Semi-parametric forecasting of spikes in electricity prices

The occurrence of extreme movements in the spot price of electricity represents a significant source of risk to retailers. A range of approaches have been considered with respect to modelling electricity prices; these models, however, have relied on time-series approaches, which typically use restrictive decay schemes placing greater weight on more recent observations. This study develops an alternative, semi-parametric method for forecasting, which uses state-dependent weights derived from a kernel function. The forecasts that are obtained using this method are accurate and therefore potentially useful to electricity retailers in terms of risk management.

Transparency tools in gene patenting for informing policy and practice

In the recent decision Association for Molecular Pathology v. Myriad Genetics1, the US Supreme Court held that naturally occurring sequences from human genomic DNA are not patentable subject matter. Only certain complementary DNAs (cDNA), modified sequences and methods to use sequences are potentially patentable. It is likely that this distinction will hold for all DNA sequences, whether animal, plant or microbial2. However, it is not clear whether this means that other naturally occurring informational molecules, such as polypeptides (proteins) or polysaccharides, will also be excluded from patents. The decision underscores a pressing need for precise analysis of patents that disclose and reference genetic sequences, especially in the claims. Similarly, data sets, standards compliance and analytical tools must be improved—in particular, data sets and analytical tools must be made openly accessible—in order to provide a basis for effective decision making and policy setting to support biological innovation. Here, we present a web-based platform that allows such data aggregation, analysis and visualization in an open, shareable facility. To demonstrate the potential for the extension of this platform to global patent jurisdictions, we discuss the results of a global survey of patent offices that shows that much progress is still needed in making these data freely available for aggregation in the first place.

Semi-binary based video features for activity representation

Efficient and effective feature detection and representation is an important consideration when processing videos, and a large number of applications such as motion analysis, 3D scene understanding, tracking etc. depend on this. Amongst several feature description methods, local features are becoming increasingly popular for representing videos because of their simplicity and efficiency. While they achieve state-of-the-art performance with low computational complexity, their performance is still too limited for real world applications. Furthermore, rapid increases in the uptake of mobile devices has increased the demand for algorithms that can run with reduced memory and computational requirements. In this paper we propose a semi binary based feature detectordescriptor based on the BRISK detector, which can detect and represent videos with significantly reduced computational requirements, while achieving comparable performance to the state of the art spatio-temporal feature descriptors. First, the BRISK feature detector is applied on a frame by frame basis to detect interest points, then the detected key points are compared against consecutive frames for significant motion. Key points with significant motion are encoded with the BRISK descriptor in the spatial domain and Motion Boundary Histogram in the temporal domain. This descriptor is not only lightweight but also has lower memory requirements because of the binary nature of the BRISK descriptor, allowing the possibility of applications using hand held devices.We evaluate the combination of detectordescriptor performance in the context of action classification with a standard, popular bag-of-features with SVM framework. Experiments are carried out on two popular datasets with varying complexity and we demonstrate comparable performance with other descriptors with reduced computational complexity.

A comparative study of the semi-elastic and fully-elastic MapReduce models

MapReduce is a computation model for processing large data sets in parallel on large clusters of machines, in a reliable, fault-tolerant manner. A MapReduce computation is broken down into a number of map tasks and reduce tasks, which are performed by so called mappers and reducers, respectively. The placement of the mappers and reducers on the machines directly affects the performance and cost of the MapReduce computation in cloud computing. From the computational point of view, the mappers/reducers placement problem is a generation of the classical bin packing problem, which is NP-complete. Thus, in this paper we propose a new heuristic algorithm for the mappers/reducers placement problem in cloud computing and evaluate it by comparing with other several heuristics on solution quality and computation time by solving a set of test problems with various characteristics. The computational results show that our heuristic algorithm is much more efficient than the other heuristics and it can obtain a better solution in a reasonable time. Furthermore, we verify the effectiveness of our heuristic algorithm by comparing the mapper/reducer placement for a benchmark problem generated by our heuristic algorithm with a conventional mapper/reducer placement which puts a fixed number of mapper/reducer on each machine. The comparison results show that the computation using our mapper/reducer placement is much cheaper than the computation using the conventional placement while still satisfying the computation deadline.

Discussion of : constructing summary statistics for approximate Bayesian computation: semi-automatic approximate Bayesian computation

This is a discussion of the journal article: "Construcing summary statistics for approximate Bayesian computation: semi-automatic approximate Bayesian computation". The article and discussion have appeared in the Journal of the Royal Statistical Society: Series B (Statistical Methodology).

