Editorial A New Home for Xjenza Online Cristiana Sebu PDF
Article Learning to Invert Pseudo-Spectral Data for Seismic Waveforms Christopher Zerafa, Pauline Galea and Cristiana Sebu Pages: 3 - 17 Read Abstract | View full article |
PDF Abstract: Full-waveform inversion (FWI) is a widely adopted technique used in seismic processing to produce high resolution Earth models, that fully explain the recorded seismic data. FWI is a local optimisation problem which aims to minimise, using a least-squares approach, the misfit between recorded and modelled data. The inversion process begins with a best-guess initial model which is iteratively improved using a sequence of linearised local inversions to solve a fully non-linear problem. Deep learning has gained widespread popularity in the new millennium. At the core of these tools are Neural Networks (NN), in particular Deep Neural Networks (DNN), which are variants of these original NN algorithms with significantly more hidden layers, resulting in efficient learning of a non-linear function between input and output pairs. The learning process within DNN involves repeatedly updating network neuron weights to best approximate input-to-output mappings. There is clear similarity between FWI and DNN as both approaches attempt to solve non-linear mapping in an iterative sense. However, they are fundamentally different in that FWI is knowledge-driven, whereas DNN is data-driven. This article proposes a novel approach which learns pseudo-spectral data-driven FWI. We test this methodology by training a DNN on 1D multi-layer, horizontally-isotropic data and then apply this to previously unseen data to infer the surface velocity. Results are compared against a synthetic model and success and failures of this approach are hence identified.
Doi: http://dx.medra.org/10.7423/XJENZA.2019.1.01
Article A Preliminary Assessment of the Effciency of Using Drones in Land Cover Mapping Andrea Francesca Bellia and Sandro Lanfranco Pages: 18 - 27 Read Abstract | View full article |
PDF Abstract: This study represents a preliminary assessment of the efficiency of drones in surveying land cover at both large (c: 10 ha) and smaller (1m2) spatial scales. A DJI Mavic 2 drone was used to image the entire area of study and an orthomosaic was produced. This was converted into a land cover map through k-means clustering, with k = 3, where `Vegetation', `Bedrock' and `Bare soil' corresponded to the land cover categories. Regions of interest (ROIs) were selected and sub- sequently surveyed from close range. The correspondence between predicted land cover (pLC) and observed land cover (oLC) was then assessed. On a large spatial scale, absolute correspondence was present between pLC and oLC. In terms of relative representation of land cover categories, `Vegetation' was the only significantly correlated category across pLC and oLC, whilst the analogous correlations for `Bedrock' and `Bare soil' were weaker. The lower correspondence between pLC and oLC for `Bedrock' and `Bare soil' was due to the low value of k = 3 in the k-means clustering algorithm. This constrains a mixture of land covers into just one land cover category, with consequent reduction of the correlation between pLC and oLC. The method's accuracy and cost-effectiveness were compared to that of standard methods for land cover surveying. The entire process, including verification and orthomosaic land cover map processing times, approximated 32 hours. Consequently, this method is much shorter than standard surveys, which take days or weeks, and also requires less manpower.
Doi: http://dx.medra.org/10.7423/XJENZA.2019.1.02
Article Axial Flux Permanent Magnet Motor Design and Optimisation by Using Artificial Neural Networks Tuğçe Talay and Kadir Erkan Pages: 28 - 36 Read Abstract | View full article |
PDF Abstract: In this study, the necessary steps for the design of axial ux permanent magnet motors are shown. The design and analysis of the engine were carried out based on ANSYS Maxwell program. The design parameters of the ANSYS Maxwell program and the artificial neural network system were established in MATLAB, and the most efficient design parameters were found with the trained neural network. The results of the Maxwell program were compared with the results of the artificial neural networks, and optimal working design parameters were found. The most efficient design parameters were submitted to the ANSYS Maxwell 3D design, the cogging torque was subsequently examined and design studies were carried out to reduce the cogging torque.
