Natural images dominate in binocular rivalry

Ecological approaches to perception have demonstrated that information encoding by the visual system is informed by the natural environment, both in terms of simple image attributes like luminance and contrast, and more complex relationships corresponding to Gestalt principles of perceptual organization. Here, we ask if this optimization biases perception of visual inputs that are perceptually bistable. Using the binocular rivalry paradigm, we designed stimuli that varied in either their spatiotemporal amplitude spectra or their phase spectra. We found that noise stimuli with “natural” amplitude spectra (i.e., amplitude content proportional to 1/f, where f is spatial or temporal frequency) dominate over those with any other systematic spectral slope, along both spatial and temporal dimensions. This could not be explained by perceived contrast measurements, and occurred even though all stimuli had equal energy. Calculating the effective contrast following attenuation by a model contrast sensitivity function suggested that the strong contrast dependency of rivalry provides the mechanism by which binocular vision is optimized for viewing natural images. We also compared rivalry between natural and phase-scrambled images and found a strong preference for natural phase spectra that could not be accounted for by observer biases in a control task. We propose that this phase specificity relates to contour information, and arises either from the activity of V1 complex cells, or from later visual areas, consistent with recent neuroimaging and single-cell work. Our findings demonstrate that human vision integrates information across space, time, and phase to select the input most likely to hold behavioral relevance.

Sustainable energy from paper industry wastes

Secondary fibre paper mills are significant users of both heat and electricity which is mainly derived from the combustion of fossil fuels. The cost of producing this energy is increasing year upon year. These mills are also significant producers of fibrous sludge and reject waste material which can contain high amounts of useful energy. Currently the majority of these waste fractions are disposed of by landfill, land-spread or incineration using natural gas. These disposal methods not only present environmental problems but are also very costly. The focus of this work was to utilise the waste fractions produced at secondary fibre paper mills for the on-site production of combined heat and power (CHP) using advanced thermal conversion methods (gasification and pyrolysis), well suited to relatively small scales of throughput. The heat and power can either be used on-site or exported. The first stage of the work was the development of methods to condition selected paper industry wastes to enable thermal conversion. This stage required detailed characterisation of the waste streams in terms of proximate and ultimate analysis and heat content. Suitable methods to dry and condition the wastes in preparation for thermal conversion were also explored. Through trials at pilot scale with both fixed bed downdraft gasification and intermediate pyrolysis systems, the energy recovered from selected wastes and waste blends in the form of product gas and pyrolysis products was quantified. The optimal process routes were selected based on the experimental results, and implementation studies were carried out at the selected candidate mills. The studies consider the pre-processing of the wastes, thermal conversion, and full integration of the energy products. The final stage of work was an economic analysis to quantify economic gain, return on investment and environmental benefits from the proposed processes.

Uses of frontier efficiency methodologies and multi-criteria decision making for performance measurement in the energy sector

