RSS 1.0Bradford Scholars is the University of Bradford online research archive. Access is free to anyone interested in research being conducted at Bradford. In the repository you will find a range of materials from journal articles and conference papers to research reports and theses.
Contact the repository team via openaccess@bradford.ac.uk with any queries about Open Access or how to deposit your research papers.
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Solar-powered direct contact membrane distillation system: performance and water cost evaluationFresh water is crucial for life, supporting human civilizations and ecosystems, and its production is one of the global issues. To cope with this issue, we evaluated the performance and cost of a solar-powered direct contact membrane distillation (DCMD) unit for fresh water production in Karachi, Pakistan. The solar water heating system (SWHS) was evaluated with the help of a system advisor model (SAM) tool. The evaluation of the DCMD unit was performed by solving the DCMD mathematical model through a numerical iterative method in MATLAB software®. For the SWHS, the simulation results showed that the highest average temperature of 55.05 ◦C and lowest average temperature of 44.26 ◦C were achieved in May and December, respectively. The capacity factor and solar fraction of the SWHS were found to be 27.9% and 87%, respectively. An exponential increase from 11.4 kg/m2 ·h to 23.23 kg/m2 ·h in permeate flux was observed when increasing the hot water temperatures from 44 ◦C to 56 ◦C. In the proposed system, a maximum of 279.82 L/day fresh water was produced in May and a minimum of 146.83 L/day in January. On average, the solar-powered DCMD system produced 217.66 L/day with a levelized water cost of 23.01 USD/m3
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Synthesis of Ce3+ substituted Ni-Co ferrites for high frequency and memory storage devices by sol-gel routeCerium (Ce3+) substituted Ni-Co ferrites with composition Ni0.3Co0.7CexFe2−xO4 (x = 0.0–0.20, with step size 0.05) were synthesized by sol-gel method. Face-centered cubic (FCC) spinel structure was revealed by X-ray analysis. The crystalline size was calculated ranging between 17.1 and 18.8 nm, lattice constant showed a decreasing trend with increase of Ce3+ contents, furthermore, X-ray density was calculated between 5.30 and 5.69 g/cm3. The two characteristic spinel ferrites absorption bands were seen around 550 (cm−1) and 415 (cm−1) in Fourier transform infra-red (FTIR) spectroscopy. The microstructural and elemental studies were carried out by field emission transmission electron microscopy (FE-TEM) and energy dispersive X-ray (EDX) respectively, the average particle size was calculated around 21.83 nm. Magnetic studies were per- formed by vibrating sample magnetometer (VSM), which showed that saturation magnetization Ms and remanence Mr decreased with substitution up to x = 0.10 due to small magnetic moment of Ce3+ than Fe3+. The coercivity Hc increased with substitution up to 908.93 Oe at x = 0.05, then it decreased following the trend of anisotropy constant. The dielectric studies exhibited decrease in dielectric parameters with fre- quency due to decreasing polarization in material. The dielectric loss was significantly decreased in material at high frequency. The Cole-Cole interpretation exhibited conduction mechanism being caused by grain boundary density. These attributes of Ce3+ substituted Ni-Co ferrites suggest their possible use in memory storage, switching and high frequency devices like antenna and satellite systems.
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A smart sound fingerprinting system for monitoring elderly people living aloneThere is a sharp increase in the number of old people living alone throughout the world. More often than not, such people require continuous and immediate care and attention in their everyday lives, hence the need for round the clock monitoring, albeit in a respectful, dignified and non-intrusive way. For example, continuous care is required when they become frail and less active, and immediate attention is required when they fall or remain in the same position for a long time. To this extent, various monitoring technologies have been developed, yet there are major improvements still to be realised. Current technologies include indoor positioning systems (IPSs) and health monitoring systems. The former relies on defined configurations of various sensors to capture a person's position within a given space in real-time. The functionality of the sensors varies depending on receiving appropriate data using WiFi, radio frequency identification (RFIO), ultrawide band (UWB), dead reckoning (OR), infrared indoor (IR), Bluetooth (BLE), acoustic signal, visible light detection, and sound signal monitoring. The systems use various algorithms to capture proximity, location detection, time of arrival, time difference of arrival angle, and received signal strength data. Health monitoring technologies capture important health data using accelerometers and gyroscope sensors. In some studies, audio fingerprinting has been used to detect indoor environment sound variation and have largely been based on recognising TV sound and songs. This has been achieved using various staging methods, including pre-processing, framing, windowing, time/frequency domain feature extraction, and post-processing. Time/frequency domain feature extraction tools used include Fourier Transforms (FTs}, Modified Discrete Cosine Transform (MDCT}, Principal Component Analysis (PCA), Mel-Frequency Cepstrum Coefficients (MFCCs), Constant Q Transform (CQT}, Local Energy centroid (LEC), and Wavelet transform. Artificial intelligence (Al) and probabilistic algorithms have also been used in IPSs to classify and predict different activities, with interesting applications in healthcare monitoring. Several tools have been applied in IPSs and audio fingerprinting. They include Radial Basis Kernel (RBF), Support Vector Machine (SVM), Decision Trees (DTs), Hidden Markov Models (HMMs), Na'ive Bayes (NB), Gaussian Mixture