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The field of Space has become very important in the wake of opportunities and strategic advantages accrued from it. It has become increasingly evident that space science education is beginning to gain grounds in the 21st century and it is perceived to bring about Science, Technology, Engineering, Mathematics (STEM) awareness providing a context for teaching and learning of the subject. The competitive nature of Space means developing nations will need to invest in the skills that the industry needs by way of sustained pathways, incentivised research and development, and assured a favourable prevailing business environment by way of creating capacities and capabilities in space sciences. Through an explanatory study, we draw on students' responses (N = 504, interviews = 20) to investigate the educational need of a space science program. Findings revealed implications for the quality and function of a space science program which provide a platform for engagement in space related research and development. Data also demonstrated key facts with the potential of increasing the future of space science instruction which will necessitate a paradigm shift towards the value of space education in the phase of integrating a space science program in the curriculum. The paper concludes with recommendations for practitioners responsible for integration of new curriculum in science education in higher education sectors. • Positive student perceptions towards a space science program. • Space science education influences learners' decision in STEM studies. • Overwhelming student interest for space science curriculum integration. • Space science provides an interdisciplinary avenue for engagement and participation. • Positive mindset breeds right mentality to teaching and learning of the space science. [ABSTRACT FROM AUTHOR]

Accurate and reliable prediction of photovoltaic (PV) power output is critical to electricity grid stability and power dispatching capabilities. However, PV power generation is highly volatile and unstable due to different reasons. The wavelet transform (WT) has been utilised in time series applications, such as PV power prediction, to model the stochastic volatility and reduce prediction errors. Yet the existing WT approach has a limitation in terms of time complexity. It requires reconstructing the decomposed components and modelling them separately and thus needs more time for reconstruction, model configuration and training. The aim of this study is to improve the efficiency of applying WT by proposing a new method that uses a single simplified model. Given a time series and its WT coefficients, it trains one model with the coefficients as features and the original time series as labels. This eliminates the need for component reconstruction and training numerous models. This work contributes to the day-ahead aggregated solar PV power time series prediction problem by proposing and comprehensively evaluating a new approach of employing WT. The proposed approach is evaluated using 17 months of aggregated solar PV power data from two real-world datasets. The evaluation includes the use of a variety of prediction models, including Linear Regression, Random Forest, Support Vector Regression, and Convolutional Neural Networks. The results indicate that using a coefficients-based strategy can give predictions that are comparable to those obtained using the components-based approach while requiring fewer models and less computational time. [ABSTRACT FROM AUTHOR]

In Victoria (a southern Australian state) in 1995, Narcotics Anonymous had a small but growing membership providing an opportunity to study the early experience of new self-help members. Ninety-one new members were interviewed and 62 (68%) were reinterviewed after 12 months. Three measures of self-help participation were examined: service role involvement, step work, and stable meeting attendance. Lower prior involvement in treatment services and greater participation in self-help predicted subsequent self-help participation. Higher levels of secondary school education predicted service role involvement and longer periods in stable meeting attendance. Higher self-help participation through the 12 months prior to follow-up was associated with lower levels of hazardous alcohol use and higher emotional support at reinterview. Multivariate regression analysis suggested stable self-help meeting attendance and step work continued to predict reductions in hazardous alcohol use and improvements in social support, after controlling for a range of alternative predictors. [Copyright &y& Elsevier]

Discusses ethical and legal liabilities in illicit drug research. Legal and ethical obligations on ethnographic researchers to breach confidential relationships formed in the research environment; Processes that form the research process; Consequences of knowing about illegal activities.

Accurate forecasting of regional solar photovoltaic power (SPVP) generation is essential for efficient energy management and planning. Existing approaches have shown the effectiveness of decomposing the time series to model the stochastic variability in SPVP data. However, these approaches have limitations in extracting and exploiting both spatial and temporal information from complex and high-dimensional data from multiple sources with intricate relationships, which can impact the accuracy of predictions. In this paper, we propose a novel approach called multilevel data fusion and neural basis expansion analysis (MF-NBEA) for forecasting aggregated regional-level SPVP generation. MF-NBEA integrates exogenous data at multiple levels, uses supervised and unsupervised encoders to provide compact data representation, and enhances model learning from complex data by incorporating spatial information. It also includes a sequence analyser module based on a neural network decomposition mechanism to learn the variability in data and incorporates a residuals learner module to improve overall predictions. We evaluate MF-NBEA using two real-world datasets and find that it outperforms state-of-the-art deep learning methods in terms of forecast accuracy. Furthermore, MF-NBEA facilitates information fusion and knowledge extraction to provide interpretable predictions regarding trend, seasonality, and residual components. The insights gained from our approach inform decision-making for energy management and planning, and can lead to more efficient and sustainable resource utilisation. • MF-NBEA for multivariate solar power time series forecasting. • A 3D autoencoder encodes spatial and temporal information from heterogeneous sources. • Interpretable forecasting in terms of trend, seasonality, and residuals. [ABSTRACT FROM AUTHOR]

Abstract: Specimens of 316L stainless steel were irradiated to 0.5–10.3dpa at 30–80°C with a mixture of 500–800MeV protons and spallation neutrons at the Los Alamos Neutron Science Center (LANSCE). Tensile test results of irradiated 316L reported earlier had showed hardening and embrittlement with increasing irradiation dose, with significant irradiation hardening occurring at a dose of as low as 0.5dpa. Transmission electron microscope (TEM) examination of the irradiated microstructure of 316L showed black-spot damage (small loops) and somewhat larger faulted Frank loops to produce the hardening. There was an initial decrease in uniform elongation at low dose levels from 49% (unirradiated) to 30% at 1.1dpa, followed by a second, rather abrupt contribution to ductility loss at higher doses (∼2.5dpa) from 21% at 2.5dpa to 0.5% at 3dpa. This second drop in ductility was not accompanied by any visible new or enhanced microstructural development. In the current study additional transmission electron microscope investigation was conducted on both as-irradiated and irradiated plus subsequently deformed 316L in the vicinity of the second abrupt ductility loss (∼2.5dpa). The steel was observed to deform mainly by twinning and no brittle phases were found in the deformation microstructure. It is proposed that gas accumulation with increasing dpa, especially of hydrogen, may be a contributor to this second abrupt decrease in uniform elongation. Although the retained gas (helium and hydrogen) levels approached ∼0.6at.% total at the highest exposure level, no discernible cavities were observed. [Copyright &y& Elsevier]

Peripheral administration of adenosine A1 receptor selective agonists is generally thought to protect the hippocampus against ischemic damage via central actions. We examined the effects of two peripherally administered A1 agonists, cyclohexyladenosine (CHA) and adenosine amine congener (ADAC), on synaptic transmission in the hippocampus and on indices of cardiovascular function. We conclude that the permeability of these agonists is not sufficient to result in concentrations necessary to activate central adenosine A1 receptors within the hippocampus. [Copyright &y& Elsevier]

Abstract: Web 2.0 authoring forms such as wikis and blogs, social bookmarking, and audio and video podcasting pose a challenge to academic authorship traditions. This paper reviews the provisions made in major academic referencing and citation style guides for acknowledging content and ideas that may be published using these new web authoring forms. It offers an overview of features of web 2.0 authoring forms and explores concepts of authoring that can help academics to understand the challenges of working with these forms. It provides examples of referencing and citation in scholarly and scientific communication, and concludes that the conceptual basis of referencing and citation as expressed in current systems and standards needs reform in order to bring academic integrity to the use of these new forms of authorship. [Copyright &y& Elsevier]