Guest editorial Ali Emrouznejad is a Senior Lecturer at the Aston Business School in Birmingham, UK. His areas of research interest include performance measurement and management, efficiency and productivity analysis as well as data mining. He has published widely in various international journals. He is an Associate Editor of IMA Journal of Management Mathematics and Guest Editor to several special issues of journals including Journal of Operational Research Society, Annals of Operations Research, Journal of Medical Systems, and International Journal of Energy Management Sector. He is in the editorial board of several international journals and co-founder of Performance Improvement Management Software. William Ho is a Senior Lecturer at the Aston University Business School. Before joining Aston in 2005, he had worked as a Research Associate in the Department of Industrial and Systems Engineering at the Hong Kong Polytechnic University. His research interests include supply chain management, production and operations management, and operations research. He has published extensively in various international journals like Computers & Operations Research, Engineering Applications of Artificial Intelligence, European Journal of Operational Research, Expert Systems with Applications, International Journal of Production Economics, International Journal of Production Research, Supply Chain Management: An International Journal, and so on. His first authored book was published in 2006. He is an Editorial Board member of the International Journal of Advanced Manufacturing Technology and an Associate Editor of the OR Insight Journal. Currently, he is a Scholar of the Advanced Institute of Management Research. Uses of frontier efficiency methodologies and multi-criteria decision making for performance measurement in the energy sector This special issue aims to focus on holistic, applied research on performance measurement in energy sector management and for publication of relevant applied research to bridge the gap between industry and academia. After a rigorous refereeing process, seven papers were included in this special issue. The volume opens with five data envelopment analysis (DEA)-based papers. Wu et al. apply the DEA-based Malmquist index to evaluate the changes in relative efficiency and the total factor productivity of coal-fired electricity generation of 30 Chinese administrative regions from 1999 to 2007. Factors considered in the model include fuel consumption, labor, capital, sulphur dioxide emissions, and electricity generated. The authors reveal that the east provinces were relatively and technically more efficient, whereas the west provinces had the highest growth rate in the period studied. Ioannis E. Tsolas applies the DEA approach to assess the performance of Greek fossil fuel-fired power stations taking undesirable outputs into consideration, such as carbon dioxide and sulphur dioxide emissions. In addition, the bootstrapping approach is deployed to address the uncertainty surrounding DEA point estimates, and provide bias-corrected estimations and confidence intervals for the point estimates. The author revealed from the sample that the non-lignite-fired stations are on an average more efficient than the lignite-fired stations. Maethee Mekaroonreung and Andrew L. Johnson compare the relative performance of three DEA-based measures, which estimate production frontiers and evaluate the relative efficiency of 113 US petroleum refineries while considering undesirable outputs. Three inputs (capital, energy consumption, and crude oil consumption), two desirable outputs (gasoline and distillate generation), and an undesirable output (toxic release) are considered in the DEA models. The authors discover that refineries in the Rocky Mountain region performed the best, and about 60 percent of oil refineries in the sample could improve their efficiencies further. H. Omrani, A. Azadeh, S. F. Ghaderi, and S. Abdollahzadeh presented an integrated approach, combining DEA, corrected ordinary least squares (COLS), and principal component analysis (PCA) methods, to calculate the relative efficiency scores of 26 Iranian electricity distribution units from 2003 to 2006. Specifically, both DEA and COLS are used to check three internal consistency conditions, whereas PCA is used to verify and validate the final ranking results of either DEA (consistency) or DEA-COLS (non-consistency). Three inputs (network length, transformer capacity, and number of employees) and two outputs (number of customers and total electricity sales) are considered in the model. Virendra Ajodhia applied three DEA-based models to evaluate the relative performance of 20 electricity distribution firms from the UK and the Netherlands. The first model is a traditional DEA model for analyzing cost-only efficiency. The second model includes (inverse) quality by modelling total customer minutes lost as an input data. The third model is based on the idea of using total social costs, including the firm’s private costs and the interruption costs incurred by consumers, as an input. Both energy-delivered and number of consumers are treated as the outputs in the models. After five DEA papers, Stelios Grafakos, Alexandros Flamos, Vlasis Oikonomou, and D. Zevgolis presented a multiple criteria analysis weighting approach to evaluate the energy and climate policy. The proposed approach is akin to the analytic hierarchy process, which consists of pairwise comparisons, consistency verification, and criteria prioritization. In the approach, stakeholders and experts in the energy policy field are incorporated in the evaluation process by providing an interactive mean with verbal, numerical, and visual representation of their preferences. A total of 14 evaluation criteria were considered and classified into four objectives, such as climate change mitigation, energy effectiveness, socioeconomic, and competitiveness and technology. Finally, Borge Hess applied the stochastic frontier analysis approach to analyze the impact of various business strategies, including acquisition, holding structures, and joint ventures, on a firm’s efficiency within a sample of 47 natural gas transmission pipelines in the USA from 1996 to 2005. The author finds that there were no significant changes in the firm’s efficiency by an acquisition, and there is a weak evidence for efficiency improvements caused by the new shareholder. Besides, the author discovers that parent companies appear not to influence a subsidiary’s efficiency positively. In addition, the analysis shows a negative impact of a joint venture on technical efficiency of the pipeline company. To conclude, we are grateful to all the authors for their contribution, and all the reviewers for their constructive comments, which made this special issue possible. We hope that this issue would contribute significantly to performance improvement of the energy sector.

