Impact of Climate Change on Future Flood Susceptibility: an Evaluation Based on Deep Learning Algorithms and GCM Model

Floods are common and recurring natural hazards which damages is the destruction for society. Several regions of the world with different climatic conditions face the challenge of floods in different magnitudes. Here we estimate flood susceptibility based on Analytical neural network (ANN), Deep learning neural network (DLNN) and Deep boost (DB) algorithm approach. We also attempt to estimate the future rainfall scenario, using the General circulation model (GCM) with its ensemble. The Representative concentration pathway (RCP) scenario is employed for estimating the future rainfall in more an authentic way. The validation of all models was done with considering different indices and the results show that the DB model is most optimal as compared to the other models. According to the DB model, the spatial coverage of very low, low, moderate, high and very high flood prone region is 68.20%, 9.48%, 5.64%, 7.34% and 9.33% respectively. The approach and results in this research would be beneficial to take the decision in managing this natural hazard in a more efficient way.

     

Using AR,MA, and ARMA Time Series Models to Improve the Performance of MARS and KNN Approaches in Monthly Precipitation Modeling under Limited Climatic Data

Precipitation is one of the most important components of the hydrologic cycle as it is required for multi-objective applications including flood estimation, drought monitoring, watersheds management, hydrology, agriculture, etc. Therefore, its estimation and modeling via a suitable method is a challenging task for hydrologists. The present study seeks to model monthly precipitation at two stations located in Iran. Two artificial intelligence (AI)-based models consisting of multivariate adaptive regression splines (MARS) and k-nearest neighbors (KNN) were used as the modeling techniques. In doing so, nine single-input scenarios under limited climatic data are implemented using minimum, maximum, and mean air temperatures, dew point temperature, station pressure, vapor pressure, relative humidity, wind speed, and antecedent precipitation data. The attained results illustrate that the performance of single MARS and KNN is relatively poor when modeling the monthly precipitation. Additionally, this study develops hybrid models to enhance the precipitation modeling through combining the MARS and KNN models with three diverse types of the time series (TS) models, namely autoregressive (AR), moving average (MA), and autoregressive moving average (ARMA). The most important justification for integrating the models applied is that the AI and TS-based models are respectively capable of modeling the non-linear and linear terms of the hydrological variables such as precipitation. It is therefore necessary to be considered both of the aforementioned terms in the modeling procedure. A performance comparison of the single and hybrid models denotes the higher accuracy of hybrid models than the single ones. However, the hybrid models generated by combining the KNN and the TS models used are the best-performing models.

     

A kinetic model for pyrolysis of petroleum residue under nonisothermal conditions

Kinetic study on pyrolysis of petroleum residue was carried out by an accurate Arrhenius type equation at heating rate of 0.5–30.0°C/min under nonisothermal conditions. The influence of some critical parameters including temperature, heating rate and activation energy on mass conversion was evaluated. The apparent activation energies for during the pyrolysis process were in the range of 198–361 kJ/mol at various mass conversions of 5–94%. The reaction temperature was introduced as the most important parameter for the improvement of mass conversion, compared to that of other parameters. The pyrolysis of petroleum residue was occurred in a broad temperature range, from 150–650°C, yielding 33 wt% unpyrolyzed residue. It also was found that an increase in heating rate has a minor impact in the process. Model predictions were compared with experimental data, which showed fully good agreement.     

A novel proposal for all optical 3 to 8 decoder based on nonlinear ring resonators

In this paper, a 3 to 8 optical decoder was proposed using nonlinear photonic crystal ring resonators. For realizing the 3 to 8 decoder, we combined seven 1 to 2 optical decoders. In the proposed structure, X, Y and Z serve as input ports. By combination of these ports, one can control which output port to be ON. The maximum time delay of the proposed structure is about 6?ps.     

Sweet lemon mechanical damage detection using image processing technique and UV radiation

Non-destructive measurement of qualitative parameters of agricultural produce is quite beneficial in the postharvest operations. The consequential effect of mechanical damage in citrus fruits is rarely visible in their appearance compared to other commodities. The purpose of this study was to propose a fast, non-destructive method for sweet lemon mechanical damage detection using image processing technique and UV radiation. For this purpose, 135 sweet lemons were tested based on a completely randomized factorial design. In order to examine mechanical damage, the independent variables included drop height, fruit diameter and tempering period (holding time at room temperature after treatment). Fruits were dropped from heights of 2, 2.5 and 3 m onto the ground. Then images were captured under UV light having a wavelength of 365 nm, 1, 3 and 6 days after treatment. The images were sent to a PC and analyzed using MATLAB software. “Green Spot Index” or GSI was defined to show the extent of mechanical damage. Results of the analysis of variance showed that the percentage of green spots on fruit skin is significant at the 1% level considering the main and double interaction effects of drop height and fruit diameter. Green spot index significantly increases with the level of mechanical damage. Accuracy of the developed method in differentiating the bruised and undamaged fruits was found to be 100%.     

