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The adsorption of polymers on solid surfaces is common in many industrial applications, such as coatings, paints, catalysis, colloids, and adhesion processes. The properties of absorbed polymers commonly vary with temperature. In this paper, inverse gas chromatography at infinite dilution was used to determine the physicochemical characterization of PMMA adsorbed on silica. A new method based on the London dispersion equation was applied with a new parameter associating the deformation polarizability with the harmonic mean of the ionization energies of the solvent. More accurate values of the dispersive and polar interaction energies of the various organic solvents adsorbed on PMMA in bulk phase and PMMA/silica at different recovery fractions were obtained, as well as the Lewis acid–base parameters and the transition temperatures of the different composites. It was found that the temperature and the recovery fraction have important effects on the various physicochemical and thermodynamic properties. The variations in all the interaction parameters showed the presence of three transition temperatures for the different PMMA composites adsorbed on silica with various coverage rates, with a shift in these temperatures for a recovery fraction of 31%. An important variation in the polar enthalpy and entropy of adsorption, the Lewis acid–base parameters and the intermolecular separation distance was highlighted as a function of the temperature and the recovery fraction of PMMA on silica. [ABSTRACT FROM AUTHOR]

The determination of the polar surface free energy, polar properties, and Lewis's acid base of solid materials is of capital importance in many industrial processes, such as adhesion, coatings, two-dimensional films, and adsorption phenomena. (1) Background: The physicochemical properties of many solid particles were characterized during the last forty years by using the retention time of injected well-known molecules into chromatographic columns containing the solid substrates to be characterized. The obtained net retention time of the solvents adsorbed on the solid, allowing the determination of the net retention volume directly correlated to the specific surface variables, dispersive, polar, and acid–base properties. (2) Methods: Many chromatographic methods were used to quantify the values of the different specific surface variables of the solids. However, one found a large deviation between the different results. In this paper, one proposed a new method based on the London dispersion equation that allowed the quantification of the polar free energy of adsorption, as well as the Lewis's acid–base constants of many solid surfaces. (3) Results: The newly applied method allowed us to obtain the polar enthalpy and entropy of adsorption of polar model organic molecules on several solid substrates, such as silica, alumina, MgO, ZnO, Zn, TiO2, and carbon fibers. (4) Conclusions: our new method based on the separation between the dispersive and polar free surface energy allowed us to better characterize the solid materials. [ABSTRACT FROM AUTHOR]

This paper analyses the spatial trends and patterns of the crime rates in London and explores how socio-economic characteristics affect crime rates with consideration of the geographic context across London. The 2015 London Crime Statistics and Socio-economic Characteristics datasets were used. First, we investigated the spatial patterns of crime rates through exploratory spatial analysis at the ward level. In addition, both the ordinary least square (OLS) model and geographically weighted regression (GWR) model, which allow the effects of factors to vary in spatial scales, were adopted and compared to explore the potential spatially varying effect across London. The results showed that there exists obvious spatial clustering for the crime rate in central London. Both global and local Moran's I values indicated the spatial dependence of crime at the ward level. The GWR model performed better in explaining crime rates than the OLS model. Only two factors, namely, the percentage of children aged from 0 to 15 and employment rates, had significant spatial variability in London. The influences of the percentage of children aged 0 to 15 on crime rates are constantly negative over a spatial scale; however, employment rates positively affect crime rates in the north-western areas near the centre of London. Therefore, this paper focuses more on the spatial perspective, which fills the gap in traditional crime analysis, especially on the spatially varying influence of socio-economic status. [ABSTRACT FROM AUTHOR]

The Church of England's historic buildings represent the single largest group of heritage sites in the UK, playing a key public-facing role in the church's 'cultural witness'. However, they are complex historic environments implicated in the recent focus on 'contested heritage' and imperial legacies. The wider heritage sector's answer to the adversarial nature of this debate has been to turn contested histories into dialogical opportunities; participatory and collaborative approaches to interpretation and curation have become an important feature of much recent secular heritage work. Yet, the CofE has not yet articulated or embraced the value of similar initiatives for its own collections, with guidance at the institutional level aimed primarily at conservation and protection. This paper initiates a discussion about how engagement with sensitive memories enhances the importance of CofE's cultural heritage. It offers a preliminary report of a research project led by the author titled 'Inclusive Interpretations of Christian Heritage', carried out between 2021 and 2022 at iconic churches in central London. After discussing the theoretical context, project rationale, and method, the paper discusses the connections which Christians from ethnic minority or immigrant backgrounds have to ecclesiastical spaces usually associated with national history. The perspectives of previously unrepresented groups can supplement expert assessments concerning a site's significance, revealing important areas in which the CofE's cultural assets hold meaning beyond national or aesthetic importance. The paper argues that widening community engagement represents a crucial task for accentuating the social and civic importance of the CofE's cultural heritage. [ABSTRACT FROM AUTHOR]

