A novel application of deep learning with image cropping: a smart city use case for flood monitoring

© 2020, The Author(s). Event monitoring is an essential application of Smart City platforms. Real-time monitoring of gully and drainage blockage is an important part of flood monitoring applications. Building viable IoT sensors for detecting blockage is a complex task due to the limitations of deploying such sensors in situ. Image classification with deep learning is a potential alternative solution. However, there are no image datasets of gullies and drainages. We were faced with such challenges as part of developing a flood monitoring application in a European Union-funded project. To address these issues, we propose a novel image classification approach based on deep learning with an IoT-enabled camera to monitor gullies and drainages. This approach utilises deep learning to develop an effective image classification model to classify blockage images into different class labels based on the severity. In order to handle the complexity of video-based images, and subsequent poor classification accuracy of the model, we have carried out experiments with the removal of image edges by applying image cropping. The process of cropping in our proposed experimentation is aimed to concentrate only on the regions of interest within images, hence leaving out some proportion of image edges. An image dataset from crowd-sourced publicly accessible images has been curated to train and test the proposed model. For validation, model accuracies were compared considering model with and without image cropping. The cropping-based image classification showed improvement in the classification accuracy. This paper outlines the lessons from our experimentation that have a wider impact on many similar use cases involving IoT-based cameras as part of smart city event monitoring platforms

Switching on electrocatalytic activity in solid oxide cells

Solid oxide cells (SOCs) can operate with high efficiency in two ways - as fuel cells, oxidizing a fuel to produce electricity, and as electrolysis cells, electrolysing water to produce hydrogen and oxygen gases. Ideally, SOCs should perform well, be durable and be inexpensive, but there are often competitive tensions, meaning that, for example, performance is achieved at the expense of durability. SOCs consist of porous electrodes - the fuel and air electrodes - separated by a dense electrolyte. In terms of the electrodes, the greatest challenge is to deliver high, long-lasting electrocatalytic activity while ensuring cost- and time-efficient manufacture. This has typically been achieved through lengthy and intricate ex situ procedures. These often require dedicated precursors and equipment; moreover, although the degradation of such electrodes associated with their reversible operation can be mitigated, they are susceptible to many other forms of degradation. An alternative is to grow appropriate electrode nanoarchitectures under operationally relevant conditions, for example, via redox exsolution. Here we describe the growth of a finely dispersed array of anchored metal nanoparticles on an oxide electrode through electrochemical poling of a SOC at 2 volts for a few seconds. These electrode structures perform well as both fuel cells and electrolysis cells (for example, at 900 °C they deliver 2 watts per square centimetre of power in humidified hydrogen gas, and a current of 2.75 amps per square centimetre at 1.3 volts in 50% water/nitrogen gas). The nanostructures and corresponding electrochemical activity do not degrade in 150 hours of testing. These results not only prove that in operando methods can yield emergent nanomaterials, which in turn deliver exceptional performance, but also offer proof of concept that electrolysis and fuel cells can be unified in a single, high-performance, versatile and easily manufactured device. This opens up the possibility of simple, almost instantaneous production of highly active nanostructures for reinvigorating SOCs during operation

Interpreting random forest models using a feature contribution method

Model interpretation is one of the key aspects of the model evaluation process. The explanation of the relationship between model variables and outputs is easy for statistical models, such as linear regressions, thanks to the availability of model parameters and their statistical significance. For “black box” models, such as random forest, this information is hidden inside the model structure. This work presents an approach for computing feature contributions for random forest classification models. It allows for the determination of the influence of each variable on the model prediction for an individual instance. Interpretation of feature contributions for two UCI benchmark datasets shows the potential of the proposed methodology. The robustness of results is demonstrated through an extensive analysis of feature contributions calculated for a large number of generated random forest models

Exploring the potential for secondary uses of Dementia Care Mapping (DCM) data for improving the quality of dementia care

