each test case. Furthermore, the detected faulty candidates are ordered by degree of risk, which helps the tester inspect the results. The validation results on a transformation mechanism used by an industrial partner confirm the effectiveness of our approach.     

A Youla–Kucera parametrized adaptive feedforward compensator for active vibration control with mechanical coupling

Most of the adaptive feedforward vibration (or noise) compensation systems feature an internal ”positive feedback” coupling between the compensator system and the correlated disturbance measurement which serves as reference. This may lead to the instability of the system. Instead of the standard IIR structure for the adaptive feedforward compensator, the paper proposes a Youla–Kucera parametrization of the adaptive compensator. The central compensator assures the stability of the system and its performances are enhanced in real time by the direct adaptation of the Youla–Kucera parameters. Theoretical and experimental comparison with recent results obtained using an IIR adaptive feedforward compensator are provided.     

Sea-ice monitoring over the Caspian Sea using geostationary satellite data

A new technique is proposed for sea-ice mapping using observations from geostationary satellite over the Caspian Sea. A two end-member linear-mixture approach has been employed. A neural-network-based approach was used to simulate water and ice reflectances for all possible sun–satellite geometries. The ice-mapping technique incorporates an advanced cloud-detection algorithm with adaptive threshold values. The average percentage of cloud reduction because of the daily compositing ranged from 22% to 25%. Daily maps of ice distribution and concentration with minimal cloud coverage were produced for the winter seasons of 2007 and 2008. The retrieved ice distribution demonstrated a good agreement with Moderate Resolution Imaging Spectroradiometer (MODIS) images and National Oceanic Atmospheric Administration (NOAA) Interactive Multisensor Snow and Ice Mapping System (IMS) snow and ice charts. The obtained correlation coefficients with IMS charts for 2007 and 2008 were 0.92 and 0.83, respectively. The technique has been proposed as one of the candidate ice-mapping techniques for the future Geostationary Operational Environmental Satellite-R Series (GOES-R) Advance Baseline Imager (ABI) instrument.