One of the main pathways that cities are taking to reduce greenhouse gas emissions is the decarbonisation of the electricity supply in conjunction with the electrification of transport and heat services. Estimating these future electricity demands, greatly influenced by end-users' behaviour, is key for planning energy systems. In this context, support tools can help decision-makers assess different scenarios and interventions during the design of new planning guidelines, policies, and operational procedures. This paper presents a novel bottom-up decision support framework using an agent-based modelling and simulation approach to evaluate, in an integrated way, transport and heat electrification scenarios in urban areas. In this work, an open-source tool named SmartCityModel is introduced, where agents represent energy users with diverse sociodemographic and technical attributes. Based on agents' behavioural rules and daily activities, vehicle trips and building occupancy patterns are generated together with electric vehicle charging and building heating demands. A representative case study set in London, UK, is shown in detail, and a summary of more than ten other case studies is presented to highlight the flexibility of the framework to generate high-resolution spatiotemporal energy demand profiles in urban areas, supporting decision-makers in planning low-carbon and sustainable cities. [ABSTRACT FROM AUTHOR]

As known, the realistic, exponential, fall-off of the rate of production of light mesons in the chromo-electric field of a quark–antiquark string, as a function of the meson mass, can be obtained from the Schwinger-formula Gaussian fall-off within a phenomenological approach which assumes a certain distribution of the string tension. This approach gets a clear meaning in the London limit of the dual superconductor, where the logarithmic increase of the chromo-electric field towards the core of the string leads precisely to the change of the Gaussian fall-off to the exponential one, thus allowing for an identification of the phenomenological distribution of the string tension. In this paper, we demonstrate that, for this distribution of the string tension, the distribution of large-size Yang–Mills instantons, which are interacting with the confining monopole background, becomes O (1 / ρ 3) , where ρ is the size of an instanton. Since such a distribution of large-size instantons is known to yield confinement, we conclude that, in the London limit of the dual-superconductor vacuum, instantons can form a confining medium, and we evaluate their contribution to the total string tension. [ABSTRACT FROM AUTHOR]

The building and transportation sectors are responsible for the greatest proportion of energy consumption in cities. While they are intrinsically interlinked with urban built form and density, climate change and technological innovation are having an effect on their relative contributions. This paper aims to develop an optimisation framework to facilitate the identification of the most energy-efficient urban built forms and urban geometry for the future built environment that can be adapted to the changing climate and ongoing technological development. It examines future scenarios for the city of London as a temperate climate zone (as a case study), in 2050, and contrasts it with the present situation. Specifically, the impact of climate change along with the penetration of electric vehicles into the transportation system that can be charged via rooftop photovoltaics is investigated. This study initially develops the geometrical models of four selected urban built forms and, secondly, analyzes their energy performance using an urban energy simulation software. The results, showing the impact of future scenarios on building energy performance, urban built form and density, demonstrate that court and tunnel-court built forms show better energy performance for future development. It is therefore recommended that for future urban developments in London, deep plan court and tunnel-court buildings with a lower number of storeys and a large cut-off angle are more advantageous in terms of building energy to accommodate the expected climate change. Finally, results of simulation trials indicate that the total building energy demand in 2050 is considerably higher than in the present climate as a result of additional cooling load and electric vehicle charging load. [ABSTRACT FROM AUTHOR]

