Flow pattern and hydraulic performance of the REDAC Gross Pollutant Trap

This paper discusses the flow pattern and hydraulic performance of a Gross Pollutant Trap (GPT), designed and patented by River Engineering and Drainage Research Centre (REDAC) at Universiti Sains Malaysia. Stormwater problems have become more severe due to the increase in urbanization. The increase in the amount of impervious surface in urban areas produces more stormwater runoff, that is carried to the receiving bodies of water. The higher runoff volume also carries more pollutants (gross pollutants, sediments, and nutrients) from the contributing catchment area. Coarse sediments transported by stormwater runoff have negative effects on the receiving body of water and the aquatic environment by covering up aquatic habitats and clogging waterways. One of the challenges in designing a GPT for urban stormwater drainage is providing effective trapping without hindering the hydraulic function of the channel, thus, avoiding overspill or flooding. The current study presents a GPT design to meet these specific requirements of trapping efficiency and hydraulic function. The current GPT overcame the common problem of overspilling of gross pollutants in GPT by the introduction of additional overspill compartments that can handle excessive runoff and improve pollutant trapping in higher flow conditions. In laboratory testing, the prototype GPT was capable of achieving good trapping efficiency (over 80% for gross pollutants and over 60% for coarse sediments) without causing any overspill.     

L-band erbium-doped fibre amplifier with clamped- and flattened-gain using FBG

A gain clamped long wavelength band erbium-doped fibre amplifier (L-band EDFA) based on a ring laser cavity is demonstrated using a fibre Bragg grating (FBG) at the output end of the amplifier. This new design provides a good gain clamping as well as a gain flattening. The gain is clamped at 16.9 dB with gain variation of less than 0.1 dB from input signal power of -40 to -18 dBm by setting the VOA=5 dB. Also, the amplifier has the flattest gain spectrum at VOA=5. The gain variation is less than 1.0 dB within the wavelength range from 1570 to 1600 nm. This gain clamped amplifier also can support a 12 channel WDM system.     

Low-dose irradiation as a measure to improve microbiological quality of Turkish raw meat ball (cig kofte)

Raw meat ball is a traditional food, which is prepared by adding ground meat, spices and different supplements and consumed without any cooking process. The effect of low‐dose gamma irradiation application on microbiological quality of raw meat ball was examined. In this study, 1, 2, and 3 kGy irradiation doses have decreased or eliminated the microorganism counts in raw meat ball, parallel with the increased doses. Coliform bacteria counts were reduced under detectable value after application of 2 kGy irradiation doses; Staphylococcus aureus, sulphite‐reducing Clostridia, yeast and mould by the application of 3 kGy. It has been concluded that the low‐dose gamma irradiation applications have increased the hygienic quality of raw meat balls and possible public health risks can be prevented.     

Observations on the homogeneity of crosslinked copolymers prepared by γ-irradiation

Bulk copolymerization in vacuo has been effected to 100% conversion via γ-irradiation. Visual assessment is made of the homogeneity of the products, which comprise n-butyl acrylate with N-vinyl-2-pyrrolidone over the whole composition range. Textural inhomogeneity (popcorn structure or spongy opacity), produced only below ≈5 × 10^?3 Mrad h^?1, is attributed to proliferous polymerization. At medium-high dose rates, the optical homogeneity, classified as opacity, translucency or transparency, is dictated only by the composition. Thus, transparency in a xerogel requires a minimum n-butyl acrylate content of 40 wt%, but the threshold composition is reduced by incorporating a hexa-functional crosslinking agent. There is a general trend towards decreased optical homogeneity on hydrating a xerogel to its hydrogel. The change in compositional distribution up to 100% conversion is analysed according to the procedure of Skeist and is shown to be consistent with the experimental observations on compatibility.     

Automatic diagnosis of ureteral stone and degree of hydronephrosis with proposed convolutional neural network,RelieF, and gradient-weighted class activation mapping based deep hybrid model

Urinary system stone disease is a common disease group all over the world. Ureteral stones constitute 20% of all urinary system stones. Ureteral stones are important because they can cause hydronephrosis and related renal parenchymal damage in the kidneys. In the study, a hybrid model was developed to detect hydronephrosis and ureteral stones from kidney images. In the developed model, heat maps of the original images were obtained by using gradient-weighted class activation mapping (Grad-CAM) technology. Then, feature maps were extracted from both the original and heatmap datasets using the Efficientnetb0 architecture. Extracted feature maps were concatenated using a multimodal fusion technique. In this way, different features of an image are obtained. This has a positive effect on the performance of the model. The Relief dimension reduction technique was used to eliminate unnecessary features in the obtained feature map so that the proposed model can work faster and more effectively. Finally, the optimized feature map is classified in the support vector machine (SVM) classifier. To compare the performance of the proposed hybrid model, results were obtained with 8 state-of-the-art models accepted in the literature. Among these models, the highest accuracy value was achieved in the Efficientnetb0 architecture with 67.98%, whereas the accuracy of the proposed hybrid model was 91.1%. This value indicates that the proposed model can be used for HUN diagnosis.     

Forward and back substitution algorithms on GPU: a case study on modified incomplete Cholesky Preconditioner for three-dimensional finite difference method

Forward and back substitution algorithms are widely used for solving linear systems of equations after performing LU decomposition on the coefficient matrix. They are also essential in the implementation of high performance preconditioners which improve the convergence properties of the various iterative methods. In this paper, we describe an efficient approach to implementing forward and back substitution algorithms on a GPU and provide the implementation details of these algorithms on a Modified Incomplete Cholesky Preconditioner for the Conjugate Gradient (CG) algorithm. The resulting forward and back substitution algorithms are then used on a Modified Incomplete Cholesky Preconditioned Conjugate Gradient method to solve the sparse, symmetric, positive definite and linear systems of equations arising from the discretization of three dimensional finite difference ground-water flow models. By utilizing multiple threads, the proposed method yields speedups up to 60 times on GeForce GTX 280 compared to CPU implementation and up to 4.8 times speedup compared to cuSPARSE library function optimized for GPU by NVIDIA.