Preliminary Study of Renewable Pico Hydro Electrification Schemes to Improve Current Electrification Method in Royal Belum

PV/Diesel Power Generation Scheme was adopted to replace the stand-alone diesel power generation system in rural villages and schools in Malaysia. As the diesel fuel costs fluctuates highly over the year and the PV module showing inefficiency and incapability to totally replace diesel generators, hydro power is identified as an essential surplus to the current PV/Diesel system to reduce the fuel consumption in the system. Hence, this paper studies the preliminary elements and analyse the potential for PV/Diesel/Pico Hydro hybrid system to be applied in SK Sungai Tiang, Royal Belum, Perak. The comparison between PV/Diesel hybrid and PV/Diesel/Pico Hydro hybrid with emphasis placed on the amount of fuel savings and total cost needed to implement the PV/Diesel/Pico Hydro hybrid based on capital cost and operational & maintenance cost (O&M)

Assessing the Reliability of Template-Based Clustering for Tractography in Healthy Human Adults

Tractography is a non-invasive technique to investigate the brain’s structural pathways (also referred to as tracts) that connect different brain regions. A commonly used approach for identifying tracts is with template-based clustering, where unsupervised clustering is first performed on a template in order to label corresponding tracts in unseen data. However, the reliability of this approach has not been extensively studied. Here, an investigation into template-based clustering reliability was performed, assessing the output from two datasets: Human Connectome Project (HCP) and MyConnectome project. The effect of intersubject variability on template-based clustering reliability was investigated, as well as the reliability of both deep and superficial white matter tracts. Identified tracts were evaluated by assessing Euclidean distances from a dataset-specific tract average centroid, the volumetric overlap across corresponding tracts, and along-tract agreement of quantitative values. Further, two template-based techniques were employed to evaluate the reliability of different clustering approaches. Reliability assessment can increase the confidence of a tract identifying technique in future applications to study pathways of interest. The two different template-based approaches exhibited similar reliability for identifying both deep white matter tracts and the superficial white matter

An in vivo investigation of short-ranged structural connectivity in the human brain

Short-ranged connectivity comprise the majority of connections throughout the brain, joining together nearby regions and contributing to important networks that facilitate complex function and cognition. Despite constituting the majority of white matter in the brain and their importance, studies examining short-ranged connections have thus far been limited in part due to the challenges associated with identifying and validating them. Tractography, a computational technique for reconstructing axon trajectories from diffusion magnetic resonance imaging, has been commonly used to identify and study major white connections (e.g. corticospinal tract), which are easier to identify relative to the short-ranged connections. The use of additional constraints (e.g. geometry, regions of interest) together with tractography has enabled the ability to identify short-ranged connections of interest, such as the ”U”-shaped tracts residing just below the cortical surface, and the subcortical connectome tracts found in the deep brain. In this thesis, we aimed to quantify the reliability of such techniques for studying the short- ranged connections and applied them to examine changes to short-ranged connectivity in patients with first episode schizophrenia. First, the reliability of identifying short-ranged, ”U”- shaped tracts is examined in Chapter 1, leveraging geometric constraints for identifying the ”U”-shaped geometry together with clustering techniques to establish distinct tracts. Here, we two different clustering techniques, applying them to two datasets to study both the reliability of identifying short-ranged, ”U”-shaped tracts across different subjects and in a single individual (across different sessions). In Chapter 2, the reliability for identifying the subcortical connectome (short-ranged connections between subcortical structures) is evaluated. Connectivity of the deep brain is often hard to recapitulate due to the multiple orientations contributing to com- plex diffusion signals. Thus, we leveraged regions of interests determined through histological data to aid identification of the short-ranged connections in the compact region. Finally, Chapter 4, uses the techniques from chapter 2 in combination with quantitative measures sensitive to microstructural changes to study changes to short-ranged, ”U”-shaped tracts in the frontal lobes of patients with first-episode schizophrenia (FES). By studying the short-ranged connections in patients with FES, biomarkers associated with clinical presentation may be elucidated and may aid the current understanding to improve future treatment. Overall, the projects presented here quantify the reliability of current techniques for investigating short-ranged connectivity and provides a framework for evaluating of future techniques. Additionally, the techniques evaluated here can be used to elucidate new findings and improve treatment in clinical popula- tions

