Use of artificial neural network to evaluate the vibration mitigation performance of geofoam-filled trenches

Development activities in a city often generate ground vibration that can cause discomfort to the occupants in nearby buildings, disturbances to the activities undertaken in the buildings and possible damage to nearby structures. This ground vibration is caused by construction activities such as pile driving, ground compaction etc., and road and rail traffic. The use of trenches has been an effective way to mitigate the adverse effects of such ground vibration. The effectiveness of the trench depends on many parameters including the properties of the vibration source, soil medium and trench in-fill material, trench dimensions and the requirements of the receiver. The process of selecting an effective trench for vibration mitigation can therefore become complex due to the influence of a number of parameters and their wide range of values. This paper investigates the use of artificial neural network (ANN) as a smart and efficient tool to predict the effectiveness of geofoam-filled trenches to mitigate ground vibration. Towards this end, a database is developed from an extensive study on the effects of the controlling parameters through numerical simulations with a validated finite element (FE) model. At a certain distance from the vibration source, a geofoam-filled trench is introduced to evaluate the efficiency of vibration mitigation with changes in key parameters such as excitation frequency, amplitude of load, trench configuration (i.e. depth and width), soil shear wave velocity, soil density and damping ratio. These were selected as the input parameters for the ANN while amplitude reduction ratio and peak particle velocity (PPV) were considered as outputs. A multilayer feed forward network was used and trained with the Levenberg-Marquardt algorithm. Neural networks with different configurations were evaluated by comparing coefficient of determination (R²) and mean square error (MSE). The optimum architecture was then used to predict previous results, which revealed the accuracy and the effectiveness of the ANN approach. The findings of this study will provide useful information for vibration mitigation using geofoam-filed trenches.     

Utilization of adzuki bean extract as a natural antioxidant in cured and uncured cooked pork sausages

A commercial adzuki bean extract (AE) was evaluated for antioxidant effectiveness in cured and uncured cooked pork sausages. TBARS values, instrumental color evaluation and sensory panel scores were assessed. For uncured sausages, AE at 0.2% was equally effective as 0.1% butylated hydroxytoluene (BHT) in reducing TBARS values. Similarly, AE at 0.2% significantly (P<0.01) reduced the TBARS in cured sausages. Incorporation of 0.2% AE into sausages produced higher (P<0.05) CIE lab color a* value and lower (P<0.05) L* and b* values. Sensory panels did not detect any difference in color, odor, taste, flavor, and overall acceptance in uncured pork sausages with addition of 0.2% AE. However, there were adverse changes in the color and odor of cured sausages, even though the taste, flavor, and overall acceptance were similar. Therefore, the results suggest that AE is a potential antioxidant.     

Potato and soy peptides alter caecal fermentation and reduce serum non‐HDL cholesterol in rats fed cholesterol

We examined the effect of potato peptides (PPC) in rats fed a cholesterol‐enriched diet, in comparison with two cholesterol‐enriched diets containing soy peptides (SPC) or casein (CNC), and a cholesterol‐free diet containing casein (CN) for 4 weeks. The serum non‐high‐density lipoprotein (HDL) cholesterol level was lower in the PPC‐ (–18.39%) and SPC‐fed (–32.76%) groups (p <0.05) than in the CNC‐fed group at the end of the feeding period. The low‐density lipoprotein receptor mRNA level in the PPC‐fed group, and cholesterol 7α‐hydroxylase and scavenger receptor class B type 1 mRNA levels in the SPC‐fed group, were higher (p <0.05) than in the CN‐ and CNC‐fed groups. Faecal neutral sterol and caecal short‐chain fatty acid concentrations in the PPC‐ and SPC‐fed groups were higher (p <0.05) than in the CN‐ and CNC‐fed groups. The faecal total acidic sterol concentration was higher in the SPC‐fed group than in the CN‐ and PPC‐fed groups. Caecal anaerobe and Bifidobacterium populations were higher (p <0.05) in the PPC‐ and SPC‐fed groups than in the CN‐fed group. This study suggests that potato peptides, as soy peptides, alter caecal fermentation and steroid absorption and reduce the serum non‐HDL cholesterol level in rats fed cholesterol.     

