938 resultados para School network


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This project was an innovative approach in developing smart coordination of available energy resources to improve the integration level of PV in distribution network. Voltage and loading issues are considered as the main concerns for future electricity grid which need to be avoided using such resources. A distributed control structure was proposed for the resources in distribution network to avoid noted power quality issues.

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The use of Wireless Sensor Networks (WSNs) for vibration-based Structural Health Monitoring (SHM) has become a promising approach due to many advantages such as low cost, fast and flexible deployment. However, inherent technical issues such as data asynchronicity and data loss have prevented these distinct systems from being extensively used. Recently, several SHM-oriented WSNs have been proposed and believed to be able to overcome a large number of technical uncertainties. Nevertheless, there is limited research verifying the applicability of those WSNs with respect to demanding SHM applications like modal analysis and damage identification. Based on a brief review, this paper first reveals that Data Synchronization Error (DSE) is the most inherent factor amongst uncertainties of SHM-oriented WSNs. Effects of this factor are then investigated on outcomes and performance of the most robust Output-only Modal Analysis (OMA) techniques when merging data from multiple sensor setups. The two OMA families selected for this investigation are Frequency Domain Decomposition (FDD) and data-driven Stochastic Subspace Identification (SSI-data) due to the fact that they both have been widely applied in the past decade. Accelerations collected by a wired sensory system on a large-scale laboratory bridge model are initially used as benchmark data after being added with a certain level of noise to account for the higher presence of this factor in SHM-oriented WSNs. From this source, a large number of simulations have been made to generate multiple DSE-corrupted datasets to facilitate statistical analyses. The results of this study show the robustness of FDD and the precautions needed for SSI-data family when dealing with DSE at a relaxed level. Finally, the combination of preferred OMA techniques and the use of the channel projection for the time-domain OMA technique to cope with DSE are recommended.

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Wind speed measurement systems are sparse in the tropical regions of Australia. Tropical cyclone wind speeds impacting communities are often ‘guestimated’ from analyzing damaged structures. A re-locatable anemometer system is required to enable measurements of wind speeds. This paper discusses design criteria of the tripods and tie down system, proposed deployment of the anemometers, instrumentation, and data logging. Preliminary assessment of the anemometer response indicates a reliable system for 1 second response, however, it is noted that the Australian building code and wind loading standard uses a moving average time of approximately 0.2 seconds for its wind speed design criteria.

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The practice of medicine has always aimed at individualized treatment of disease. The relationship between patient and physician has always been a personal one, and the physician's choice of treatment has been intended to be the best fit for the patient's needs. The necessary pooling/grouping of disease families and their assignment to a number of drugs or treatment methods has, consequently, led to an increase in the number of effective therapies. However, given the heterogeneity of most human diseases, and cancer specifically, it is currently impossible for the treating clinician to effectively predict a patient's response and outcome based on current technologies, much less the idiosyncratic resistances and adverse effects associated with the limited therapeutic options.

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Wind speed measurement systems are sparse in the tropical regions of Australia. Given this, tropical cyclone wind speeds impacting communities are seldom measured and often only ‘guestimated’ by analysing the extent of damage to structures. In an attempt to overcome this dearth of data, a re-locatable network of anemometers to be deployed prior to tropical cyclone landfall is currently being developed. This paper discusses design criteria of the network’s tripods and tie down system, proposed deployment of the anemometers, instrumentation and data logging. Preliminary assessment of the anemometer response indicates a reliable system for measuring the spectral component of wind with frequencies of approximately 1 Hz. This system limitation highlights an important difference between the capabilities of modern instrumentation and that of the Dines anemometer (around 0.2 seconds) that was used to develop much of the design criteria within the Australian building code and wind loading standard.

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Inhibition of cholesterol export from late endosomes causes cellular cholesterol imbalance, including cholesterol depletion in the trans-Golgi network (TGN). Here, using Chinese hamster ovary (CHO) Niemann-Pick type C1 (NPC1) mutant cell lines and human NPC1 mutant fibroblasts, we show that altered cholesterol levels at the TGN/endosome boundaries trigger Syntaxin 6 (Stx6) accumulation into VAMP3, transferrin, and Rab11-positive recycling endosomes (REs). This increases Stx6/VAMP3 interaction and interferes with the recycling of αVβ3 and α5β1 integrins and cell migration, possibly in a Stx6-dependent manner. In NPC1 mutant cells, restoration of cholesterol levels in the TGN, but not inhibition of VAMP3, restores the steady-state localization of Stx6 in the TGN. Furthermore, elevation of RE cholesterol is associated with increased amounts of Stx6 in RE. Hence, the fine-tuning of cholesterol levels at the TGN-RE boundaries together with a subset of cholesterol-sensitive SNARE proteins may play a regulatory role in cell migration and invasion.