This paper proposes an accurate short-term prediction model of bike-sharing demand with the hybrid TCN-GRU method. The emergence of shared bicycles has provided people with a low-carbon, green and healthy way of transportation. However, the explosive growth and free-form development of bike-sharing has also brought about a series of problems in the area of urban governance, creating a new opportunity and challenge in the use of a large amount of historical data for regional bike-sharing traffic flow predictions. In this study, we built an accurate short-term prediction model of bike-sharing demand with the bike-sharing dataset from 2015 to 2017 in London. First, we conducted a multidimensional bike-sharing travel characteristics analysis based on explanatory variables such as weather, temperature, and humidity. This will help us to understand the travel characteristics of local people, will facilitate traffic management and, to a certain extent, improve traffic congestion. Then, the explanatory variables that help predict the demand for bike-sharing were obtained using the Granger causality with the entropy theory-based MIC method to verify each other. The Multivariate Temporal Convolutional Network (TCN) and Gated Recurrent Unit (GRU) model were integrated to build the prediction model, and this is abbreviated as the TCN-GRU model. The fitted coefficient of determination R2 and explainable variance score (EVar) of the dataset reached 98.42% and 98.49%, respectively. Meanwhile, the mean absolute error (MAE) and root mean square error (RMSE) were at least 1.98% and 2.4% lower than those in other models. The results show that the TCN-GRU model has strong efficiency and robustness. The model can be used to make short-term accurate predictions of bike-sharing demand in the region, so as to provide decision support for intelligent dispatching and urban traffic safety improvement, which will help to promote the development of green and low-carbon mobility in the future. [ABSTRACT FROM AUTHOR]

In this paper, DFT computations revealed the mechanisms of the asymmetric catalytic reactions of diisopropylzinc with pyrimidylaldehyde catalyzed by 1- and 2-aza[6]helicenes, which make them effective inductors of the autocatalytic chiral amplification Soai reaction. The generation of chirality takes place through the formation of adducts of aldehyde and helicenes stabilized via non-covalent disperse interactions strictly defining the orientation of the aldehyde molecule in the corresponding transition state. [ABSTRACT FROM AUTHOR]

In this paper, driving strategy optimization for a track is proposed for an energy efficient battery electric vehicle dedicated to the Shell Eco-marathon. A measurement-based mathematical vehicle model was developed to simulate the behavior of the vehicle. The model contains complicated elements such as the vehicle's cornering resistance and the efficiency field of the entire powertrain. The validation of the model was presented by using the collected telemetry data from the 2019 Shell Eco-marathon competition in London (UK). The evaluation of applicable powertrains was carried out before the driving strategy optimization. The optimal acceleration curve for each investigated powertrain was defined. Using the proper powertrain is a crucial part of energy efficiency, as the drive has the most significant energy demand among all components. Two tracks with different characteristics were analyzed to show the efficiency of the proposed optimization method. The optimization results are compared to the reference method from the literature. The results of this study provide an applicable vehicle modelling methodology with efficient optimization framework, which demonstrates 5.5% improvement in energy consumption compared to the reference optimization theory. [ABSTRACT FROM AUTHOR]

Examining the complexity of urban form may help to understand human behavior in urban spaces, thereby improving the conditions for sustainable design of future cities. Metrics, such as fractal dimension, ht-index, and cumulative rate of growth (CRG) index have been proposed to measure this complexity. However, as these indicators are statistical rather than spatial, they result in an inability to characterize the spatial complexity of urban forms, such as building footprints. To overcome this problem, this paper proposes a graph-based fractality index (GFI), which is based on a hybrid of fractal theory and deep learning techniques. First, to quantify the spatial complexity, several fractal variants were synthesized to train a deep graph convolutional neural network. Next, building footprints in London were used to test the method, where the results showed that the proposed framework performed better than the traditional indices, i.e., the index is capable of differentiating complex patterns. Another advantage is that it seems to assure that the trained deep learning is objective and not affected by potential biases in empirically selected training datasets Furthermore, the possibility to connect fractal theory and deep learning techniques on complexity issues opens up new possibilities for data-driven GIS science. [ABSTRACT FROM AUTHOR]

Technological advances have enabled new sources of geoinformation, such as geosocial media, and have supported the propagation of the concept of smart cities. This paper argues that a city cannot be smart without citizens in the loop, and that a geosocial sensor might be one component to achieve that. First, we need to better understand which facets of urban life could be detected by a geosocial sensor, and how to calibrate it. This requires replicable studies that foster longitudinal and comparative research. Consequently, this paper examines the relationship between geosocial media content and socio-demographic census data for a global city, London, at two administrative levels. It aims for a transparent study design to encourage replication, using Term Frequency—Inverse Document Frequency of keywords, rule-based and word-embedding sentiment analysis, and local cluster analysis. The findings of limited links between geosocial media content and socio-demographic characteristics support earlier critiques on the utility of geosocial media for smart city planning purposes. The paper concludes that passive listening to publicly available geosocial media, in contrast to pro-active engagement with citizens, seems of limited use to understand and improve urban quality of life. [ABSTRACT FROM AUTHOR]

