Physicochemical and rheological parameters changes for determining the quality of surimi and kamaboko produced by conventional, acid and alkaline solubilization process methods from common kilka (Clupeonella cultriventris caspia)

Physicochemical properties of surimi and kamaboko obtained of solubility in acid, alkaline and conventional methods were compared. The results indicated that the highest protein recovery was related to solubility in acid, alkaline and conventional methods, respectively. The highest removal of lipid and myoglobin was observed by solubility in alkali. Excretion of total pigment and sulfhydryl groups was not significantly different between solubility in alkali and acid methods. Whiteness of surimi prepared by acid method was more than the other two methods. Electrophoresis pattern in surimi produced by conventional method indicated loss of myofibril and sarcoplasmic proteins through the washing process. Solubility in acid and alkali methods showed myofibril proteins recovery along a part of the sarcoplasmic proteins and disintegration of myosin heavy chain. Physically, study of kamaboko showed that solubility in alkali generated features such as gel strength, expressible moisture, hardness, gumminess and elasticity was superior to the other two methods. About folding test and cohesiveness factor, there was no significant difference between solubility in alkali and conventional methods. In general, solubility in alkali method was better

Comparison of different algorithms to map hydrothermal alteration zones using ASTER remote sensing data for polymetallic Vein-Type ore exploration: Toroud-Chahshirin Magmatic Belt (TCMB), north Iran

© 2019 by the authors. Polymetallic vein-type ores are important sources of precious metal and a principal type of orebody for various base-metals. In this research, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) remote sensing data were used for mapping hydrothermal alteration zones associated with epithermal polymetallic vein-type mineralization in the Toroud-Chahshirin Magmatic Belt (TCMB), North of Iran. The TCMB is the largest known goldfield and base metals province in the central-north of Iran. Propylitic, phyllic, argillic, and advanced argillic alteration and silicification zones are typically associated with Au-Cu, Ag, and/or Pb-Zn mineralization in the TCMB. Specialized image processing techniques, namely Selective Principal Component Analysis (SPCA), Band Ratio Matrix Transformation (BRMT), Spectral Angle Mapper (SAM) and Mixture Tuned Matched Filtering (MTMF) were implemented and compared to map hydrothermal alteration minerals at the pixel and sub-pixel levels. Subtle differences between altered and non-altered rocks and hydrothermal alteration mineral assemblages were detected and mapped in the study area. The SPCA and BRMT spectral transformation algorithms discriminated the propylitic, phyllic, argillic and advanced argillic alteration and silicification zones as well as lithological units. The SAM and MTMF spectral mapping algorithms detected spectrally dominated mineral groups such as muscovite/montmorillonite/illite, hematite/jarosite, and chlorite/epidote/calcite mineral assemblages, systematically. Comprehensive fieldwork and laboratory analysis, including X-ray diffraction (XRD), petrographic study, and spectroscopy were conducted in the study area for verifying the remote sensing outputs. Results indicate several high potential zones of epithermal polymetallic vein-type mineralization in the northeastern and southwestern parts of the study area, which can be considered for future systematic exploration programs. The approach used in this research has great implications for the exploration of epithermal polymetallic vein-type mineralization in other base metals provinces in Iran and semi-arid regions around the world

Exploring Unknown Universes in Probabilistic Relational Models

Large probabilistic models are often shaped by a pool of known individuals (a universe) and relations between them. Lifted inference algorithms handle sets of known individuals for tractable inference. Universes may not always be known, though, or may only described by assumptions such as "small universes are more likely". Without a universe, inference is no longer possible for lifted algorithms, losing their advantage of tractable inference. The aim of this paper is to define a semantics for models with unknown universes decoupled from a specific constraint language to enable lifted and thereby, tractable inference.Comment: Also accepted at the 9th StarAI Workshop at AAAI-2

Development of Sensitive Detection of Cryptosporidium and Giardia from Surface Water in Iran

Background: The protozoan parasites Cryptosporidium spp. and Giardia are known to occur widely in both raw and drinking waters. They are two of the causative agents of waterborne out-breaks of gastroenteritis throughout the world. In the present study, a PCR assay and FA were developed for detection of Cryptosporidium oocysts and Giardia cyst in environmental samples. Methods: We have detected Cryptosporidium spp. oocysts and Giardia cysts in seeded and un-seeded environmental water samples by PCR method. Water samples were spiked with oocysts (50, 100,300,500) and filtrated with a 1.2-µm pore size cellulose nitrate and follow by DNA extrac¬tion and purification by QIAamp DNA mini kit. Nested-PCR assay amplified an 850 bp fragment of 18s rRNA gene specific for Cryptosporidium and 435 bp fragment of glutamate dehydrogenase (GDH) target gene for Giardia. Also many river water from north of Iran, be checked by these methods. Results: Cryptosporidium and Giardia DNAs were detected in seeded water sample and Giardia was detected in all 5 water samples from river in north of Iran by nested- PCR and FA. Also in one river water sample, Cryptosporidium was detected.Conclusion: This protocol is effective for detection of these waterborne parasites in treated and untreated water samples. This study can also serve as a platform for further investigations and research water source in Iran

