Hall and ion-slip effect on MHD boundary layer flow of a micro polar fluid past a wedge

AbstractThis paper deals with the influence of Hall and ion slip effects on the magneto-hydrodynamic flow of a micropolar fluid past a non-conducting wedge. The analysis has been made by assuming that the fluid is viscous, incompressible and electrically conducting. The partial differential equations governing the flow and heat transfer are converted into highly non-linear ordinary differential equations by using the similarity transformations. These equations are then solved numerically. The effects of various parameters involved in the problem have been studied with the help of graphs and numerical values of skin friction coefficients and Nusselt number are presented in tabular form. Favorable comparison with previously published work on various special cases of the problem has been made. Results show that the local skin friction coefficient due to translational motion increases with the angle of the wedge and Hall effect parameter; hence heat transfer rate increases with these parameters. The result gets reversed with a increase in material, ion slip and magnetic field parameters

Role of non-timber forest products in sustaining forest-based livelihoods and rural households' resilience capacity in and around protected area- a Bangladesh study

People in developing world derive a significant part of their livelihoods from various forest products, particularly non-timber forest products. This article attempts to explore the contribution of NTFPs in sustaining forest-based rural livelihood in and around a protected area of Bangladesh, and their potential role in enhancing households resilience capacity. Based on empirical investigation our study revealed that, local communities gather a substantial amount of NTFPs from national park despite the official restrictions. 27 percent households of the area received at least some cash benefit from the collection, processing and selling of NTFPs, and NTFPs contribute as HHs primary, supplementary and emergency sources of income. NTFPs also constituted an estimated 19 percent of HHs net annual income, and were the primary occupation for about 18 percent of the HHs. HHs dependency on nearby forests for various NTFPs varied vis-a-vis their socio-economic condition as well as with their location from the park. Based on our case study the article also offers some clues for improving the situation in PA.Comment: To appear in Journal of Environmental Planning and Management, 201

Tuning the Mechanical Behavior of Density-Graded Elastomeric Foam Structures via Interlayer Properties.

The concept of density-graded foams has been proposed to simultaneously enhance strain energy dissipation and the load-bearing capacities at a reduced structural weight. From a practical perspective, the fabrication of density-graded foams is often achieved by stacking different foam densities. Under such conditions, the adhesive interlayer significantly affects the mechanical performance and failure modes of the structure. This work investigates the role of different adhesive layers on the mechanical and energy absorption behaviors of graded flexible foams with distinct density layers. Three adhesive candidates with different chemical, physical, and mechanical characteristics are used to assemble density-graded polyurea foam structures. The mechanical load-bearing and energy absorption performances of the structures are evaluated under quasi-static and dynamic loading conditions. Mechanical tests are accompanied by digital image correlation (DIC) analyses to study the local strain fields developed in the vicinity of the interface. Experimental measurements are also supplemented by model predictions that reveal the interplay between the mechanical properties of an adhesive interlayer and the macroscale mechanical performance of the graded foam structures. The results obtained herein demonstrate that the deformation patterns and macroscale properties of graded foam composites can be tuned by selecting different bonding agents. It is also shown that the proper selection of an adhesive can be a practical way to address the strength-energy dissipation dichotomy in graded structures

Correlation of Oxidative Stress with Serum Trace Element Levels and Antioxidant Enzyme Status in Beta Thalassemia Major Patients: A Review of the Literature

Beta thalassemia major is an inherited disease resulting from reduction or total lack of beta globin chains. Patients with this disease need repeated blood transfusion for survival. This may cause oxidative stress and tissue injury due to iron overload, altered antioxidant enzymes, and other essential trace element levels. The aim of this review is to scrutinize the relationship between oxidative stress and serum trace elements, degree of damage caused by oxidative stress, and the role of antioxidant enzymes in beta thalassemia major patients. The findings indicate that oxidative stress in patients with beta thalassemia major is mainly caused by tissue injury due to over production of free radical by secondary iron overload, alteration in serum trace elements and antioxidant enzymes level. The role of trace elements like selenium, copper, iron, and zinc in beta thalassemia major patients reveals a significant change of these trace elements. Studies published on the status of antioxidant enzymes like catalase, superoxide dismutase, glutathione, and glutathione S-transferase in beta thalassemia patients also showed variable results. The administration of selective antioxidants along with essential trace elements and minerals to reduce the extent of oxidative damage and related complications in beta thalassemia major still need further evaluation

Autism Spectrum Disorder Classification via Local and Global Feature Representation of Facial Image

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that affects social communication and interaction. Early diagnosis of ASD can mitigate the severity and help with ideal treatment direction. Computer vision-based methods with traditional machine learning and deep learning are employed in the literature for automatic diagnosis. Recently, deep learning with a facial image-based ASD classification has gained interest due to its ease of collection and non-invasiveness. We observed that the existing approaches utilized either local or global features of facial images to diagnose ASD. However, its important to consider both local and global features to obtain fine-grained details and larger contextual information for accurate detection and classification. This paper proposes a sequencer-based patch-wise Local Feature Extractor along with a Global Feature Extractor. Finally, the features from these modules are aggregated to obtain the final feature for the classification of ASD. Experiments on a publicly available Autism Facial Image Dataset demonstrate that our proposed framework achieves state-of-the-art performance. We achieved accuracy, precision, recall, and F1-score of 94.7%, 94.0%, 95.3%, and 94.6%, respectively