Dielectrophoretically assembled particles: feasibility for optofluidic systems

This work presents the dielectrophoretic manipulation of sub-micron particles suspended in water and the investigation of their optical responses using a microfluidic system. The particles are made of silica and have different diameters of 600, 450, and 250 nm. Experiments show a very interesting feature of the curved microelectrodes, in which the particles are pushed toward or away from the microchannel centerline depending on their levitation heights, which is further analyzed by numerical simulations. In doing so, applying an AC signal of 12 V_p–p and 5 MHz across the microelectrodes along with a flow rate of 1 μl/min within the microchannel leads to the formation of a tunable band of particles along the centerline. Experiments show that the 250 nm particles guide the longitudinal light along the microchannel due to their small scattering. This arrangement is employed to study the feasibility of developing an optofluidic system, which can be potentially used for the formation of particles-core/liquid-cladding optical waveguides.     

Phospholipids and oxophospholipids in atherosclerotic plaques at different stages of plaque development

We identified and quantified the hydroperoxides, hydroxides, epoxides, isoprostanes, and core aldehydes of the major phospholipids as the main components of the oxophospholipids (a total of 5–25 pmol/μmol phosphatidylcholine) in a comparative study of human atheroma from selected stages of lesion development. The developmental stages examined included fatty streak, fibrous plaque, necrotic core, and calcified tissue. The lipid analyses were performed by normal-phase HPLC with on-line electrospray MS using conventional total lipid extracts. There was great variability in the proportions of the various oxidation products and a lack of a general trend. Specifically, the early oxidation products (hydroperoxides and epoxides) of the glycerophosphocholines were found at the advanced stages of the plaques in nearly the same relative abundance as the more advanced oxidation products (core aldehydes and acids). The anticipated linear accumulation of the more stable oxidation products with progressive development of the atherosclerotic plaque was not apparent. It is therefore suggested that lipid infiltration and/or local peroxidation is a continuous process characterized by the formation and destruction of both early and advanced products of lipid oxidation at all times. The process of lipid deposition appears to have been subject to both enzymatic and chemical modification of the normal tissue lipids. Clearly, the appearance of new and disproportionate old lipid species excludes randomness in any accumulation of oxidized LDL lipids in atheroma.     

Concurrent control chart patterns recognition with singular spectrum analysis and support vector machine

Since abnormal control chart patterns (CCPs) are indicators of production processes being out-of-control, it is a critical task to recognize these patterns effectively based on process measurements. Most methods on CCP recognition assume that the process data only suffers from single type of unnatural pattern. In reality, the observed process data could be the combination of several basic patterns, which leads to severe performance degradations in these methods. To address this problem, some independent component analysis (ICA) based schemes have been proposed. However, some limitations are observed in these algorithms, such as lacking of the capability of monitoring univariate processes with only one key measurement, misclassifications caused by the inherent permutation and scaling ambiguities, and inconsistent solution. This paper proposes a novel hybrid approach based on singular spectrum analysis (SSA) and support vector machine (SVM) to identify concurrent CCPs. In the proposed method, the observed data is first separated by SSA into multiple basic components, and then these separated components are classified by SVM for pattern recognition. The scheme is suitable for univariate concurrent CCPs identification, and the results are stable since it does not have shortcomings found in the ICA-based schemes. Furthermore, it has good generalization performance of dealing with the small samples. Superior performance of the proposed algorithm is achieved in simulations.     

A hierarchical stochastic modelling approach for reconstructing lung tumour geometry from 2D CT images

ABSTRACT

Lung cancer is one of the deadliest cancers in both men and women. Nowadays, several methods are used to cure this cancer including surgery and radiotherapy. These methods require prior knowledge about the shape of tumours. This type of knowledge may also help physicians to determine the cancer type. In this paper we propose a novel approach for 3D reconstruction of tumour geometry from a sequence of 2D images. The proposed approach consists of two phases: tumour segmentation from computed tomography (CT) images and 3D shape reconstruction. Segmentation is conducted using snake optimisation and Gustafson–Kessel clustering. For 3D reconstruction, first, we propose a new approach to interpolate some intermediate slices between original slices. Then, the well-known marching cubes algorithm is used for surface reconstruction. Eventually, we smoothen the surface using an explicit fairing algorithm. Experiments show that our new approach can highly improve the quality and the accuracy of the reconstructed tumour shape.