Journal Articles

Permanent URI for this collectionhttps://mro.massey.ac.nz/handle/10179/7915

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Now showing 1 - 10 of 11
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    Combined injection of rAAV with mannitol enhances gene expression in the rat brain
    (Cell Press, 6/09/2000) Mastakov M; Baer K; Xu R; Fitzsimons H; During M
    Recombinant adeno-associated viruses (rAAV) are highly efficient vectors for gene transfer into the central nervous system (CNS). However, a major hurdle for gene delivery to the mammalian brain is to achieve high-level transduction in target cells beyond the immediate injection site. Therefore, in addition to improvements in expression cassettes and viral titers, optimal injection parameters need to be defined. Here, we show that previous studies of somatic cell gene transfer to the mammalian brain have used suboptimal injection parameters, with even the lowest reported perfusion rates still excessively fast. Moreover, we evaluated the effect of local administration of mannitol to further enhance transgene expression and vector spread. Ultraslow microperfusion of rAAV, i.e., <33 nl/min, resulted in significantly higher gene expression and less injury of surrounding tissue than the previously reported rates of 100 nl/min or faster. Co-infusion of mannitol facilitated gene transfer to neurons, increasing both the total number and the distribution of transduced cells by 200-300%. Gene transfer studies in the CNS using rAAV should use very slow infusion rates and combined injection with mannitol to maximize transduction efficiency and spread.
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    Data mining and influential analysis of gene expression data for plant resistance genes identification in tomato (Solanum lycopersicum)
    (Elsevier, 2014-03) Torres-Aviles F; Romeo JS; Lopez-Kleine L
    Background Molecular mechanisms of plant–pathogen interactions have been studied thoroughly but much about them is still unknown. A better understanding of these mechanisms and the detection of new resistance genes can improve crop production and food supply. Extracting this knowledge from available genomic data is a challenging task. Results Here, we evaluate the usefulness of clustering, data-mining and regression to identify potential new resistance genes. Three types of analyses were conducted separately over two conditions, tomatoes inoculated with Phytophthora infestans and not inoculated tomatoes. Predictions for 10 new resistance genes obtained by all applied methods were selected as being the most reliable and are therefore reported as potential resistance genes. Conclusion Application of different statistical analyses to detect potential resistance genes reliably has shown to conduct interesting results that improve knowledge on molecular mechanisms of plant resistance to pathogens.
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    Device-Free Localization Using Privacy-Preserving Infrared Signatures Acquired from Thermopiles and Machine Learning
    (IEEE, 4/06/2021) Faulkner N; Alam F; Legg M; Demidenko S
    The development of an accurate passive localization system utilizing thermopile sensing and artificial intelligence is discussed in this paper. Several machine learning techniques are explored to create robust angular and radius coordinate models for a localization target with respect to thermopile sensors. These models are leveraged to develop a reconfigurable passive localization system that can use a varying number of thermopiles without the need for retraining. The proposed robust system achieves high localization accuracy (with the median error between 0.13 m and 0.2 m) while being trained using a single human subject and tested against multiple other subjects. It is shown that the proposed system does not experience any significant performance deterioration when localizing a subject at different ambient temperatures or with different configurations of the thermopile sensors placement.
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    A Machine Learning Approach to Enhance the Performance of D2D-Enabled Clustered Networks
    (IEEE, 20/01/2021) Aslam S; Alam F; Hasan SF; Rashid MA
    Clustering has been suggested as an effective technique to enhance the performance of multicasting networks. Typically, a cluster head is selected to broadcast the cached content to its cluster members utilizing Device-to-Device (D2D) communication. However, some users can attain better performance by being connected with the Evolved Node B (eNB) rather than being in the clusters. In this article, we apply machine learning algorithms, namely Support Vector Machine, Random Forest, and Deep Neural Network to identify the users that should be serviced by the eNB. We therefore propose a mixed-mode content distribution scheme where the cluster heads and eNB service the two segregated groups of users to improve the performance of existing clustering schemes. A D2D-enabled multicasting scenario has been set up to perform a comprehensive simulation study that demonstrates that by utilizing the mixed-mode scheme, the performance of individual users, as well as the whole network, improve significantly in terms of throughput, energy consumption, and fairness. This study also demonstrates the trade-off between eNB loading and performance improvement for various parameters.
