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Image segmentation is an essential pre-processing step and is an indispensable part of image analysis. This paper proposes Renyi's entropy multi-threshold image segmentation based on an improved Slime Mould Algorithm (DASMA). First, we introduce the diffusion mechanism (DM) into the original SMA to increase the population's diversity so that the variants can better avoid falling into local optima. The association strategy (AS) is then added to help the algorithm find the optimal solution faster. Finally, the proposed algorithm is applied to Renyi's entropy multilevel threshold image segmentation based on non-local means 2D histogram. The proposed method's effectiveness is demonstrated on the Berkeley segmentation dataset and benchmark (BSD) by comparing it with some well-known algorithms. The DASMA-based multilevel threshold segmentation technique is also successfully applied to the CT image segmentation of chronic obstructive pulmonary disease (COPD). The experimental results are evaluated by image quality metrics, which show the proposed algorithm's extraordinary performance. This means that it can help doctors analyze the lesion tissue qualitatively and quantitatively, improve its diagnostic accuracy and make the right treatment plan. The supplementary material and info about this article will be available at https://aliasgharheidari.com.

In this paper, a gradient-index lens is designed to implement an acoustic Bessel beam. Here, the wave from the point source is reshaped to the Bessel beam with the energy concentrated near the axial direction and almost no divergence. The two-dimensional distribution of the gradient refractive index is obtained based on the analysis of the impedance matching theory. The interface reflection is reduced significantly due to the impedance matching with air. The effect of the acoustic Bessel lens is found to work in a broadband with the use of the subwavelength unit cell and the finite-element simulations. Our results may provide the potential applications for medical ultrasound imaging and signal detection.In this paper, a gradient-index lens is designed to implement an acoustic Bessel beam. Here, the wave from the point source is reshaped to the Bessel beam with the energy concentrated near the axial direction and almost no divergence. The two-dimensional distribution of the gradient refractive index is obtained based on the analysis of the impedance matching theory. The interface reflection is reduced significantly due to the impedance matching with air. The effect of the acoustic Bessel lens is found to work in a broadband with the use of the subwavelength unit cell and the finite-element simulations. Our results may provide the potential applications for medical ultrasound imaging and signal detection.

With the worldwide development of nuclear power plant and requirement of saving energy and resource, high thermal efficiency and economical competitiveness are achieved by using supercritical COR2R with spe-cial thermal properties and better flow and heat transfer characters. In this paper, heat transfer of supercritical COR2R has been investigated in square and triangle array tube-bundle of cooled system in reactor using com-putational fluid dynamics (CFD) code FLUENT, and the basic knowledge of heat transfer of supercritical COR2R and first experience of CFD simulation are obtained. The effect of mesh structures, turbulence models, as well as flow channel size is analyzed. The choice of turbulence model adopted in simulating supercritical COR2R is recommended. Comparing the effect of heat transfer with supercritical COR2R and supercritical water as cooled medium, the results show that the former was higher. The new idea is provided for choice of cooled medium and improving thermal efficiency this paper.

Safety quality of pork meat is desired by costumers, who require freshness of meat to be maintained during shelf life in storage. Among different reasons, microorganisms are the most common cause for the spoilage of chilled pork during shelf life. Pseudomonas is one of the dominant bacteria species which multiplies during storage at 4 o C. The research was focused to develop optical technology based on hyperspectral imaging technology to detect spoilage of chilled pork meat rapidly and non-invasively. Fifty four pork meat samples were bought from local market, vacuum packed and stored in refrigerator at 4 o C. Hyperspectral scattering image in the range of 400nm to 1100nm were collected from 3 different samples on each day. After the collection of hyperspectral data, the microbiological characteristics such as Total Viable Counts (TVC) and Pseudomonas spp. were determined by standard method as reference value. The scattering profiles were fitted by four-parameter Gompertz function.Based on the hyperspectral data and reference value, prediction model was developed using support vector machines (SVM) regression. Moreover, a single prediction model for pork shelf life was established by a combination of TVC and growth of Pseudomonas spp. The model thus develop yielded regression coefficient of 0.93 for pork spoilage. The result obtained in the research demonstrates that the hyperspectral technology is precise and reliable method for rapid, non-invasive, real time detection of chilled pork quality during its shelf life.

Reflectance spectral data in the range of 350 nm to 1700 nm were collected in this research to investigate the potential application of VIS/NIR to develop rapid and non-destructive optical technology for assessing the water content of fresh pork. Two prediction models were developed using partial least squares regression (PLSR) and multiple linear regression (MLR) method. The results indicated that PLSR model based on compound pre-processing methods of median smooth filter (M-filter), multiplication scatter correction (MSC), standard normalized variant (SNV) and first derivative (FD) yielded the best result. Total 57 fresh pork samples were used for developing prediction model. Cross-validation correlation coefficient and cross-validation standard deviation were 0.90 and 0.50 respectively. The correlation coefficient of 0.81 and root mean square error of 0.70 were obtained in validation set. The research results demonstrate that the visible/near-infrared combined with PLSR modeling can well detect water content in fresh pork.

Pork freshness influences the consumers' health and has become a food safety issue. The quality of pork meat can be determined in terms of its freshness, which influences the purchasing decision of the meat by consumers. The pork quality in general is recognized by its appearance and smell, which is unscientific and subjective method. As such, real-time, non-destructive, rapid and accurate detection of pork freshness is current industrial desire. A multi-channel near-infrared spectroscopy in the wave length range of 300 to 2500nm was used to detect three parameters of pork freshness namely, Total Volatile Basic-Nitrogen (TVB-N), pH value and meat color. The system mainly included hardware system and software system. The hardware system mainly included the visible and near infrared spectroscopy system, signals transmission system and transfer system. Serial signal detection, spectra collecting, data saving and processing, model predicting was planted into the software system. A controlled mechanical system was fabricated to hold the detection device and acquire the NIR spectral data. A Partial Least Square Regression (PLSR) method was used to develop prediction model of pork freshness. The research shows that the multi-channel near-infrared can be a mile-stone for development of real-time, non-destructive, rapid and accurate detection technology for accessing pork quality.