Novel biomass conversion routes:ammonia from biomass, and marine macroalgae for energy

This thesis presents a techno-economic investigation of the generation of electricity from marine macroalgae (seaweed) in the UK (Part 1), and the production of anhydrous ammonia from synthesis gas (syngas) generated from biomass gasification (Part 2). In Part 1, the study covers the costs from macroalgae production to the generation of electricity via a CHP system. Seven scenarios, which varied the scale and production technique, were investigated to determine the most suitable scale of operation for the UK. Anaerobic digestion was established as the most suitable technology for macroalgae conversion to CHP, based on a number of criteria. All performance and cost data have been taken from published literature. None of the scenarios assessed would be economically viable under present conditions, although the use of large-scale electricity generation has more potential than small-scale localised production. Part 2 covers the costs from the delivery of the wood chip feedstock to the production of ammonia. Four cases, which varied the gasification process used and the scale of production, were investigated to determine the most suitable scale of operation for the UK. Two gasification processes were considered, these were O2-enriched air entrained flow gasification and Fast Internal Circulating Fluidised Bed. All performance and cost data have been taken from published literature, unless otherwise stated. Large-scale (1,200 tpd) ammonia production using O2-enriched air entrained flow gasification was determined as the most suitable system, producing the lowest ammonia-selling price, which was competitive to fossil fuels. Large-scale (1,200 tpd) combined natural gas/biomass syngas ammonia production also generated ammonia at a price competitive to fossil fuels.

A non-parametric data envelopment analysis approach for improving energy efficiency of grape production

Grape is one of the world's largest fruit crops with approximately 67.5 million tonnes produced each year and energy is an important element in modern grape productions as it heavily depends on fossil and other energy resources. Efficient use of these energies is a necessary step toward reducing environmental hazards, preventing destruction of natural resources and ensuring agricultural sustainability. Hence, identifying excessive use of energy as well as reducing energy resources is the main focus of this paper to optimize energy consumption in grape production.In this study we use a two-stage methodology to find the association of energy efficiency and performance explained by farmers' specific characteristics. In the first stage a non-parametric Data Envelopment Analysis is used to model efficiencies as an explicit function of human labor, machinery, chemicals, FYM (farmyard manure), diesel fuel, electricity and water for irrigation energies. In the second step, farm specific variables such as farmers' age, gender, level of education and agricultural experience are used in a Tobit regression framework to explain how these factors influence efficiency of grape farming.The result of the first stage shows substantial inefficiency between the grape producers in the studied area while the second stage shows that the main difference between efficient and inefficient farmers was in the use of chemicals, diesel fuel and water for irrigation. The use of chemicals such as insecticides, herbicides and fungicides were considerably less than inefficient ones. The results revealed that the more educated farmers are more energy efficient in comparison with their less educated counterparts. © 2013.

Fe-doped and -mediated graphitic carbon nitride nanosheets for enhanced photocatalytic performance under natural sunlight

Herein, we demonstrate the synthesis of highly efficient Fe-doped graphitic carbon nitride (g-C3N4) nanosheets via a facile and cost effective method. The synthesized Fe-doped g-C3N4 nanosheets were well characterized by various analytical techniques. The results revealed that the Fe exists mainly in the +3 oxidation state in the Fe-doped g-C3N4 nanosheets. Fe doping of g-C3N4 nanosheets has a great influence on the electronic and optical properties. The diffuse reflectance spectra of Fe-doped g-C3N4 nanosheets exhibit red shift and increased absorption in the visible light range, which is highly beneficial for absorbing the visible light in the solar spectrum. More significantly, the Fe-doped g-C3N4 nanosheets exhibit greatly enhanced photocatalytic activity for the degradation of Rhodamine B under sunlight irradiation. The photocatalytic activity of 2 mol% Fe-doped g-C3N4 nanosheets is almost 7 times higher than that of bulk g-C3N4 and 4.5 times higher than that of pure g-C3N4 nanosheets. A proposed mechanism for the enhanced photocatalytic activity of Fe-doped g-C3N4 nanosheets was investigated by trapping experiments. The synthesized photocatalysts are highly stable even after five successive experimental runs. The enhanced photocatalytic performance of Fe-doped g-C3N4 nanosheets is due to high visible light response, large surface area, high charge separation and charge transfer. Therefore, the Fe-doped g-C3N4 photocatalyst is a promising candidate for energy conversion and environmental remediation.

An investigation into the tension arising between natural language and mathematical language experienced by mechanical engineering students

This investigation is grounded within the concept of embodied cognition where the mind is considered to be part of a biological system. A first year undergraduate Mechanical Engineering cohort of students was tasked with explaining the behaviour of three balls of different masses being rolled down a ramp. The explanations given by the students highlighted the cognitive conflict between the everyday interpretation of the word energy and its mathematical use. The results showed that even after many years of schooling, students found it challenging to interpret the mathematics they had learned and relied upon pseudo-scientific notions to account for the behaviour of the balls.