Predicting Interfacial Loads between the Prosthetic Socket and the Residual Limb for Below-Knee Amputees – A Case Study

Abstract:  In this study, an artificial neural network (ANN) was deployed as a tool to determine the internal loads between the residual limb and prosthetic socket for below-knee amputees. This was achieved by using simulated load data to validate the ANN and captured clinical load data to predict the internal loads at the residual limb–socket interface. Load/pressure was applied to 16 regions of the socket, using loading pads in conjunction with a load applicator, and surface strains were collected using 15 strain gauge rosettes. A super-position program was utilised to generate training and testing patterns from the original load/strain data collected. Using this data, a back-propagation ANN, developed at the University of the West of England, was trained. The input to the trained network was the surface strains and the output the internal loads/pressure. The system was validated and the mean square error (MSE) of the system was found to be 8.8% for 1000 training patterns and 8.9% for 50 testing patterns, which was deemed an acceptable error. Finally, the validated system was used to predict pressure-sensitive/-tolerant regions at the limb–socket interface with great success.     

Highly efficient photocatalytic degradation of methylene blue using carbonaceous WO<Subscript>3</Subscript>/TiO<Subscript>2</Subscript> composites

Recent improvements in the performance of photocatalysts made it possible to tackle pollution through environment friendly methods. This study investigates the modification of the photocatalytic activity of TiO₂ by employing WO₃ and conductive polymers, namely, polyaniline (Pani) and polypyrrole (Ppy). Basing on our previous improvement of TiO₂ using a conductive polymer and activated carbon (AC), this study determines the activated carbon forms of TiO₂. The prepared composites are characterized using X-ray powder diffraction, transmission electron microscopy, Fourier transform infrared, thermogravimetric analysis, Brunauer–Emmet–Teller, and UV–Vis spectroscopy. The specific surface area of the mesoporous composites is as follows: WO₃/TiO₂·AC (Pani) > WO₃/TiO₂·AC (Ppy) > WO₃/TiO₂·Pani > WO₃/TiO₂·Ppy (127 > 98 > 68 > 44 m² g^?1), which exhibited a similar trend to the photocatalytic performances (100 > 95 > 91 > 72 % conversion rate). This result could be attributed to higher porosity, surge of charge separation, and photo-responding range extension induced by the synergistic effect of WO₃, conducting polymers, and TiO₂ in the samples.     

Integrating a broader notion of food security and gender empowerment into the African Green Revolution

A Green Revolution for Africa is emerging after decades of neglect of Africa’s agricultural systems. To counter these years of neglect, the then United Nations Secretary-General Kofi Annan called for “a uniquely African Green Revolution”. Since then, a number of initiatives have emerged or are emerging to realize this important vision. As more money and attention galvanizes much-needed action on the African Green Revolution, a vigorous debate is required to ensure that the mission of improving food security on the world’s poorest continent is achieved in the most effective, comprehensive and inclusive manner possible. The African Green Revolution cannot be limited to increasing yields of staple crops but must be designed as a driver of sustainable development, which includes gender empowerment and nutrition elements. This paper first reviews the Asian Green Revolution’s successes and shortcomings from a nutrition and gender perspective and then outlines what the global community can do to ensure that some of the limitations of the Asian Green Revolution, specifically with regard to nutrition and gender, are not repeated.     

Classification of shoreline vegetation in the Western Basin of Lake Erie using airborne hyperspectral imager HSI2, Pleiades and UAV data

