Aegle marmelos (L.) Correa: An Underutilized Fruit with High Nutraceutical Values: A Review

Aegle marmelos (L.) Correa (Bael) fruit, a member of the Rutaceae family, is a major cultivated fruit plant in tropical and subtropical regions in countries of southeast Asia. Bael fruit has been a major topic for studies in recent years mainly due to its high nutritional (carbohydrates, proteins, minerals, and vitamins) value and presence of various phytochemicals, which attributed to its high medicinal value. These phytochemicals include various compounds, e.g., alkaloids, flavonoids, and phenolic acids (protocatechuic acid, gallic, and ellagic acid). The fruit extract of bael has been also an important study area for its pharmacological activities, including antidiarrheal, antioxidant, antidiabetic, hepatoprotective, radioprotective, anticancer, antiulcer properties. The current review mainly highlighted the nutritional and pharmacological activities of bael fruit. The nutritional profile and phytochemical profile were discussed in the review, along with their concentration in the fruit. Moreover, the experiments carried out in vivo and in vitro of bael fruit extracts with respect to their pharmacological activities were also discussed in the article. The recent literature based on nutritional and pharmacological values of bael fruit showed its high potential as a food and pharmaceutical product. Despite having high nutritional and pharmacological value, research related to molecular mechanisms of bael fruit is still limited, and clinical trials are needed to ensure its safety as a product in the food and pharma industries.     

Bioconvective Casson nanofluid flow toward stagnation point in non-Darcy porous medium with buoyancy effects,chemical reaction,and thermal radiation

The emphasis on non-Newtonian fluid encountered in biomedical, pharmaceuticals, mining, food, chemical, and plastics industries and in noticeably enormous diverse industrial applications influenced this article. This study is accomplished in a non-Darcy porous stretching surface to investigate the stagnation point of bioconvective Casson nanofluid. Chemical reaction, applied consistent magnetic field, radiative heat transfer, and buoyancy force consequences are studied for numerical examination. Composed of nonlinear partial differential equations for the above presumptions and reforming them into ordinary differential equations by means of compatible transformations are enforced. Adopting the fifth order Runge–Kutta Felhberg method with the shooting technique obtained a numerical solution. Obtained solutions are authenticated by comparing previous solutions. The major finding includes the reduction of the Casson parameter on the skin friction coefficient.     

Physicochemical,techno-functional and structural properties of gluten-free crackers developed from sorghum (Sorghum bicolor (L.)) varieties

The present study was conducted to develop gluten-free crackers using six different white varieties of sorghum, namely CO30, K12, Parbhani Moti (PM), Parbhani Shakti (PS), ICSV15021 and M35-1. The physico-chemical, mineral and functional properties of developed crackers were studied. The crackers from soft wheat flour serve as a control. Highest protein (7.35 g/100 g), iron (6.63 mg/100 g) and potassium (199.86 mg/100 g) were found in ICSV15021 crackers, whereas highest total dietary fibre (9.01 g/100 g), magnesium (31.13 mg/100 g), copper (1.47 mg/100 g), zinc (1.92 g/100 g) and manganese (1.32 mg/100 g) were found in PS crackers. All exhibited A-type crystallinity pattern except PS crackers (V-type pattern). Overall acceptability of K12 sorghum crackers was found to be similar to control. In conclusion, this study evidenced that gluten-free sorghum crackers exhibited higher nutrients and mineral composition and can be regarded as health-promoting functional food, which is also cost-effective than commercially available gluten-free products.     

A machine learning assisted optical multistage interconnection network: Performance analysis and hardware demonstration

Integration of the machine learning (ML) technique in all-optical networks can enhance the effectiveness of resource utilization, quality of service assurances, and scalability in optical networks. All-optical multistage interconnection networks (MINs) are implicitly designed to withstand the increasing high-volume traffic demands at data centers. However, the contention resolution mechanism in MINs becomes a bottleneck in handling such data traffic. In this paper, a select list of ML algorithms replaces the traditional electronic signal processing methods used to resolve contention in MIN. The suitability of these algorithms in improving the performance of the entire network is assessed in terms of injection rate, average latency, and latency distribution. Our findings showed that the ML module is recommended for improving the performance of the network. The improved performance and traffic grooming capabilities of the module are also validated by using a hardware testbed.