Stress Analysis Of The Human Tibia Knee Joint Using Finite Element Method

Despite the several years of studies that have been contributed to the human knee joint in pursue of producing a failure free knee joint protheses, there are still a lot of rooms for improvement on the available prostheses. In this present study, a series of analyses on the human tibia has been carried out. The objectives of the present study were to study effects of stress distribution on human tibia in various degrees of flexion simulating walking and squatting. The Finite Element (FE) method was adopted for the analysis. Through the finite element analyses, data concerning the stress distribution and von Misses stress during gait cycle and squatting were obtained. The results obtained were compared with those of the experimental literature for validation. The results of this present study indicated that low stress value occurs during toe-off simulation while the high stress value occurs during deep flexion with the knee is flexed 90°. The von Mises stress observed on the medial compartment during these instants were 13.85MPa and 26.84MPa respectively. The obtained average stress distribution of a gait cycle and deep flexions were 15.29MPa and 25.09MPa respectively. it is worth to note that a high stress concentration occurs at the tibial plateau, distinctively at the medial compartment. This implies that under deep flexion a possible unstable fracture will be initiated since the maximum stress allowable on the tibia is 25MPa. In conclusion, this kind of research gives a better understanding of the stress applied on the tibia by body weight that assist on designing Total Knee Replacement against failure. The result could support in the context of minimizing contact stress between the tibia bone and the tibial inser

Fingerprinting of complex bioprocess data

PhD ThesisThe focus of the research is on the analysis of complex bioprocess datasets with the ultimate goal of forming a link between the data and its underlying biological patterns. The challenges associated with investigating complex bioprocess data include the high dimensionality of the underlying measurements, the limited number of “observations”, and the complexity of selecting meaningful features to characterise the data. Contained within these data is a wealth of information that can contribute to inferring process outcomes and providing insight into improving productivity and process efficiency. To address these challenges, there is a real need for techniques to analyse and extract knowledge from the data. This thesis investigates an integrated discrete wavelet transform (DWT) and multiway principal components analysis (MPCA) approach to extract meaningful information from different types of bioprocess data. The integrated methodology is demonstrated by application to two types of bioprocess data: a near infrared (NIR) dataset collected from an industrial monoclonal antibodies (MAb) process, and an electrospray ionisation mass spectrometry (ESI-MS) dataset generated during the development of recombinant mammalian cell lines. The objective of the thesis was to develop a methodology that enabled the extraction of information from these two data sets. For the industrial NIR dataset, the genealogy or parent-child relationship of batch process from monoclonal antibodies (MAb) manufacturing was investigated whilst for the ESI-MS dataset goal was to identify characteristics that would enable the differentiation between high and low cell producers. The main challenges of the NIR and ESI-MS data sets lay in the complexity of the spectra. The NIR spectra usually have broad overlapping peaks and baseline shifts. Furthermore, as the NIR spectra used in this thesis were collected from batch process, there is an extra dimension in the data that of batch. On the one hand, the extra dimension provides extra information but on the other, it presents a further challenge as the data now is three-dimensional and requires additional pre-processing, including data matrix unfolding and batch alignment. Similar to the NIR spectra, the ESI-MS dataset also faces the problem of baseline shifts along with other complexities including high noise to signal ratio, shifts in the mass-to-charge ratio, and differences in signal intensities. These challenges lead to difficulties in extracting relevant information about the feature of interest. The proposed methodology was proven effective in extracting meaningful information from both data sets. In summary, the proposed method which utilised the integration of discrete wavelet transform and multiway principal component analysis was able to differentiate the distinguished characteristics of the spectra in the datasets thereby providing understanding of the relationships between spectral data and the underlying behaviour of the process.International Islamic University Malaysia, Ministry of High Education Malaysi

Characterization And Classification Of Bioactive Compound In Natural Products By FTIR And Multivariate Data Analysis

Bioactive compounds are one of the natural products used especially for medicinal, pharmaceutical and food application. Increasing research performed on the extraction, isolation and identification of bioactive compounds, however non to date has explored on the identification of flavonoids classes. Therefore, this study was focused on the development of algorithm for rapid identification of flavonoids classes which are flavanone, flavone and flavonol. Fourier Transform Infrared (FTIR) spectroscopy coupled with multivariate statistical data analysis, which is Principal Component Analysis (PCA) was performed. The results showed that the flavonoids classes were identified according to spectral region assigned using the PCA algorithm based on the FTIR spectrum of the samples. The study concluded that FTIR coupled with PCA analysis can be used as a molecular fingerprint for rapid identification of flavonoids classes. The comparative studies of other flavonoids classes are still under investigation using the same method