Mapping land and aquatic vegetation of coastal areas using remote sensing for better management and conservation has been a long-standing interest in many parts of the world. Due to natural complexity and heterogeneity of vegetation cover, various remote sensing sensors and techniques are utilized for monitoring coastal ecosystems. In this study, two unsupervised and two supervised standard pixel-based classifiers were tested to evaluate the mapping performance of the second-generation airborne NASA Glenn Hyperspectral Imager (HSI2) over the narrow coastal area along the Western Lake Erie’s shoreline. Furthermore, the classification results of HSI2 (using the whole Visible-Near Infrared (VIS+ NIR) hyperspectral dataset, and also the spectral subset of Visible (VIS) spectral bands) were compared to multispectral Pleiades (VIS+ NIR) and Unmanned Aerial Vehicle (UAV) VIS classified images. The goal was to explore how different spectral ranges, and spatial and spectral resolutions impact the unsupervised and supervised classifiers. While the unsupervised classifiers depended more on the spectral range, spectral or spatial resolutions were important for the supervised classifiers. The Support Vector Machine (SVM) was found to perform better than other classification methods for the HSI2 images over all twenty-two study sites with the overall accuracy (OA) ranging from 82.6%–97.5% for VIS, and 81.5%–95.6 % for VIS + NIR. Considerably better performance of the supervised classifiers for the HSI2 VIS data over the Pleiades data (OA = 74.8–83.4%) suggested the importance of spectral resolution over spectral range (VIS vs. VIS+ NIR) for the supervised methods. The unsupervised classifiers exhibited low accuracy for both HSI2 VIS and UAV VIS imagery (OA< 30.0%) while the overall accuracy for the HSI2 VIS+ NIR and Pleiades data ranged from 60.4%–78.4 % and 42.1%–66.4%, respectively, suggesting the importance of spectral range for the unsupervised classifiers.     

Removal of methylated arsenic in groundwater with iron filings

Field and laboratory experiments were conducted to study the potential use of Fe filings to remove monomethyl arsenate (MMA) and dimethyl arsenate (DMA) from contaminated waters. Batch experiments show that the affinity of MMAfor Fe filings is comparable to that of inorganic arsenate, but lower than that for arsenite. In contrast, very little DMA was retained by Fe filings or their corrosion products. The wide range of affinities of different As species for Fe filings was confirmed by a series of retention and selective-leaching experiments using small columns and As-spiked groundwater. The effectiveness of Fe filings was also demonstrated with a field deployment at a U.S. Superfund site where groundwater is highly contaminated with both organic and inorganic As species. Over the course of 4 months, a 3 L cartridge of Fe filings removed > 85% of As contained in 16000 L of groundwater containing 1-1.5 mg/L total dissolved As, approximately 30% of which was organic As. The results indicate that Fe filings could be used to treat groundwater contaminated with MMA, which is the main organic form of As at most contaminated sites. Indications of partial demethylation in the column of Fe filings suggest conditions might be optimized further to enhance the removal of DMA.     

Early Experience and Postembryonic Maturation of Body Patterns in Cuttlefish (Sepia officinalis).

This study investigates effects of the environment on the maturation of body patterns in cuttlefish (Sepia officinalis). Cuttlefish were reared either individually on a uniform background, which the authors have termed uniform-solitary conditions (Group A), or grouped on variegated backgrounds, which the authors have termed varied-social conditions (Group B). At Days 1, 15, 30, and 60, juveniles were placed individually in perceptually different testing conditions, either on small, variegated stones or on a uniform pale gray background. During development in both testing conditions, juveniles from Group B concealed themselves differently from those from Group A. Thus, it appears that the response to the background is subject to individual experience. Some hypotheses are discussed relating to the effect of early experience on the maturation of body patterns. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Engine's behaviour on magnetite nanoparticles as additive and hydrogen addition of chicken fat methyl ester fuelled DICI engine: A dual fuel approach

The present work evaluated the influence of magnetite nanoparticles in chicken fat methyl-ester blend (CFME20) and hydrogen induction in CFME20 nano-fuels combustion performance and exhaust emissions using a 1-cylinder dual DICI engine. Results revealed that CFME20 blend exhibits a significant reduction in smoke, hydrocarbon, and carbon-monoxide emissions; however, increment in oxides of nitrogen and insignificant reduction in BTE also noted. Magnetite nanoparticles mixed with CFME20 through ultrasonication to make nano-additive fuel which noticeably decreased the exhaust emissions including oxides of nitrogen and slightly enhanced the BTE as well. Performance parameters gradually enhanced with escalating nanoparticles dosage up to 100 ppm. Hydrogen induction during nano-additive pilot fuel combustion further decreased the smoke, carbon-monoxide, and hydrocarbon emissions and enhanced the BTE to an appreciable level. However, oxides of nitrogen also enhanced with increasing hydrogen flow rate, but for 10 lpm trivial increased. Hence, the results confirmed that nano-fuel (100 ppm) with hydrogen (10 lpm) addition provides optimum outcomes.