Transport accessibility is an important driver of urban growth and key to the sustainable development of cities. This paper presents a simple GIS-based tool developed to allow the rapid analysis of accessibility by different transport modes. Designed to be flexible and use publicly-available data, this tool (built in ArcGIS) uses generalized cost to measure transport costs across networks including monetary and distance components. The utility of the tool is demonstrated on London, UK, showing the differing patterns of accessibility across the city by different modes. It is shown that these patterns can be examined spatially, by accessibility to particular destinations (e.g., employment locations), or as a global measure across a whole city system. A number of future infrastructure scenarios are tested, examining the potential for increasing the use of low-carbon forms of transport. It is shown that private car journeys are still the least cost mode choice in London, but that infrastructure investments can play a part in reducing the cost of more sustainable transport options. [ABSTRACT FROM AUTHOR]

There has been a world-wide surge of interest in cycling over the last 10 years of which London has seen a continuous growth in cyclists and investment in infrastructure that has resulted in the introduction of the Barclays Cycle Superhighway and Barclays Cycling Hiring Scheme. Despite the investment in cycling infrastructure, there has been little understanding of cycling activity patterns in general and the effect of spatial configuration on cycling route choices. This research aims at measuring the impact of cycling infrastructure and spatial configuration on aggregate cyclist movement over two time periods. To do so, this paper presents a spatial-based cyclist movement statistical model that regress cyclist movement flows with measure of spatial configuration, safety and infrastructure and urban character attributes. Using Elephant and Castle, a Central London location, as a case study, the authors analyze cycling movement data sets from 2003 and 2012 to compare the change in cycling behaviour and the impact that the Cycling Superhighway 07, introduced in 2011, has had on cycling patterns. Findings confirm the growth of cycling in London with a 1000% increase in cyclists along some routes in comparison to a 10% increase in population at the same time. More importantly, results also suggest that higher cyclist movement were observed along routes with greater convenience and continuity--over and above route segregation from vehicular traffic. The relationship between spatial configuration and aggregate cyclists movement is consistent between 2003 and 2012 where spatial configuration have remained the same while changes were observed in both modal split and cycling infrastructure. This result is in line with previous research wherein aggregate higher cyclists movement are observed on major routes offering direct connections than less direct routes. From a spatial cognition perspective, this research enriches our understanding on how the external built environment as measured by the spatial configuration measure relates to aggregated cyclists movement overtime and in identifying key potential factors in influencing cyclist wayfinding. Further research is needed into validating the results and examining this relationship at an individual basis on route choice. These results help us better understand the trade off between cycling safety and cycling legibility which could help inform cycling route design in the future. [ABSTRACT FROM AUTHOR]

This paper analyzes the price correlation effect between domestic and foreign copper futures contracts. The VAR-BEKK-GARCH (1,1) spillover effect model and the BN-S class non-parametric model based on the jumping perspective are used. The co-integration test shows a long-term equilibrium relationship between the three copper futures markets, and the Granger causality test shows that copper futures contracts have significant two-way spillover effects between different periods in Shanghai for New York copper and unidirectional mean spillover effects for London copper. The BEKK model shows significant bidirectional fluctuation spillover effects between the futures contracts of the Shanghai, London, and New York copper markets before the stock market crash. After the crash, Shanghai and New York copper have significant one-way fluctuation spillover effects on London copper futures contracts. There are jumps within a single market, and the number of joint jumps between markets increases with the significance level. [ABSTRACT FROM AUTHOR]

This paper reports on the analysis and findings of the data collected during a mobile air quality campaign commissioned by the City of London Corporation (CoL). This was done using an equipped vehicle capable of taking continuous precision measurements of local air quality while travelling within the City. Several comparative analyses on measured Nitrogen Dioxide (NO2) data have been performed between Smogmobile data and those available from CoL precision systems as well as with indicative systems, namely Diffusion Tubes, distributed across the City. Key findings highlight that data collected from the Smogmobile, in terms of average concentration of NO2 across the City (62 µg/m3), are very similar to those obtained by averaging the values from the 48 indicative systems (59.5 µg/m3), with an error of just 4%. Overall, this study demonstrates significant potential and value in using mobile air quality measurements to support assessment of air quality over large areas by Local authorities. [ABSTRACT FROM AUTHOR]