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Cancer can be defined as a deregulation or hyperactivity in the ongoing network of intracellular and extracellular signaling events. Reverse phase protein microarray technology may offer a new opportunity to measure and profile these signaling pathways, providing data on post-translational phosphorylation events not obtainable by gene microarray analysis. Treatment of ovarian epithelial carcinoma almost always takes place in a metastatic setting since unfortunately the disease is often not detected until later stages. Thus, in addition to elucidation of the molecular network within a tumor specimen, critical questions are to what extent do signaling changes occur upon metastasis and are there common pathway elements that arise in the metastatic microenvironment. For individualized combinatorial therapy, ideal therapeutic selection based on proteomic mapping of phosphorylation end points may require evaluation of the patient's metastatic tissue. Extending these findings to the bedside will require the development of optimized protocols and reference standards. We have developed a reference standard based on a mixture of phosphorylated peptides to begin to address this challenge.

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This paper examines a buffer scheme to mitigate the negative impacts of power-conditioned loads on network voltage and transient stabilities. The scheme is based on the use of battery energy-storage systems in the buffers. The storage systems ensure that protected loads downstream of the buffers can ride through upstream voltage sags and swells. Also, by controlling the buffers to operate in either constant impedance or constant power modes, power is absorbed or injected by the storage systems. The scheme thereby regulates the rotor-angle deviations of generators and enhances network transient stability. A computational method is described in which the capacity of the storage systems is determined to achieve simultaneously the above dual objectives of load ride-through and stability enhancement. The efficacy of the resulting scheme is demonstrated through numerical examples.

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Toxicity is a major concern for anti-neoplastic drugs, with much of the existing pharmacopoeia being characterized by a very narrow therapeutic index. 'Network-targeted' combination therapy is a promising new concept in cancer therapy, whereby therapeutic index might be improved by targeting multiple nodes in a cell's signaling network, rather than a single node. Here, we examine the potential of this novel approach, illustrating how therapeutic benefit could be achieved with smaller doses of the necessary agents.

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Social Networks (SN) users have various privacy requirements to protect their information; to address this issue, a six-stage thematic analysis of scholarly articles related to SN user privacy concerns were synthesized. Then this research combines mixed methods research employing the strengths of quantitative and qualitative research to investigate general SN users, and thus construct a new set of ?ve primary and Twenty-?ve secondary SN user privacy requirements. Such an approach has been rarely used to examine the privacy requirements. Factor analysis results show superior agreement with theoretical predictions and signi?cant improvement over previous alternative models of SN user privacy requirements. This research presented here has the potential to provide for the development of more sophisticated privacy controls which will increase the ability of SN users to: specify their rights in SNs and to determine the protection of their own SN data.

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A novel gray-box neural network model (GBNNM), including multi-layer perception (MLP) neural network (NN) and integrators, is proposed for a model identification and fault estimation (MIFE) scheme. With the GBNNM, both the nonlinearity and dynamics of a class of nonlinear dynamic systems can be approximated. Unlike previous NN-based model identification methods, the GBNNM directly inherits system dynamics and separately models system nonlinearities. This model corresponds well with the object system and is easy to build. The GBNNM is embedded online as a normal model reference to obtain the quantitative residual between the object system output and the GBNNM output. This residual can accurately indicate the fault offset value, so it is suitable for differing fault severities. To further estimate the fault parameters (FPs), an improved extended state observer (ESO) using the same NNs (IESONN) from the GBNNM is proposed to avoid requiring the knowledge of ESO nonlinearity. Then, the proposed MIFE scheme is applied for reaction wheels (RW) in a satellite attitude control system (SACS). The scheme using the GBNNM is compared with other NNs in the same fault scenario, and several partial loss of effect (LOE) faults with different severities are considered to validate the effectiveness of the FP estimation and its superiority.

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This thesis introduces advanced Demand Response algorithms for residential appliances to provide benefits for both utility and customers. The algorithms are engaged in scheduling appliances appropriately in a critical peak day to alleviate network peak, adverse voltage conditions and wholesale price spikes also reducing the cost of residential energy consumption. Initially, a demand response technique via customer reward is proposed, where the utility controls appliances to achieve network improvement. Then, an improved real-time pricing scheme is introduced and customers are supported by energy management schedulers to actively participate in it. Finally, the demand response algorithm is improved to provide frequency regulation services.

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This paper introduces a new method to automate the detection of marine species in aerial imagery using a Machine Learning approach. Our proposed system has at its core, a convolutional neural network. We compare this trainable classifier to a handcrafted classifier based on color features, entropy and shape analysis. Experiments demonstrate that the convolutional neural network outperforms the handcrafted solution. We also introduce a negative training example-selection method for situations where the original training set consists of a collection of labeled images in which the objects of interest (positive examples) have been marked by a bounding box. We show that picking random rectangles from the background is not necessarily the best way to generate useful negative examples with respect to learning.