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Automated image analysis techniques to characterise pulverised coal particles and predict combustion char morphology
Get PDFA new automated image analysis system that analyses individual coal particles to predict daughter char morphology is presented. 12 different coals were milled to 75–106 µm, segmented from large mosaic images and the proportions of the different petrographic features were obtained from reflectance histograms via an automated Matlab system. Each sample was then analysed on a particle by particle basis, and daughter char morphologies were automatically predicted using a decision tree-based system built into the program. Predicted morphologies were then compared to ‘real’ char intermediates generated at 1300 °C in a drop-tube furnace (DTF). For the majority of the samples, automated coal particle characterisation and char morphology prediction differed from manually obtained results by a maximum of 9%. This automated system is a step towards eliminating the inherent variability and repeatability issues of manually operated systems in both coal and char analysis. By analysing large numbers of coal particles, the char morphology prediction could potentially be used as a more accurate and reliable method of predicting fuel performance for power generators
The Belt and Road Initiative for an intercontinental ecosystem: Strategic implications for multinational enterprises around the world
Get PDFBuilding a resilient and sustainable food system in a changing world – A case for climate-smart and nutrient dense crops
Get PDFCurrent food production and consumption practices have had negative impacts on the environment and are central to global health concerns. Using a mixed-methods review, we examined the nutritional and environmental impacts of our global food systems and addressed the apparent decrease in food sources and crop diversity, and its implication on sustainable and healthy diets. Moreover, we explored the merits of weighing the use of natural capital and agricultural inputs against the output generated in terms of nutrient density. Transforming our food systems to safeguard planetary health will require a shift towards sufficient production of nutrient dense crops that are environmentally sustainable. Such a transformation largely depends on valuing crops for their natural nutrient density and matching them to suitable environments
Quality estimation and optimization of adaptive stereo matching algorithms for smart vehicles
Get PDFStereo matching is a promising approach for smart vehicles to find the depth of nearby objects. Transforming a traditional stereo matching algorithm to its adaptive version has potential advantages to achieve the maximum quality (depth accuracy) in a best-effort manner. However, it is very challenging to support this adaptive feature, since (1) the internal mechanism of adaptive stereo matching (ASM) has to be accurately modeled, and (2) scheduling ASM tasks on multiprocessors to generate the maximum quality is difficult under strict real-time constraints of smart vehicles. In this article, we propose a framework for constructing an ASM application and optimizing its output quality on smart vehicles. First, we empirically convert stereo matching into ASM by exploiting its inherent characteristics of disparity–cycle correspondence and introduce an exponential quality model that accurately represents the quality–cycle relationship. Second, with the explicit quality model, we propose an efficient quadratic programming-based dynamic voltage/frequency scaling (DVFS) algorithm to decide the optimal operating strategy, which maximizes the output quality under timing, energy, and temperature constraints. Third, we propose two novel methods to efficiently estimate the parameters of the quality model, namely location similarity-based feature point thresholding and street scenario-confined CNN prediction. Results show that our DVFS algorithm achieves at least 1.61 times quality improvement compared to the state-of-the-art techniques, and average parameter estimation for the quality model achieves 96.35% accuracy on the straight road
A multi-dimensional energy-based analysis of neighbourhood sustainability assessment tools: are institutional indicators really missing?
Get PDFNeighbourhood Sustainability Assessment Tools (NSATs) have become the modern day template for urban planners to achieve sustainable development in their communities, districts and cities. The popularity of the pioneering NSATs led to the creation of other tools in different regions. Also, with the popularity and replication of these tools came the replication of their limitations. The most notable limitation and motivation for this study is the inadequate recognition of the complexities of institutional dimensions (i.e. policies, laws and regulation) that contribute to mainstreaming and operationalising sustainable neighbourhood development. Studies that have investigated NSATs generally argue lack of coverage of the institutional dimension. However, there has been little consistent and explicit mention of the precise indicators and criteria sought out to make this claim. Also, there is a clear confusion as to what institutional indicators actually are, what characteristics they possess and how best they can be identified. This study, via the lens of energy-based indicators, expands on the role of the institutional indicator and its associated dimensions. This study also utilises a multi-dimensional approach to indicator analysis and draws out current trend or characteristics of institutional indicators in 15 currently existing NSATs. The results show a limited view on the classification of institutional indicators. The study also demonstrates that there are more institutional indicators than previously reported in prior studies. Additionally, this study confirms that an institutional indicator cannot be a single entity or identity but rather it must operate under the linkage of the other dimensions (environment, social and economic). Finally, this study, based on the analysis of 15 NSATs provides a definition of what can be considered an institutional indicator. In conclusion, it is recommended that future development of NSATs should ensure a constant institutional link to indicators, as this could provide an enhanced alternative to the development of NSATs, particularly for regions that are looking into developing their own assessment tools
Electrochemical production of sustainable hydrocarbon fuels from CO2 co-electrolysis in eutectic molten melts