The reuse of existing datasets to identify mechanisms for improving healthcare quality has been widely encouraged. There has been limited application within dementia care. Dementia Care Mapping is an observational tool in widespread use, predominantly to assess and improve quality of care in single organisations. Dementia Care Mapping data have the potential to be used for secondary purposes to improve quality of care. However, its suitability for such use requires careful evaluation. This study conducted in-depth interviews with 29 Dementia Care Mapping users to identify issues, concerns and challenges regarding the secondary use of Dementia Care Mapping data. Data were analysed using modified Grounded Theory. Major themes identified included the need to collect complimentary contextual data in addition to Dementia Care Mapping data, to reassure users regarding ethical issues associated with storage and reuse of care related data and the need to assess and specify data quality for any data that might be available for secondary analysis

Kinetic Monte Carlo and Cellular Particle Dynamics Simulations of Multicellular Systems

Computer modeling of multicellular systems has been a valuable tool for interpreting and guiding in vitro experiments relevant to embryonic morphogenesis, tumor growth, angiogenesis and, lately, structure formation following the printing of cell aggregates as bioink particles. Computer simulations based on Metropolis Monte Carlo (MMC) algorithms were successful in explaining and predicting the resulting stationary structures (corresponding to the lowest adhesion energy state). Here we present two alternatives to the MMC approach for modeling cellular motion and self-assembly: (1) a kinetic Monte Carlo (KMC), and (2) a cellular particle dynamics (CPD) method. Unlike MMC, both KMC and CPD methods are capable of simulating the dynamics of the cellular system in real time. In the KMC approach a transition rate is associated with possible rearrangements of the cellular system, and the corresponding time evolution is expressed in terms of these rates. In the CPD approach cells are modeled as interacting cellular particles (CPs) and the time evolution of the multicellular system is determined by integrating the equations of motion of all CPs. The KMC and CPD methods are tested and compared by simulating two experimentally well known phenomena: (1) cell-sorting within an aggregate formed by two types of cells with different adhesivities, and (2) fusion of two spherical aggregates of living cells.Comment: 11 pages, 7 figures; submitted to Phys Rev

Co-electrolysis of H2O and CO2 on exsolved Ni nanoparticles for efficient syngas generation at controllable H2/CO ratios

Syngas (CO+H2) is a key-intermediate for the production of liquid fuels via the Fischer-Tropsch process. An emerging technology for generating syngas is the co-electrolysis of H2O/CO2 in solid oxide cells powered by renewable electricity. An application of this technology, however, is still challenging because the Ni-based cermet fuel electrodes are susceptible to degradation under redox and coking conditions, requiring protective hydrogen atmosphere to maintain stable operation. Perovskite oxides are the most promising alternatives due to their redox stability, extensive range of functionalities and the exsolution concept. The latter allows perovskites to be decorated with uniformly dispersed Ni nanoparticles with unique functionalities that can dramatically enhance the performance. Herein, we demonstrate the advantage of employing a nanoparticle-decorated La0.43Ca0.37Ni0.06Ti0.94O3 (LCT-Ni) perovskite to efficiently generate syngas at adjustable H2/CO ratios and simultaneously avoid the need of a reducing agent, hence decreasing the total cost and complexity of the process

Stemming Techniques for Arabic Words: A Comparative Study

Results show that the correction algorithm improved the accuracy of the rule-based one by about 14% and the positional letter ranking based algorithms by 7% to 10%. The adjusted positional letter ranking method proved to be the highest in accuracy among all five algorithms but slightly higher than the rule-based one. However, the rule-based algorithm was found to be the approach with the highest accuracy among all ten algorithms when the correction algorithm was included in it

Linear and Nonlinear Estimated GFR Slopes in ADPKD Patients Reaching ESRD

Correspondence Letter to the Edito

Low temperature methane conversion with perovskite-supported: exo / endo-particles

Lowering the temperature at which CH(4)is converted to useful products has been long-sought in energy conversion applications. Selective conversion to syngas is additionally desirable. Generally, most of the current CH(4)activation processes operate at temperatures between 600 and 900 degrees C when non-noble metal systems are used. These temperatures can be even higher for redox processes where a gas phase-solid reaction must occur. Here we employ the endogenous-exsolution concept to create a perovskite oxide with surface and embedded metal nanoparticles able to activate methane at temperatures as low as 450 degrees C in a cyclic redox process. We achieve this by using a non-noble, Co-Ni-based system with tailored nano- and micro-structure. The materials designed and prepared in this study demonstrate long-term stability and resistance to deactivation mechanisms while still being selective when applied for chemical looping partial oxidation of methane