Doi: http://dx.medra.org/10.7423/XJENZA.2019.1.03
Article The Evolution of Malta's Tourism Sector Silvio Attard Pages: 37 - 48 Read Abstract | View full article |
PDF Abstract: The study aims to analyse historic developments in Malta's tourism industry, focussing on the changing characteristics of demand and supply. The recent surge in inbound tourism appears to be largely driven by increased air connectivity to and from Malta. The advent of low-cost carriers is considered an important positive supply shock on the local sector. At the same time, the sustained shift towards stays in private accommodation can be partly explained by changing preferences, but also by capacity constraints in collective accommodation establishments. Moreover, the pa- per compares a number of indicators which shed light on the economic importance of tourism for the Maltese economy. It also examines the issue concerning sustain- able growth of the sector and seeks to draw some policy inferences.
Doi: http://dx.medra.org/10.7423/XJENZA.2019.1.04
Article Iron and its alloys for Bone Regeneration Scaffolds - A Review Christabelle Tonna and Luke Saliba Pages: 49 - 64 Read Abstract | View full article |
PDF Abstract: Permanent implants and bone grafts have been used successfully to repair bone defects for a number of years. However, there are significant limitations, for example patients requiring revision surgery for implant removal, inadequate mechanical properties leading to stress-shielding and osteoporosis, as well as restricted bone development, particularly in paediatric patients. As a result, those implants with a more active involvement in the healing process than the original inert implants, were favoured. Biodegradable scaffolds are porous implants which are incorporated into sizeable bone defects in order to support the damaged area while the bone regenerates. In response to bone healing, the structure is expected to degrade at a controlled rate in vivo. Following the promising research published in relation to magnesium-based alloys for cardiovascular stents, iron and its alloys have recently been proposed for this application. An in vivo study published in 2001 showed that pure iron exhibited an inadequately slow degradation rate. Since then, research efforts have been focused on accelerating the corrosion rate by implementing various material design strategies. This review presents an overview of notable research work treating the tailoring of corrosion, mechanical and cytotoxic response as well as promising processing methods for the production of iron-based foam structures. To conclude, based on current research, the clinical potential for these materials will be analysed.
Doi: http://dx.medra.org/10.7423/XJENZA.2019.1.05
Article The Use of Cannabinoids in Parkinson's Disease Francesca Borg and Giuseppe Di Giovanni Pages: 65 - 80 Read Abstract | View full article |
PDF Abstract: Parkinson's disease (PD) is a very common neurodegenerative disorder in the elderly for which there is no current cure. The neuropathological hallmark is the loss of dopaminergic cells in the substantia nigra pars compacta. Current treatments use L-DOPA and dopamine agonists to replace the lack of dopamine, however such treatments have significant limitations and side effects, thus, the need for more effective therapeutics is critical. Cannabinoids (CBs), which include 9-tetrahydrocannabinol, cannabidiol and 9-tetrahydrocannabivarin, target the endocannabinoid (ECB) system, which is highly involved in dopaminergic functions. The endocannabinoid system undergoes extensive changes in PD such as upregulation of the ECB anandamide, in addition to variations in the concentration of CB receptors. These changes can be modified and corrected using CB1 and CB2 receptor ligands and by modulating the levels of the ECB catabolic enzyme fatty acid amide hydrolase (FAAH), in order to increase endogenous anandamide (AEA) levels. Therefore, CBs may represent a valid therapeutic alternative to treat PD. CB drugs may not only treat the symptoms of the disease, but may also help slow down disease progression. Nevertheless, with regards to motor symptoms of PD such as rigidity, bradykinesia, postural instability, resting tremors and levodopa-induced dyskinesia, evidence of the therapeutic e ect of CBs is somewhat inconsistent. Although only evidence in the preclinical phase, more promising results have been seen in general regarding the neuroprotective effect of CBs, as well as in relation to sleep, depression and pain.
Doi: http://dx.medra.org/10.7423/XJENZA.2019.1.06
Article Malta's Science and Arts Festival Focuses on the Science of YOU Edward Duca Pages: 81 - 82 View full article |
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