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    SpringLoc: A device-free localization technique for indoor positioning and tracking using adaptive RSSI spring relaxation
    (Institute of Electrical and Electronics Engineers (IEEE), 5/05/2019) Konings D; Alam F; Noble F; Lai E
    Device-free localization (DFL) algorithms using the received signal strength indicator (RSSI) metrics have become a popular research focus in recent years as they allow for location-based service using commercial-off-the-shelf (COTS) wireless equipment. However, most existing DFL approaches have limited applicability in realistic smart home environments as they typically require extensive offline calibration, large node densities, or use technology that is not readily available in commercial smart homes. In this paper, we introduce SpringLoc and a DFL algorithm that relies on simple parameter tuning and does not require offline measurements. It localizes and tracks an entity using an adaptive spring relaxation approach. The anchor points of the artificial springs are placed in regions containing the links that are affected by the entity. The affected links are determined by comparing the kernel-based histogram distance of successive RSSI values. SpringLoc is benchmarked against existing algorithms in two diverse and realistic environments, showing significant improvement over the state-of-the-art, especially in situations with low-node deployment density.
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    The Application of Machine Learning to Consolidate Critical Success Factors of Lean Six Sigma
    (IEEE, 17/08/2021) Perera AD; Jayamaha NP; Grigg NP; Tunnicliffe M; Singh A
    Lean six sigma (LSS) is a quality improvement phenomenon that has captured the attention of the industry. Aiming at a capability level of 3.4 defects per million opportunities (Six Sigma) and efficient (lean) processes, LSS has been shown to improve business efficiency and customer satisfaction by blending the best methods from Lean and Six Sigma (SS). Many businesses have attempted to implement LSS, but not everyone has succeeded in improving the business processes to achieve expected outcomes. Hence, understanding the cause and effect relationships of the enablers of LSS, while deriving deeper insights from the functioning of the LSS strategy will be of great value for effective execution of LSS. However, there is little research on the causal mechanisms that explain how expected outcomes are caused through LSS enablers, highlighting the need for comprehensive research on this topic. LSS literature is overwhelmed by the diverse range of Critical Success Factors (CSFs) prescribed by a plethora of conceptual papers, and very few attempts have been made to harness these CSFs to a coherent theory on LSS. We fill this gap through a novel method using artificial intelligence, more specifically Natural Language Processing (NLP), with particular emphasis on cross-domain knowledge utilization to develop a parsimonious set of constructs that explain the LSS phenomenon. This model is then reconciled against published models on SS to develop a final testable model that explains how LSS elements cause quality performance, customer satisfaction, and business performance.
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    Gender Diversity Population Simulations in an Extended Game of Life Context
    (IEEE, 20/06/2019) Mathrani A; Scogings C; Mathrani S
    Cellular automata studies have been instrumental in computational and biological studies for simulating life contours based on simple rule-based strategies. Game of Life (GoL) presented us with one of the earliest automata studies that led the way in exemplifying non-linear spatial representations, such as large-scale population evolution scenarios depicting species dominance, species equilibrium, and species extinction. However, the GoL was driven by interactions among vegetative entities comprising live and die states only. This paper extends GoL to gendered-GoL (g-GoL) in which male phenotypes and female phenotypes interact in an extended world to procreate. Using the g-GoL, we have demonstrated many evolution contours by applying gender-based dependence rules. Evolution scenarios have been simulated with skewed gender ratios that favor the birth of male offspring. Preference for a male child is common in certain cultures; therefore, empirical data realized with skewed gender settings in g-GoL can reveal the long-term impact of non-egalitarian gender societal structures. Our model provides a tool for the study of emergent life contours and brings awareness on current gender imbalances to strengthen multi-disciplinary research inquiry in the areas of social practices, mathematical modeling, and use of computational technologies.