Get PDFBecause of the heavy reliance of people on limited fossil fuels as energy resources, global warming has increased to severe levels because of huge CO2 emission into the atmosphere. To mitigate this situation, a green method is presented here for the conversion of CO2/H2O into sustainable hydrocarbon fuels via electrolysis in eutectic molten salts [(KCl-LiCl; 41:59 mol %), (LiOH-NaOH; 27:73 mol %), (KOH-NaOH; 50:50 mol %), and (Li2CO3-Na2CO3-K2CO3; 43.5:31.5:25 mol %)] under the conditions of 1.5-2 V and 225-475 °C depending on the molten electrolyte used. Gas chromatography (GC) and GC-mass spectrometry (MS) techniques were employed to analyze the content of gaseous products. The electrolysis results in hydrocarbon production with maximum 59.30, 87.70, and 99% Faraday efficiencies in the case of molten chloride, molten hydroxide, and molten carbonate electrolytes under the temperatures of 375, 275, and 425 °C, respectively. GC with a flame-ionization detector and a thermal conductivity detector and GC-MS analysis confirmed that H2 and CH4 were the main products in the case of molten chlorides and hydroxides at an applied voltage of 2 V, while longer-chain hydrocarbons (>C1) were obtained only in molten carbonates at 1.5 V. In this way, electricity is transformed into chemical energy. The heating values obtained from the produced hydrocarbon fuels are satisfactory for further application. The practice of using molten salts could be a promising and encouraging technology for further fundamental investigation of sustainable hydrocarbon fuel formation with more product concentrations because of their fast electrolytic conversion rate without the use of a catalyst
Power routing: a new paradigm for maintenance scheduling
Get PDFCurrently, the necessity of efficient and reliable power systems is also increasing because of the strict requirements that standards and regulations impose, but still costs have to remain low. The monitoring and control of the components' lifetime can lead to reduce maintenance costs. However, overcoming the related challenges is not a straightforward task, as it involves knowledge of power device physics, smart management of electrical quantities, and optimal maintenance planning and scheduling. It represents a multidisciplinary issue being faced in the last decade
Face2Multi-modal: in-vehicle multi-modal predictors via facial expressions
Get PDFTowards intelligent Human-Vehicle Interaction systems and innovative Human-Vehicle Interaction designs, in-vehicle drivers' physiological data has been explored as an essential data source. However, equipping multiple biosensors is considered the limited extent of user-friendliness and impractical during the driving procedure. The lack of a proper approach to access physiological data has hindered wider applications of advanced biosignal-driven designs in practice (e.g. monitoring systems and etc.). Hence, the demand for a user-friendly approach to measuring drivers' body statuses has become more intense. In this Work-In-Progress, we present Face2Multi-modal, an In-vehicle multi-modal Data Streams Predictors through facial expressions only. More specifically, we have explored the estimations of Heart Rate, Skin Conductance, and Vehicle Speed of the drivers. We believe Face2Multi-modal provides a user-friendly alternative to acquiring drivers' physiological status and vehicle status, which could serve as the building block for many current or future personalized Human-Vehicle Interaction designs. More details and updates about the project Face2Multi-modal is online at https://github.com/unnc-ucc/Face2Multimodal/
Estimation of oil spray cooling heat transfer coefficients on hairpin windings with reduced-parameter models
Get PDFHairpin windings and spray cooling are becoming an increasingly popular combination in the field of high-performance electrical machine design. Machines adopting hairpin windings can achieve higher torque and power densities while enabling them to be manufactured automatically on a large scale to meet the rapid market growth of electric transport. Spray cooling is an effective way for high heat flux removal, which has shown great potentials in electrical machine applications. Although spray cooling has been studied for decades in different engineering applications, the focus had been on investigating its performance on regular surfaces using low-viscosity liquids, such as water. Additionally, many existing models for spray cooling heat transfer were built on spray parameters that are difficult to obtain without specialist equipment. Thus, most results from previous studies are difficult to be interpreted and directly applied to electrical machine applications. Practical and economical approaches for estimating the heat transfer coefficients of spray cooling on hairpin windings are needed. This paper proposes and validates an experimental approach based on reduced-parameter models which can be applied to predict the heat transfer coefficient of spray cooling setups on hairpin windings
Reflection on early lessons for urban resilience and public health enhancement during the COVID-19
Get PDFWith growing impacts on public health systems and economies across the world, as a result of the COVID-19 pandemic outbreak, we need to reflect on some of the early lessons for urban resilience enhancement. In this paper, a brief discussion is made through several recommendations that could make our cities more prepared specially in the probable future waves of this current outbreak or potential spikes in infections or clustered cases. The experiences from global examples highlighted in this study address what has worked in the past few months at the spatial levels of communities and cities. The COVID-19 outbreak highlighted the deficiencies and shortfall across multiple sectors of the urban systems and enabled us to identify risks, challenges, and pathways to better city management. With regard to urban resilience enhancement, the negative impacts of the COVID-19 outbreak are assessed to suggest a checklist of what could be done through early preparedness. The findings are novel in ongoing research related to urban resilience and public health during the COVID-19 pandemic. The early lessons here reflect on the ongoing situation of this pandemic outbreak, but could effectually help to enhance the resilience of our cities and communities, and especially addressing the protection of public health and societal well-being. The findings contribute to major sectors of urban resilience, city management, and public health. The recommendations from this study could be utilised and adapted in any context, allowing for the consideration of all-inclusive decision-making and much-enhanced planning processes
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