Assessment of the nutritional value of wild leafy vegetables consumed in the Buhera District of Zimbabwe: a preliminary study

Wild leafy vegetables play a vital role in the livelihoods of many communities in Africa. The focus of this study was to investigate the nutritional value of wild vegetables commonly consumed by the people of Buhera District in the Manicaland province of Zimbabwe. A variety of vegetables including Amaranthus hybridus, Cleome gynandra, Bidens pilosa, Corchorus tridens, and Adansonia digitata were collected during a survey in Buhera District. Samples were processed employing traditional methods of cooking and drying, then subjected to proximate and micronutrient analyses. The results indicate that these vegetables were particularly high in calcium, iron, and vitamin C. Compared with Brassica napus (rape), Amaranthus hybridus contained twice the amount of calcium, with other nutrients almost in the same range. Compared with Spinacia oleracea (spinach), Amaranthus hybridus contained three times more vitamin C (44 mg/100 g). Calcium levels were 530 mg/100 g. Amaranthus hybridus was also found to contain 7, 13, and 20 times more vitamin C, calcium, and iron respectively compared with Lactuca sativa (lettuce). Cleome gynandra contained 14 mg/100 g, 115 mg/100 g, 9 mg/100 g of vitamin C, calcium, and iron respectively. Bidens pilosa was found to be a valuable source of vitamin C (63 mg/100 g), iron (15 mg/100 g), and zinc (19 mg/100 g), compared with Brassica oleracea (cabbage). The leaves of Corchorus tridens were an excellent source of vitamin C (78 mg/100 g), calcium (380 mg/100 g), and iron (8 mg/100 g). The Adansonia digitata leaves were also rich in vitamin C (55 mg/100 g), iron (23 mg/ 100 g), and calcium (400 mg/100 g). Based on these nutrient contents, the above vegetables will have potential benefits as part of feeding programmes, as well as their promotion as part of composite diet for vulnerable groups.

Application of regularization methods to damage detection in large scale plane frame structures using incomplete noisy modal data

This paper presents an approach for detecting local damage in large scale frame structures by utilizing regularization methods for ill-posed problems. A direct relationship between the change in stiffness caused by local damage and the measured modal data for the damaged structure is developed, based on the perturbation method for structural dynamic systems. Thus, the measured incomplete modal data can be directly adopted in damage identification without requiring model reduction techniques, and common regularization methods could be effectively employed to solve the developed equations. Damage indicators are appropriately chosen to reflect both the location and severity of local damage in individual components of frame structures such as in brace members and at beam-column joints. The Truncated Singular Value Decomposition solution incorporating the Generalized Cross Validation method is introduced to evaluate the damage indicators for the cases when realistic errors exist in modal data measurements. Results for a 16-story building model structure show that structural damage can be correctly identified at detailed level using only limited information on the measured noisy modal data for the damaged structure.

Communication latency hiding in a parallel conjugate gradient method

Abstract not available