Cowpea: an overview on its nutritional facts and health benefits

Cowpea (Vigna unguiculata) is a legume consumed as a high‐quality plant protein source in many parts of the world. High protein and carbohydrate contents with a relatively low fat content and a complementary amino acid pattern to that of cereal grains make cowpea an important nutritional food in the human diet. Cowpea has gained more attention recently from consumers and researchers worldwide as a result of its exerted health beneficial properties, including anti‐diabetic, anti‐cancer, anti‐hyperlipidemic, anti‐inflammatory and anti‐hypertensive properties. Among the mechanisms that have been proposed in the prevention of chronic diseases, the most proven are attributed to the presence of compounds such as soluble and insoluble dietary fiber, phytochemicals, and proteins and peptides in cowpea. However, studies on the anti‐cancer and anti‐inflammatory properties of cowpea have produced conflicting results. Some studies support a protective effect of cowpea on the progression of cancer and inflammation, whereas others did not reveal any. Because there are only a few studies addressing health‐related effects of cowpea consumption, further studies in this area are suggested. In addition, despite the reported favorable effects of cowpea on diabetes, hyperlipidemia and hypertension, a long‐term epidemiological study investigating the association between cowpea consumption and diabetes, cardiovascular disease and cancer is also recommended. © 2018 Society of Chemical Industry     

Health‐beneficial properties of potato and compounds of interest

Potatoes have shown promising health‐promoting properties in human cell culture, experimental animal and human clinical studies, including antioxidant, hypocholesterolemic, anti‐inflammatory, antiobesity, anticancer and antidiabetic effects. Compounds present such as phenolics, fiber, starch and proteins as well as compounds considered antinutritional such as glycoalkaloids, lectins and proteinase inhibitors are believed to contribute to the health benefits of potatoes. However, epidemiological studies exploring the role of potatoes in human health have been inconclusive. Some studies support a protective effect of potato consumption in weight management and diabetes, while other studies demonstrate no effect and a few suggest a negative effect. As there are many biological activities attributed to the compounds present in potato, some of which could be beneficial or detrimental depending on specific circumstances, a long‐term study investigating the association between potato consumption and diabetes, obesity, cardiovascular disease and cancer while controlling for fat intake is needed. © 2016 Society of Chemical Industry     

Removing of central nervous tissues from dressed carcasses: Washing with a low concentration of lactic acid in spraying cabinet

The efficiency of using lactic acid (LA) as a surface decontaminator for carcasses contaminated with spinal chord (SC) tissues was evaluated. Four representative places with surface area of 5 × 5 cm, were marked on the inner side of the porcine carcasses. 0.200 μl of 0.5% SC solution was applied to each marked area and separately washed in a specially designed spraying cabinet, which comprises 15 nozzles adjusted to spray 1% LA or tap water with a scatter pressure of 0.49 MPa. Samples were collected after washing for 15, 30, 60 and 90 s and analyzed by Ridascreen kit ELISA test. 1% LA reduces the SC materials to pre-contaminated levels while having negligible effect on the color of carcasses. Furthermore washing with LA insignificantly reduced the total microbial counts. Using very low concentration of LA (1%) in production lines of slaughter houses may help to decontaminate the carcasses to some extent without having adverse impact on quality attributes.     

Porcine artery elastin preparation reduces serum cholesterol level in rats

The effect of porcine artery elastin on serum cholesterol level was investigated in rats fed a cholesterol-free diet. Rats were fed for 4 weeks, with a diet (ED) containing 15% casein and 5% of porcine artery elastin in comparison with a diet (CD) containing 20% casein. The total serum and non-HDL-cholesterol concentrations were lower (P < 0.001) in ED-fed group than the CD-fed group at the end of the experiment. Caecal propionate concentration and Bifidobacterium and Lactobacillus population were higher (P < 0.05) in ED-fed group than the CD-fed group. The results of this study suggest that porcine artery elastin could be considered as a functional dietary protein with hypocholesterolaemic ability. Favourable amino acid composition and lysine derived cross links may at least be partially responsible for the hypocholesterolaemic ability of ED. Moreover, the higher caecal propionic acid concentration in the ED-fed group may have suppressed the cholesterol synthesis in the liver, and reduced the serum cholesterol level.     

Malicious Traffic Detection in IoT and Local Networks Using Stacked Ensemble Classifier

Malicious traffic detection over the internet is one of the challenging areas for researchers to protect network infrastructures from any malicious activity. Several shortcomings of a network system can be leveraged by an attacker to get unauthorized access through malicious traffic. Safeguard from such attacks requires an efficient automatic system that can detect malicious traffic timely and avoid system damage. Currently, many automated systems can detect malicious activity, however, the efficacy and accuracy need further improvement to detect malicious traffic from multi-domain systems. The present study focuses on the detection of malicious traffic with high accuracy using machine learning techniques. The proposed approach used two datasets UNSW-NB15 and IoTID20 which contain the data for IoT-based traffic and local network traffic, respectively. Both datasets were combined to increase the capability of the proposed approach in detecting malicious traffic from local and IoT networks, with high accuracy. Horizontally merging both datasets requires an equal number of features which was achieved by reducing feature count to 30 for each dataset by leveraging principal component analysis (PCA). The proposed model incorporates stacked ensemble model extra boosting forest (EBF) which is a combination of tree-based models such as extra tree classifier, gradient boosting classifier, and random forest using a stacked ensemble approach. Empirical results show that EBF performed significantly better and achieved the highest accuracy score of 0.985 and 0.984 on the multi-domain dataset for two and four classes, respectively.