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    CapLoc: Capacitive Sensing Floor for Device-Free Localization and Fall Detection
    (IEEE Xplore, 12/10/2020) Faulkner N; Parr B; Alam F; Legg M; Demidenko S
    Passive indoor positioning, also known as Device-Free Localization (DFL), has applications such as occupancy sensing, human-computer interaction, fall detection, and many other location-based services in smart buildings. Vision-, infrared-, wireless-based DFL solutions have been widely explored in recent years. They are characterized by respective strengths and weaknesses in terms of the desired accuracy, feasibility in various real-world scenarios, etc. Passive positioning by tracking the footsteps on the floor has been put forward as one of the promising options. This article introduces CapLoc, a floor-based DFL solution that can localize a subject in real-time using capacitive sensing. Experimental results with three individuals walking 39 paths on the CapLoc show that it can detect and localize a single target's footsteps accurately with a median localization error of 0.026 m. The potential for fall detection is also shown with the outlines of various poses of the subject lying upon the floor.
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    Dual-band waveform generator with ultra-wide low-frequency tuning-range
    (Institute of Electrical and Electronics Engineers (IEEE), 22/04/2016) AL-DARKAZLY IAA; Hasan SMR
    This paper presents a novel mixed-signal low-power dual-band square/triangular waveform generator (WFG) chip with a wide low-frequency tuning range for medical bio-electric stimulation therapy. It consists of a relaxation oscillator comprising a hysteresis Schmitt trigger and a timing integrator, along with frequency divider (FD) stages and path selector output for driving an electrode from 16 selectable channels. It was fabricated using Global Foundries 8RF-DM 130-nm CMOS process with a supply voltage of ±1 V for the oscillator and +1 V for logic circuits. The WFG provides an output of around 1.5 Vp-p at a nominal low oscillation frequency of 17 kHz using small-size on-chip passive components of values 10 kΩ and 10 pF. The WFG core (band I) can be tuned in the range 6.44-1003 kHz through bias current adjustment, while a lower frequency (band II) in the range 0.1 Hz-502 kHz can be provided digitally through a 2 stage. The power consumption was only 0.457 mW for the WFG and 2.1 mW for the FD circuit while occupying a total silicon area of only 18 426 μm2.
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    A Performance-Optimized Deep Learning-based Plant Disease Detection Approach for Horticultural Crops of New Zealand
    (Institute of Electrical and Electronics Engineers, 23/08/2022) Saleem MH; Potgieter J; Arif K
    Deep learning-based plant disease detection has gained significant attention from the scientific community. However, various aspects of real horticultural conditions have not yet been explored. For example, the disease should be considered not only on leaves, but also on other parts of plants, including stems, canes, and fruits. Furthermore, the detection of multiple diseases in a single plant organ at a time has not been performed. Similarly, plant disease has not been identified in various crops in the complex horticultural environment with the same optimized/modified model. To address these research gaps, this research presents a dataset named NZDLPlantDisease-v1, consisting of diseases in five of the most important horticultural crops in New Zealand: kiwifruit, apple, pear, avocado, and grapevine. An optimized version of the best obtained deep learning (DL) model named region-based fully convolutional network (RFCN) has been proposed to detect plant disease using the newly generated dataset. After finding the most suitable DL model, the data augmentation techniques were successively evaluated. Subsequently, the effects of image resizers with interpolators, weight initializers, batch normalization, and DL optimizers were studied. Finally, performance was enhanced by empirical observation of position-sensitive score maps and anchor box specifications. Furthermore, the robustness/practicality of the proposed approach was demonstrated using a stratified k-fold cross-validation technique and testing on an external dataset. The final mean average precision of the RFCN model was found to be 93.80%, which was 19.33% better than the default settings. Therefore, this research could be a benchmark step for any follow-up research on automatic control of disease in several plant species.