Modelling (GMM), Clustering algorithms, Artificial Neural Networks (ANNs), and Deep Learning (DL). Despite all these attempts, there is still a major gap for a completely non-intrusive system capable of monitoring what an elderly person living alone is doing, where and for how long, and providing a quick traffic-like risk score prompting, therefore immediate action or otherwise. In this thesis, a cost-effective and completely non-intrusive indoor positioning and activity-monitoring system for elderly people living alone has been developed, tested and validated in a typical residential living space. The proposed system works based on five phases: (1)Set-up phase that defines the typical activities of daily living (TADLs). (2)Configuration phase that optimises the implementation of the required sensors in exemplar flat No.1. (3)Learning phase whereby sounds and position data of the TADLs are collected and stored in a fingerprint reference data set. (4)Listening phase whereby real-time data is collected and compared against the reference data set to provide information as to what a person is doing, when, and for how long. (5)Alert phase whereby a health frailty score varying between O unwell to 10 healthy is generated in real-time. Two typical but different residential flats (referred to here are Flats No.1 and 2) are used in the study. The system is implemented in the bathroom, living room, and bedroom of flat No.1, which includes various floor types (carpet, tiles, laminate) to distinguish between various sounds generated upon walking on such floors. The data captured during the Learning Phase yields the reference data set and includes position and sound fingerprints. The latter is generated from tests of recording a specific TADL, thus providing time and frequency-based extracted features, frequency peak magnitude (FPM), Zero Crossing Rate (ZCR), and Root Mean Square Error (RMSE). The former is generated from distance measurement. The sampling rate of the recorded sound is 44.1kHz. Fast Fourier Transform (FFT) is applied on 0.1 seconds intervals of the recorded sound with minimisation of the spectral leakage using the Hamming window. The frequency peaks are detected from the spectrogram matrices to get the most appropriate FPM between the reference and sample data. The position detection of the monitored person is based on the distance between that captured from the learning and listening phases of the system in real-time. A typical furnished one-bedroom flat (flat No.2) is used to validate the system. The topologies and floorings of flats No.1 and No.2 are different. The validation is applied based on "happy" and "unusual" but typical behaviours. Happy ones include typical TADLs of a healthy elderly person living alone with a risk metric higher than 8. Unusual one's mimic acute or chronic activities (or lack thereof), for example, falling and remaining on the floor, or staying in bed for long periods, i.e., scenarios when an elderly person may be in a compromised situation which is detected by a sudden drop of the risk metric (lower than 4) in real-time. Machine learning classification algorithms are used to identify the location, activity, and time interval in real-time, with a promising early performance of 94% in detecting the right activity and the right room at the right time.
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Digital Education Resource Mining for Decision SupportNowadays education becomes a competitive and challenging domain, both nationally and internationally in terms of quality, visibility, experience of academic delivery affecting institutions, applicants, regulatory bodies. Currently data becomes more available for the general and public use, and plays also an increasingly significant role in decision support for education topics. For example, world university rankings (WUR) such as Quacquarelli Symonds (QS), Central World University Rankings (CWUR), Times Higher Education (Times) and national university rankings (e.g. the Guardian newspaper Best UK Universities and the Complete University Guide league tables) have published their data for many years now and are increasingly used in such decision making processes by institutions and general public.
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Towards The Total Synthesis Of Withanolide E And Physachenolide CWithanolides are a class of ergostane natural products found in plants of family Solanaceae. Plants of this family are used in traditional medicine in Asia and South America. Recently, a series of 17β-hydroxy withanolides were identified from high-throughput screens as inhibitors of androgen-induced changes in gene expression of prostate cancer cells. Therefore, these compounds may have important applications as new therapies against prostate cancer. We have devised a synthetic route to members of this family and their analogues which allows stereoselective introduction of C14, C17 and C20 hydroxyl groups in separate steps. This will allow preparation of differentially hydroxylated analogues so as to identify which contributes to the potency and thus gain a better understanding of the SAR of this class of bioactive molecules. As part of this we have shown that the stereochemical outcome of the epoxidation of Δ 14-15 cholestanes with m-CPBA is controlled by the steric bulk of a C17 substituent. When the C17 is in the β configuration, the epoxide is formed on the α face, whereas if the C17 is trigonal (flat) or the substituent is in the α configuration, the epoxide is formed on the β face. The presence of a hydroxyl substituent at C20 does not influence the stereochemical outcome of the epoxidation. We have successfully introduced aldehyde functionality to the lateral side chain 14 hydroxyl compound. This aldehyde compound is a key intermediate from which many of the withanolides can be made. We have also investigated the introduction of a hydroxyl at the C18 as an entry into the physachenolides. Finally, we have carried out an assessment of the potency of the synthesised compounds against hormone-insensitive prostate cancer cell line, PC-3.