Visible and near infrared spectroscopy of hayabusa reentry using semi-autonomous tracking

Aground-based tracking camera and coaligned slitless spectrograph were used to measure the spectral signature of visible radiation emitted from the Hayabusa capsule as it entered into the Earth’s atmosphere in June 2010. Good quality spectra were obtained, which showed the presence of radiation from the heat shield of the vehicle and the shock-heated air in front of the vehicle. An analysis of the blackbody nature of the radiation concluded that the peak average temperature of the surface was about (3100± 100)K. Line spectra from oxygen and nitrogen atoms were used to infer a peak average shock-heated gas temperature of around((7000±400))K.

Exploring the scope of gene patents through new levels of transparency

As the global intellectual property (IP) system grows and now impacts virtually all citizens, it is crucial that the means to understand these rights and their teachings, as well as their implications and scope become global public goods. To do so requires not only that the primary data is available freely and openly in a standardized and re-useable form, but that tools to visualize, analyse and model that data are similarly open and free public goods, adaptable to diverse needs and uses; this we call ‘transparency’.

Demonstration of nonlinear absorption in Au semi-continuous film by electrical measurement

We have demonstrated the nonlinear absorption at 532 nm wavelength in an Au semi-continuous film (SF) resulting from smearing of the Fermi distribution and diffusion of conduction electrons into the substrate. The Au SF was irradiated by a pulsed laser with 8 ns pulse width at 532 nm in near resonance with the interband transition of the Au. We determined the temperature increase in the SF for different intensities by electrical measurement. We calculated the temperature increase by using a 1D heat transport equation; comparing the results of the calculation with measured values for the temperature increase, revealed the nonlinear absorption in the Au SF. We employed this deviation from linear behaviour to determine the nonlinear absorption coefficient.

Plasma-aided hydrogenation and Al-doping : increasing the conductivity and optical transparency of ZnO transparent conducting oxide

Plasma-assisted magnetron sputtering with varying ambient conditions has been utilised to deposit Al-doped ZnO (AZO) transparent conductive thin films directly onto a glass substrate at a low substrate temperature of 400 °C. The effects of hydrogen addition on electrical, optical and structural properties of the deposited AZO films have been investigated using X-ray diffractometry (XRD), scanning electron microscopy (SEM), Hall effect measurements and UV–vis optical transmission spectroscopy. The results indicate that hydrogen addition has a remarkable effect on the film transparency and conductivity with the greatest effects observed with a hydrogen flux of approximately 3 sccm. It has been demonstrated that the conductivity and the average transmittance in the visible range can increase simultaneously contrary to the effects observed by other authors. In addition, hydrogen incorporation further leads to the absorption edge shifting to a shorter wavelength due to the Burstein–Moss effect. These results are of particular relevance to the development of the next generation of optoelectronic and photovoltaic devices based on highly transparent conducting oxides with controllable electronic and optical properties.

Reversible and semi-blind relational database watermarking

Database watermarking has received significant research attention in the current decade. Although, almost all watermarking models have been either irreversible (the original relation cannot be restored from the watermarked relation) and/or non-blind (requiring original relation to detect the watermark in watermarked relation). This model has several disadvantages over reversible and blind watermarking (requiring only watermarked relation and secret key from which the watermark is detected and original relation is restored) including inability to identify rightful owner in case of successful secondary watermarking, inability to revert the relation to original data set (required in high precision industries) and requirement to store unmarked relation at a secure secondary storage. To overcome these problems, we propose a watermarking scheme that is reversible as well as blind. We utilize difference expansion on integers to achieve reversibility. The major advantages provided by our scheme are reversibility to high quality original data set, rightful owner identification, resistance against secondary watermarking attacks, and no need to store original database at a secure secondary storage.

Improving the transparency, openness and efficiency of e-government in Qatar in the era of Open Government Data, and beyond

This thesis investigates how Open Government Data (OGD) concepts and practices might be implemented in the State of Qatar to achieve more transparent, effective and accountable government. The thesis concludes with recommendations as to how Qatar, as a developing country, might enhance the accessibility and usability of its OGD and implement successful and sustainable OGD systems and practices.

