No abstract

In this study, investigations were carried out on the energy evaluation, performance analysis and optimization of briquettes produced from biomass wastes (rice-husk and sawdust) and their composites using starch and clay as binders. The proximate compositions of the briquettes were determined following ASTM analytical methods. The moisture content of rice husk and sawdust before briquettes was 20wt.% and 15wt.% respectively. The briquettes produced from bio-waste material of homogeneous particle sizes of 0.5mm and two binders of a percentage of 90:10 which were sun-dried, prepared and moisturized, were reduced to 5wt.% and compressed for the production of briquettes. The energy evaluation of the briquettes was performed using an oxygen bomb calorimeter and the performance test of the briquettes was carried out. Design Expert Central Composite Design Tool was used in the design and Response surface methodology was used to optimize the energy values of rice-husk/sawdust composite briquettes with clay and starch as binders, after which composite briquette made of mahogany sawdust/rice-husk were produced using the optimum condition values of 15% binder starch, 28% rice-husk and 9Mpa compaction pressure. The results showed that composite briquettes of mahogany sawdust and rice-husk produced with starch had a maximum energy value of 5.69kcal/g, while those made with clay had a minimum energy value of 3.35kcal/g. However, the experimental result was less than the predicted optimum value of 2%. This shows that composite briquette made from mahogany sawdust/rice-husk has better energy efficiency than other briquettes considered and it has been observed that starch is a better bonding material than clay. Briquetting technology has great potential to transform waste biomass in affordable, effective and environmentally safe, high-quality solid fuel for households and industry use.

Upper limb spasticity (ULS) is a common pathophysiological changes manifest by a structural damage towards the central nervous system (CNS) that includes brain and spinal cord. The current clinical practice of spasticity assessment utilizes Modified Ashworth Scale (MAS) as a subjective tool to measure the severity of spasticity. Lack of objective value, poor sensitivity in detecting minimal changes, and dependency to the interpretation by the assessing clinicians are the several reasons of the inter and intra-rater variability of the measurement using MAS. These limit the use of MAS in diagnosing, treating, and monitoring spasticity especially in inexperienced clinicians, hence leading to inadequate spasticity management. To overcome this problem, a study is carried out to quantify and develop a data-driven model of ULS detection based on MAS. The characteristics that detect the existence of ULS according to MAS are identified and adopted to train the machine learning models for smart diagnosis purpose to assist the physicians to effectively manage spasticity.