Evidence for sub-Chandrasekhar Type Ia supernovae from the last major merger

We investigate the contribution of sub-Chandrasekhar mass Type Ia supernovae to the chemical enrichment of the Gaia Sausage galaxy, the progenitor of a significant merger event in the early life of the Milky Way. Using a combination of data from Nissen & Schuster (2010), the 3rd GALAH data release (with 1D NLTE abundance corrections) and APOGEE data release 16, we fit analytic chemical evolution models to a 9-dimensional chemical abundance space (Fe, Mg, Si, Ca, Cr, Mn, Ni, Cu, Zn) in particular focusing on the iron-peak elements, Mn and Ni. We find that low [Mn/Fe] $\sim-0.15\,\mathrm{dex}$ and low [Ni/Fe] $\sim-0.3\,\mathrm{dex}$ Type Ia yields are required to explain the observed trends beyond the [$\alpha$/Fe] knee of the Gaia Sausage (approximately at [Fe/H] $=-1.4\,\mathrm{dex}$). Comparison to theoretical yield calculations indicate a significant contribution from sub-Chandrasekhar mass Type Ia supernovae in this system (from $\sim60$% to $100$% depending on the theoretical model with an additional $\pm10$% systematic from NLTE corrections). We compare to results from other Local Group environments including dwarf spheroidal galaxies, the Magellanic Clouds and the Milky Way's bulge, finding the Type Ia [Mn/Fe] yield must be metallicity-dependent. Our results suggest that sub-Chandrasekhar mass channels are a significant, perhaps even dominant, contribution to Type Ia supernovae in metal-poor systems, whilst more metal-rich systems could be explained by metallicity-dependent sub-Chandrasekhar mass yields, possibly with additional progenitor mass variation related to star formation history, or an increased contribution from Chandrasekhar mass channels at higher metallicity.Comment: 23 pages, 12 figures, resubmitted to MNRAS following referee's comment

Techniques for Deblurring Faces in Images by Utilizing Multi-Camera Fusion

This publication describes techniques for deblurring faces in images by utilizing multi-camera (e.g., dual-camera) fusion processes. In the techniques, multiple cameras of a computing device (e.g., wide-angle camera, an ultrawide-angle camera) concurrently capture a scene. A multi-camera fusion technique is utilized to fuse the captured images together to generate an image with increased sharpness while preserving the brightness of the scene and other details under a motion scene. The images are processed by a Deblur Module, which includes an optical flow machine-learned model for generating a warped ultrawide-angle image, a subject mask trained to identify and mask faces detected in the wide-angle image, and an occlusion mask for handling occlusion artifacts. The warped ultrawide-angle image, the raw wide-angle image (with blurred faces), the sharp ultrawide-angle image, the subject mask, and the occlusion map are then stacked and merged (fused) using a machine-learning model to output a sharp image without the presence of motion blur. This publication further describes techniques utilizing adaptive multi-streaming to optimize power consumption and dual camera usage on computing devices

Diffusion dispersion imaging: Mapping oscillating gradient spin-echo frequency dependence in the human brain.

PURPOSE: Oscillating gradient spin-echo (OGSE) diffusion MRI provides information about the microstructure of biological tissues by means of the frequency dependence of the apparent diffusion coefficient (ADC). ADC dependence on OGSE frequency has been explored in numerous rodent studies, but applications in the human brain have been limited and have suffered from low contrast between different frequencies, long scan times, and a limited exploration of the nature of the ADC dependence on frequency. THEORY AND METHODS: Multiple frequency OGSE acquisitions were acquired in healthy subjects at 7T to explore the power-law frequency dependence of ADC, the diffusion dispersion. Furthermore, a method for optimizing the estimation of the ADC difference between different OGSE frequencies was developed, which enabled the design of a highly efficient protocol for mapping diffusion dispersion. RESULTS: For the first time, evidence of a linear dependence of ADC on the square root of frequency in healthy human white matter was obtained. Using the optimized protocol, high-quality, full-brain maps of apparent diffusion dispersion rate were also demonstrated at an isotropic resolution of 2 mm in a scan time of 6 min. CONCLUSIONS: This work sheds light on the nature of diffusion dispersion in the healthy human brain and introduces full-brain diffusion dispersion mapping at clinically relevant scan times. These advances may lead to new biomarkers of pathology or improved microstructural modeling