Frequency Selection to Improve the Performance of Microwave Breast Cancer Detecting Support Vector Model by Using Genetic Algorithm

This paper presents an innovative paradigm for breast cancer detection by leveraging a Support Vector Machine (SVM) based model fueled with numerical data obtained from the cutting-edge MammoWave device. Operating in the microwave spectrum between 1 to 9 GHz and boasting a 5 MHz sampling rate, MammoWave emerges as a groundbreaking solution, specifically addressing the limitations posed by conventional methods, particularly for women under 50. This technological advancement opens a promising avenue for more frequent and precise breast health monitoring. To enhance the efficacy of the SVM model, our research introduces a metaheuristic-based methodology, strategically navigating the selection of frequencies crucial for breast cancer detection within the MammoWave dataset. Overcoming the challenge of judicious frequency selection, our approach employs wrapper methods in metaheuristic algorithms. These algorithms iterate through subsets of frequencies, guided by the SVM model's performance, culminating in the identification of the optimal frequency subset that significantly refines precision in breast cancer detection. Moreover, a novel cost function is proposed to strike a balanced trade-off between sensitivity and specificity, ensuring an acceptable accuracy rate. The results exhibit a noteworthy 10% increase in specificity, a milestone achievement for the MammoWave device, yielding an overall detection rate of approximately 62%. This research underscores the potential of seamlessly integrating metaheuristic algorithms into frequency selection, thereby contributing significantly to the ongoing refinement of MammoWave's capabilities in breast cancer detection

A comparative evaluation of dried activated sludge and mixed dried activated sludge with rice husk silica to remove hydrogen sulfide.

The aim of this study was to investigate the effectiveness of dried activated sludge (DAS) and mixed dried activated sludge with rice husk silica (DAS & RHS) for removal of hydrogen sulfide (H2S). Two laboratory-scale filter columns (packed one litter) were operated. Both systems were operated under different conditions of two parameters, namely different inlet gas concentrations and different inlet flow rates. The DAS & RHS packed filter showed more than 99.96% removal efficiency (RE) with empty bed residence time (EBRT) of 45 to 90 s and 300 mg/L inlet concentration of H2S. However, the RE decreased to 96.87% with the EBRT of 30 s. In the same condition, the DAS packed filter showed 99.37% RE. Nonetheless, the RE was shown to have dropped to 82.09% with the EBRT of 30 s. The maximum elimination capacity (EC) was obtained in the DAS & RHS packed filter up to 52.32 g/m3h, with the RE of 96.87% and H2S mass loading rate of 54 g/m3h. The maximum EC in the DAS packed filter was obtained up to 44.33 g/m3h with the RE of 82.09% and the H2S mass loading rate of 54 g/m3h. After 53 days of operating time and 54 g/m3h of loading rates, the maximum pressure drop reached to 3.0 and 8.0 (mm H2O) for the DAS & RHS packed and DAS packed filters, respectively. Based on the findings of this study, the DAS & RHS could be considered as a more suitable packing material to remove H2S

Performance, kinetic, and biodegradation pathway evaluation of anaerobic fixed film fixed bed reactor in removing phthalic acid esters from wastewater

Emerging and hazardous environmental pollutants like phthalic acid esters (PAEs) are one of the recent concerns worldwide. PAEs are considered to have diverse endocrine disrupting effects on human health. Industrial wastewater has been reported as an important environment with high concentrations of PAEs. In the present study, four short-chain PAEs including diallyl phthalate (DAP), diethyl phthalate (DEP), dimethyl phthalate (DMP), and phthalic acid (PA) were selected as a substrate for anaerobic fixed film fixed bed reactor (AnFFFBR). The process performances of AnFFFBR, and also its kinetic behavior, were evaluated to find the best eco-friendly phthalate from the biodegradability point of view. According to the results and kinetic coefficients, removing and mineralizing of DMP occurred at a higher rate than other phthalates. In optimum conditions 92.5, 84.41, and 80.39% of DMP, COD, and TOC were removed. DAP was found as the most bio-refractory phthalate. The second-order (Grau) model was selected as the best model for describing phthalates removal