Smart Selection of Milling Strategies for Complex Aircraft Components

Russian Engineering Research - In milling, the strategy for removing the residual margin in enclosed areas affects the machining time and the tool load; the specifics of that influence are studied...     

Compositional characteristics,texture, shelf-life and sensory quality of snack crackers produced from non-traditional ingredients

The study was conducted to evaluate the effect of non-traditional and highly available and nutrient-dense ingredients in the production of value-added crackers. The crackers were prepared by combining partially defatted chia flour, wheat germ, quinoa, and oat. The antioxidant activity and the polyphenol content were 7.0–8.4 times higher in crackers produced with non-traditional ingredients compared to the control snack. The hydroperoxide value indicated a low oxidative deterioration of the oil. The sample with 10% of partially defatted chia flour was selected for shelf-life test under storage conditions and sensory evaluation. Modified atmosphere exerted a protective effect on the lipid stability regardless of the incorporation of BHT to the formulation. All the evaluated attributes scored highly during consumer acceptance. The formulated cracker presented a relevant content of protein, dietary fibre and omega-3 and -6 fatty acids. Based on these results, the crackers containing non-traditional ingredients resulted in a product with a good potential for both consumer acceptance and outstanding nutritional benefits.     

Influence of fermentation with different lactic acid bacteria and in vitro digestion on the change of phenolic compounds in fermented kiwifruit pulps

The present study aimed to evaluate the effect of fermentation and gastrointestinal digestion of three kinds of fermented kiwifruit pulps with different Lactobacillus (Lactobacillus plantarum, Lactobacillus acidophilus and Lactobacillus casei). The changes in bioactive substances (total phenolic acid, total flavonoid, vitamin C and the viable count), antioxidant capacity (DPPH, ABTS, hydroxyl radical and superoxide anion radical scavenging activity) and phenolic profiles (protocatechuic acid, protocatechualdehyde, chlorogenic acid, caffeic acid and p-coumaric acid) were detected. The result showed, compared to non-fermented kiwifruit pulp, fermentation with LP and LA had higher content of TPA, TF and V_C, as well as antioxidant capacity. Fermentation with LP and the content of protocatechualdehyde, p-coumaric acid and chlorogenic acid were increased. However, after digestion, LP showed more effect in maintaining the content of antioxidants, antioxidant capacity and the viable count rather than LA. During digestion, the content of protocatechualdehyde and p-coumaric acid was increased in fermented samples compared with non-fermented samples. Overall, compared with LA and LC, LP is more suitable for the fermentation of kiwifruit pulp.     

Deep Learning Based Process Analytics Model for Predicting Type 2 Diabetes Mellitus

Process analytics is one of the popular research domains that advanced in the recent years. Process analytics encompasses identification, monitoring, and improvement of the processes through knowledge extraction from historical data. The evolution of Artificial Intelligence (AI)-enabled Electronic Health Records (EHRs) revolutionized the medical practice. Type 2 Diabetes Mellitus (T2DM) is a syndrome characterized by the lack of insulin secretion. If not diagnosed and managed at early stages, it may produce severe outcomes and at times, death too. Chronic Kidney Disease (CKD) and Coronary Heart Disease (CHD) are the most common, long-term and life-threatening diseases caused by T2DM. Therefore, it becomes inevitable to predict the risks of CKD and CHD in T2DM patients. The current research article presents automated Deep Learning (DL)-based Deep Neural Network (DNN) with Adagrad Optimization Algorithm i.e., DNN-AGOA model to predict CKD and CHD risks in T2DM patients. The paper proposes a risk prediction model for T2DM patients who may develop CKD or CHD. This model helps in alarming both T2DM patients and clinicians in advance. At first, the proposed DNN-AGOA model performs data preprocessing to improve the quality of data and make it compatible for further processing. Besides, a Deep Neural Network (DNN) is employed for feature extraction, after which sigmoid function is used for classification. Further, Adagrad optimizer is applied to improve the performance of DNN model. For experimental validation, benchmark medical datasets were used and the results were validated under several dimensions. The proposed model achieved a maximum precision of 93.99%, recall of 94.63%, specificity of 73.34%, accuracy of 92.58%, and F-score of 94.22%. The results attained through experimentation established that the proposed DNN-AGOA model has good prediction capability over other methods.     

Energy Conservation at High-Tech Enterprises by Means of Automated Energy Management

Russian Engineering Research - Practical approaches are described for introducing an energy management system at a high-tech enterprise so as to centralize and automate the regulation of energy...     

Energy-Efficient Separation of Water–Petroleum Emulsion

Russian Engineering Research - Systems are considered for the dehydration of petroleum so as to separate water–petroleum emulsions. In particular, the low-temperature microwave (UHF)...     

A numerical model to predict the powder–binder separation during micro-powder injection molding

The micro-powder injection molding (micro-PIM) process has the potential to bridge the gap between the design and manufacturing of micro-components that are often used in small and handy devices. Numerical modeling helps to analyze and overcome various difficulties of micro-PIM. In the present work, a numerical model is developed to predict the powder–binder separation (a common defect in PIM and especially severe in micro-PIM) during the injection of an alumina feedstock. A powder–binder separation criterion is proposed dealing with applied injection pressure and friction force between the powder and binder. An indirect comparison of feedstock travel time between two locations is used to validate the model. The predicted segregation from the simulated result is supported by a qualitative experimental measurement. The developed model can be used to optimize injection parameters to get a defect-free product.