Physicochemical characteristics of bionanocomposites, polycaprolactone/starch/cocoa pod husk microfibrillated cellulose

The development of biocomposites has increased due to their biodegradability,renewability, and high specific strength which are comparable with other polymer composites. Even though, the conventional composites are still in high demand due to their durability, however, it cannot decompose and the accumulation of these wastes for a long period is harmful to the living things and to the environment. Therefore, in this study the biodegradable polymers, polycaprolactone (PCL) and starch were selected in this study to synthesize bionanocomposite incorporated with microfibrillated cellulose (MFC). The microfibrillated cellulose (MFC) was extracted from cocoa pod husk (CPH–MFC) and was mixed with PCL and starch, where its amount was varied between 3-10wt%. The prepared bionanocomposites were characterised in terms of its water uptake rate, and structural and thermal properties using Fourier transform infared spectroscopy (FTIR) and differential scanning calorimetry (DSC). The morphology analysis using scanning electron microscope (SEM) shows that the CPH–MFC extracted was in nanoscale size. The percentage of water uptake of the prepared bionanocomposites increased with the amount of CPH–MFC. Meanwhile, the FTIR spectra of the prepared bionanocomposites showed almost similar characteristic peaks with the FTIR spectra of pure PCL. DSC analysis showed that the melting temperature increased as the amount of CPH–MFC increased. This study justifies that the incorporation of CPH–MFC with the PCL–starch matrix improved the water uptake rate and thermal properties but did not show significant changes to the structure of PCL

Algorithm for rapid identification of flavonoids classes

Bioactive compounds are one of the natural products used especially for medicinal, pharmaceutical and food application. Increasing research performed on the extraction, isolation and identification of bioactive compounds, however non to date has explored on the identification of flavonoids classes. Therefore, this study was focused on the development of algorithm for rapid identification of flavonoids classes which are flavanone, flavone and flavonol and also their derivatives. Fourier Transform Infrared (FTIR) spectroscopy coupled with multivariate statistical data analysis, which is Principal Component Analysis (PCA) was utilized. The results exhibited that few significant wavenumber range provides the identification and characterization of the flavonoids classes based on PCA algorithm. The study concluded that FTIR coupled with PCA analysis can be used as a molecular fingerprint for rapid identification of flavonoids

Rapid screening of microfibrillated cellulose structure through FTIR and principal component analsysis

Analysis of FTIR spectra combined with multivariate statistical analysis technique specifically Principal Component Analysis was used for rapid screening of microfibrillated cellulose (MFC) structure. The current methods used to extract the MFC are by using the chemical and physical approaches. To date, most researchers focused on bench (lab) scale experiment to identify the structure of MFC. Lack of mathematical models focusing on this goal has motivated this project. Principal component analysis is applied to identify the chemical composition of the MFC. The dataset comprises FTIR spectra of 12 samples that comes from MFC with different particles sizes, 200 µm, 250 µm and 800 µm. The result shows that the wavelength region which represents the MFC structure is in the range of 2950 cm-1 to 2978 cm-1 for particle size of 200 micrometer since it has larger surface area for penetration of fungal into the biomass due to lower diffusion of air, water and metabolite intermediates of which cellulose can be easily hydrolyzed due to increase in pore size of substance through greater removal of hemicellulose and lignin. The overall result indicates that the combination of FTIR analysis and PCA is a useful technique for rapid screening of MFC structure

Agarwood leaf essential oil characterization and effects on MCF-7 breast cancer cells