Achieving transparency in carbon labelling for construction materials – Lessons from current assessment standards and carbon labels

The construction industry is one of the largest sources of carbon emissions. Manufacturing of raw materials, such as cement, steel and aluminium, is energy intensive and has considerable impact on carbon emissions level. Due to the rising recognition of global climate change, the industry is under pressure to reduce carbon emissions. Carbon labelling schemes are therefore developed as meaningful yardsticks to measure and compare carbon emissions. Carbon labelling schemes can help switch consumer-purchasing habits to low-carbon alternatives. However, such switch is dependent on a transparent scheme. The principle of transparency is highlighted in all international greenhouse gas (GHG) standards, including the newly published ISO 14067: Carbon footprint of products – requirements and guidelines for quantification and communication. However, there are few studies which systematically investigate the transparency requirements in carbon labelling schemes. A comparison of five established carbon labelling schemes, namely the Singapore Green Labelling Scheme, the CarbonFree (the U.S.), the CO2 Measured Label and the Reducing CO2 Label (UK), the CarbonCounted (Canada), and the Hong Kong Carbon Labelling Scheme is therefore conducted to identify and investigate the transparency requirements. The results suggest that the design of current carbon labels have transparency issues relating but not limited to the use of a single sign to represent the comprehensiveness of the carbon footprint. These transparency issues are partially caused by the flexibility given to select system boundary in the life cycle assessment (LCA) methodology to measure GHG emissions. The primary contribution of this study to the construction industry is to reveal the transparency requirements from international GHG standards and carbon labels for construction products. The findings also offer five key strategies as practical implications for the global community to improve the performance of current carbon labelling schemes on transparency.

Biolistic transformation of elite indica rice (Oryza sativa L.) cultivars through semi-solid and liquid medium selection systems

Following microprojectile mediated delivery of a plasmid construct (pAHC-25) encoding bar (bialophos resistance) gene into five-day-old scutellar calli derived from mature embryos, the effectiveness of selection procedure for bar-gene expressing tissue was compared for two indica rice cultivars (IR-64 and Karnal Local). While IR-64 transformants could be selected through the generally used semi-solid selection medium, the same procedure was not effective in the basmati cultivar Karnal Local. In the latter case, while lower concentrations (2–4 mg 1−1) of the selective agent phosphinothricin (PPT) yielded only escapes, higher concentrations (6–8 mg l−1) inhibited proliferation of transformed as well as untransformed sectors. For Karnal Local, a liquid medium based selection system was successfully utilized for recovering transformed sectors and, eventually, regenerants. The study demonstrates the generation of transformants of two elite indica cultivars using the environment-independent system of mature embryos from seeds.

Adaptive estimation of hidden semi-Markov chains with parameterised transition probabilities and exponential decaying states

This paper develops maximum likelihood (ML) estimation schemes for finite-state semi-Markov chains in white Gaussian noise. We assume that the semi-Markov chain is characterised by transition probabilities of known parametric from with unknown parameters. We reformulate this hidden semi-Markov model (HSM) problem in the scalar case as a two-vector homogeneous hidden Markov model (HMM) problem in which the state consist of the signal augmented by the time to last transition. With this reformulation we apply the expectation Maximumisation (EM ) algorithm to obtain ML estimates of the transition probabilities parameters, Markov state levels and noise variance. To demonstrate our proposed schemes, motivated by neuro-biological applications, we use a damped sinusoidal parameterised function for the transition probabilities.

Understanding Uncertainties in Non-Linear Population Trajectories: A Bayesian Semi-Parametric Hierarchical Approach to Large-Scale Surveys of Coral Cover

Recently, attempts to improve decision making in species management have focussed on uncertainties associated with modelling temporal fluctuations in populations. Reducing model uncertainty is challenging; while larger samples improve estimation of species trajectories and reduce statistical errors, they typically amplify variability in observed trajectories. In particular, traditional modelling approaches aimed at estimating population trajectories usually do not account well for nonlinearities and uncertainties associated with multi-scale observations characteristic of large spatio-temporal surveys. We present a Bayesian semi-parametric hierarchical model for simultaneously quantifying uncertainties associated with model structure and parameters, and scale-specific variability over time. We estimate uncertainty across a four-tiered spatial hierarchy of coral cover from the Great Barrier Reef. Coral variability is well described; however, our results show that, in the absence of additional model specifications, conclusions regarding coral trajectories become highly uncertain when considering multiple reefs, suggesting that management should focus more at the scale of individual reefs. The approach presented facilitates the description and estimation of population trajectories and associated uncertainties when variability cannot be attributed to specific causes and origins. We argue that our model can unlock value contained in large-scale datasets, provide guidance for understanding sources of uncertainty, and support better informed decision making