Higher College of Technology

About: Higher College of Technology is a based out in . It is known for research contribution in the topics: Computer science & Higher education. The organization has 369 authors who have published 379 publications receiving 729 citations.

Morphology, Structural, Thermal, and Tensile Properties of Bamboo Microcrystalline Cellulose/Poly(Lactic Acid)/Poly(Butylene Succinate) Composites.

Abstract: The present study aims to develop a biodegradable polymer blend that is environmentally friendly and has comparable tensile and thermal properties with synthetic plastics. In this work, microcrystalline cellulose (MCC) extracted from bamboo-chips-reinforced poly (lactic acid) (PLA) and poly (butylene succinate) (PBS) blend composites were fabricated by melt-mixing at 180 °C and then hot pressing at 180 °C. PBS and MCC (0.5, 1, 1.5 wt%) were added to improve the brittle nature of PLA. Field emission scanning electron microscopy (FESEM), scanning electron microscopy (SEM), X-ray diffraction (XRD), Fourier transform infrared spectroscope (FTIR), thermogravimetric analysis (TGA), differential thermogravimetry (DTG), differential scanning calorimetry (DSC)), and universal testing machine were used to analyze morphology, crystallinity, physiochemical, thermal, and tensile properties, respectively. The thermal stability of the PLA-PBS blends enhanced on addition of MCC up to 1wt % due to their uniform dispersion in the polymer matrix. Tensile properties declined on addition of PBS and increased with MCC above (0.5 wt%) however except elongation at break increased on addition of PBS then decreased insignificantly on addition of MCC. Thus, PBS and MCC addition in PLA matrix decreases the brittleness, making it a potential contender that could be considered to replace plastics that are used for food packaging.

Conjugate buoyant convective transport of nanofluids in an enclosed annular geometry.

Abstract: A vertical annular configuration with differently heated cylindrical surfaces and horizontal adiabatic boundaries is systematically studied in view to their industrial applications. In this paper, we investigate the effects of conjugate buoyant heat transport in water based nanofluids with different nanoparticles such as alumina, titania or copper, and is filled in the enclosed annular gap. The annulus space is formed by a thick inner cylinder having a uniform high temperature, an exterior cylindrical tube with a constant lower temperature, and thermally insulated upper and lower surfaces. By investigating heat transport for broad spectrum of Rayleigh number, solid wall thickness, thermal conductivity ratio and nanoparticle volume fraction, we found that the influence of wall thickness on thermal dissipation rate along wall and interface greatly depends on conductivity ratio and vice-versa. In particular, we uncover that the choice of nanoparticle in a nanofluid and its concentration are key factors in enhancing the thermal transport along the interface. Specially, copper based nanofluids produces higher heat transport among other nanoparticles, and for the range of nanoparticle concentration chosen in this analysis, enhanced thermal dissipation along the interface has been detected as nanoparticle volume fraction is increased. Our results are applicable to choose nanofluids along with other critical parameters for the desired heat transport.

Dynamics of chemically reactive Jeffery fluid embedded in permeable media along with influence of magnetic field on associated boundary layers under multiple slip conditions

Abstract: The prime goal of the current pagination is to scrutinize behavior of mixed convective Jeffrey nanoliquid (JNL) flowing over an exponentially stretching sheet by using possessions of radiative energy flux, non-uniformly heated source/sink, slip boundary condition, and modified Arrhenius energy function. The governing physical expression (PDEs) are converted in view of similar ODE’s. Solution of constructed problem is heeded by employing shooting technique. Graphical and tabular results are designed for assisting flow, opposing flow and forced convection cases to scrutinize the performance of miscellaneous significant variables. The results attained exhibited worthy agreement with the preceding remarkable works. The noteworthy finding is Opposing flow case demonstrations higher temperature and concentration profile compared to assisting flow and forced convection case.

Machine learning approaches classify clinical malaria outcomes based on haematological parameters

Abstract: Malaria is still a major global health burden, with more than 3.2 billion people in 91 countries remaining at risk of the disease. Accurately distinguishing malaria from other diseases, especially uncomplicated malaria (UM) from non-malarial infections (nMI), remains a challenge. Furthermore, the success of rapid diagnostic tests (RDTs) is threatened by Pfhrp2/3 deletions and decreased sensitivity at low parasitaemia. Analysis of haematological indices can be used to support the identification of possible malaria cases for further diagnosis, especially in travellers returning from endemic areas. As a new application for precision medicine, we aimed to evaluate machine learning (ML) approaches that can accurately classify nMI, UM, and severe malaria (SM) using haematological parameters. We obtained haematological data from 2,207 participants collected in Ghana: nMI (n = 978), SM (n = 526), and UM (n = 703). Six different ML approaches were tested, to select the best approach. An artificial neural network (ANN) with three hidden layers was used for multi-classification of UM, SM, and uMI. Binary classifiers were developed to further identify the parameters that can distinguish UM or SM from nMI. Local interpretable model-agnostic explanations (LIME) were used to explain the binary classifiers. The multi-classification model had greater than 85% training and testing accuracy to distinguish clinical malaria from nMI. To distinguish UM from nMI, our approach identified platelet counts, red blood cell (RBC) counts, lymphocyte counts, and percentages as the top classifiers of UM with 0.801 test accuracy (AUC = 0.866 and F1 score = 0.747). To distinguish SM from nMI, the classifier had a test accuracy of 0.96 (AUC = 0.983 and F1 score = 0.944) with mean platelet volume and mean cell volume being the unique classifiers of SM. Random forest was used to confirm the classifications, and it showed that platelet and RBC counts were the major classifiers of UM, regardless of possible confounders such as patient age and sampling location. The study provides proof of concept methods that classify UM and SM from nMI, showing that the ML approach is a feasible tool for clinical decision support. In the future, ML approaches could be incorporated into clinical decision-support algorithms for the diagnosis of acute febrile illness and monitoring response to acute SM treatment particularly in endemic settings.