Breast cancer continues to remain as the leading cause of cancer mortality among women worldwide. Agents for prevention and cure for breast cancer are contiuously being researched. İn particular, agarwood essential oil from resin infiltrated heartwood has been reported to have substantial evidences of medicinal benefits. Nevertheless, there is very limited report on agarwood leaf essential oil (ALEO). Hence, this present study was conducted to evaluate the physicochemical properties, chemical constituents and anticancer activity of ALEO. ALEO was observed to be of pale-yellow colour with sweet smell. Other physicochemical properties include refractive index of 1.44, specific gravity of 0.886, saponification value of 131.88 mg KOH/g, acid value of 2.80 mg KOH/g and iodine value of 105.07 gI2/100g. The profiling of chemical constituents using gas chromatography-mass spectrometry (GCMS) revealed 19 compounds. Hexadecanoic acid was the major compound (64.41%). The biomarkers of agarwood; azulene (0.619%) and guaiol (0.2997%) were also detected. ALEO was tested for anticancer activity against MCF-7 cancer cells using WST-8 assay. ALEO showed the IC50 value of 31% (v/v) against MCF-7 cells after 36 hours of treatment. In conclusion, this study provides information on ALEO physicochemical properties and chemical constituents that can be used as benchmark for quality assurance as well as proof that ALEO holds a potential as anticancer agent

Web based water turbidity monitoring and automated filtration system: IoT application in water management

Water supplied to residential areas is prone to contaminants due to pipe residues and silt, and therefore resulted in cloudiness, unfavorable taste, and odor in water. Turbidity, a measure of water cloudiness, is one of the important factors for assessing water quality. This paper proposes a low-cost turbidity system based on a light detection unit to measure the cloudiness in water. The automated system uses Intel Galileo 2 as the microprocessor and a server for a web-based monitoring system. The turbidity detection unit consists of a Light Dependent Resistor (LDR) and a Light Emitting Diode (LED) inside a polyvinyl chloride (PVC) pipe. Turbidity readings were recorded for two different positionings; 90° and 180° between the detector (LDR) and the incident light (LED). Once the turbidity level reached a threshold level, the system will trigger the filtration process to clean the water. The voltage output captured from the designed system versus total suspended solid (TSS) in sample water is graphed and analyzed in two different conditions; in total darkness and in the present of ambient light. This paper also discusses and compares the results from the above-mentioned conditions when the system is submerged in still and flowing water. It was found that the trends of the plotted graph decline when the total suspended solid increased for both 90° and 180° detector turbidimeter in all conditions which imitate the trends of a commercial turbidimeter. By taking the consideration of the above findings, the design can be recommended for a low-cost real-time web-based monitoring system of the water quality in an IOT environment

Extruded and overlapped geometries of feed spacers for solution mixing in electrochemical reactors and electrodialysis-related processes

Permeate spacers, which act as static mixers, are sandwiched at regular intervals between each pair of membranes. Spacers keep membranes separated, reinforce membranes against feed pressure, and increase turbulence. However, inefficient geometry of spacers may reduce cell active area and increase boundary layer effects near the membranes. The purpose of the present research is to discuss the designs of extruded and overlapped channel spacers. The current study is significant because it reveals the fundamental mechanisms that have a considerable impact on spacer-filled channel flow hydrodynamics. Extruded and overlapped spacers are usually composed of conventional polymeric material. The flow behavior in extruded and overlapped spacer-filled channel passages differs dramatically according to the geometry of the spacers. Spacer geometry determines flow dynamics, and mass transfer to energy loss rates. The addition of more transverse spacer strands in respect to the dominant flow direction enhances solution flow disturbance and lowers concentration polarization. Transverse filaments dominate all flow features, but longitudinals induce no impact. However, extruded and overlapped spacers reduce polarization while raising wakes and pressure drop. Spacers provide more boundary disturbance when oriented at a 45° spacer-bulk attack angle. An attack angle less than 45° results in a reduced pressure drop associated with a slight rate of wakes and flow disturbance because when filaments become nearly longitudinal to the flow direction, the poorer their influence. New spacer designs must demonstrate a favorable flow pattern of velocity vectors and maximized mass transfer rates, which could aid in improving membrane performance and cross-flow power consumption

Forensic analysis on printer inks via chemometrics

Printer inks are becoming necessary for utilization for wide range of purposes by society in current times with rapid development in technology and digital media area. Thus, forgery and counterfeiting becoming easier for the criminals. It is dangerous as some criminals will misused the technology by mean of addition and adulteration of parts of text or numbers on document as the inks and document can be made as an evidence in the trial court. Thus, the characterization and differentiation of the printed inks in the suspected documents (civil or criminal cases) may provide important information about the authenticity of the printer inks. The focus of this study to differentiate the chemical component of three different types of sample inks by incorporation of FTIR spectrophotometer with principal component analysis. The unique features of the ink samples were unmasked from the score plots of the principal component analysis. Thus, the graphical representation provided by the FTIR spectra with principal component analysis enabled the discrimination certain chemical in the printer inks