In recent years, there have been several attempts to use machine learning techniques to improve the performance of exact and approximate optimization algorithms. Along this line of research, the present paper shows how supervised and unsupervised techniques can be used to improve the quality of the solutions generated by a heuristic for the Time-Dependent Travelling Salesman Problem with no increased computing time. This can be useful in a real-time setting where a speed update (or the arrival of a new customer request) may lead to the reoptimization of the planned route. The main contribution of this work is to show how to reuse the information gained in those settings in which instances with similar features have to be solved over and over again, as it is customary in distribution management. We use a method based on the nearest neighbor procedure (supervised learning) and the K-means algorithm with the Euclidean distance (unsupervised learning). In order to show the effectiveness of this approach, the computational experiments have been carried out for the dataset generated based on the real travel time functions of two European cities: Paris and London. The overall average improvement of our heuristic over the classical nearest neighbor procedure is about 5 % for London, and about 4 % for Paris. [ABSTRACT FROM AUTHOR]

The preservation of natural assets is nowadays an essential commitment. In this regard, root systems are endangered by fungal diseases which can undermine the health and stability of trees. Within this framework, ground penetrating radar (GPR) is emerging as a reliable non-destructive method for root investigation. A coherent GPR-based root-detection framework is presented in this paper. The proposed methodology is a multi-stage data analysis system that is applied to semi-circular measurements collected around the investigated tree. In the first step, the raw data are processed by applying several standard and advanced signal processing techniques in order to reduce noise-related information. In the second stage, the presence of any discontinuity element within the survey area is investigated by analysing the signal reflectivity. Then, a tracking algorithm aimed at identifying patterns compatible with tree roots is implemented. Finally, the mass density of roots is estimated by means of continuous functions in order to achieve a more realistic representation of the root paths and to identify their length in a continuous and more realistic domain. The method was validated in a case study in London (UK), where the root system of a real tree was surveyed using GPR and a soil test pit was excavated for validation purposes. Results support the feasibility of the data processing framework implemented in this study. [ABSTRACT FROM AUTHOR]

Decarbonization of marine transport is a key global issue, with the carbon emissions of international shipping projected to increase 23% to 1090 million tonnes by 2035 in comparison to 2015 levels. Optimization of the energy system (especially propulsion system) in these vessels is a complex multi-objective challenge involving economical maintenance, environmental metrics, and energy demand requirements. In this paper, data from instrumented vessels on the River Thames in London, which includes environmental emissions, power demands, journey patterns, and variance in operational patterns from the captain(s) and loading (passenger numbers), is integrated and analyzed through automatic, multi-objective global optimization to create an optimal hybrid propulsion configuration for a hybrid vessel. We propose and analyze a number of computational techniques, both for monitoring and remaining useful lifetime (RUL) estimation of individual energy assets, as well as modeling and optimization of energy use scenarios of a hybrid-powered vessel. Our multi-objective optimization relates to emissions, asset health, and power performance. We show that, irrespective of the battery packs used, our Relevance Vector Machine (RVM) algorithm is able to achieve over 92% accuracy in remaining useful life (RUL) predictions. A k-nearest neighbors algorithm (KNN) is proposed for prognostics of state of charge (SOC) of back-up lead-acid batteries. The classifier achieved an average of 95.5% accuracy in a three-fold cross validation. Utilizing operational data from the vessel, optimal autonomous propulsion strategies are modeled combining the use of battery and diesel engines. The experiment results show that 70% to 80% of fuel saving can be achieved when the diesel engine is operated up to 350 kW. Our methodology has demonstrated the feasibility of combination of artificial intelligence (AI) methods and real world data in decarbonization and optimization of green technologies for maritime propulsion. [ABSTRACT FROM AUTHOR]

The decay rates of building stones and, the processes leading to their deterioration is governed by intrinsic properties such as texture, mineralogy, porosity and pore size distribution, along with other extrinsic factors related to the climate and anthropogenic activities. For urban cities such as London, the influence of extrinsic factors like temperature and rainfall, as well as the concentrations of air pollutants, such as sulphur and nitrogen oxides, along with the emissions of carbonaceous aerosols, can be particularly significant. While considering the long-term preservation of building stones used in various heritage sites in the city, it is imperative to consider how the stone could be affected by the changing air pollutant concentrations, superimposed on the effects of climate change in the region, including rising average annual temperature and precipitation with a hotter, drier summer and, warmer, wetter winter months. This paper deals with the intrinsic rock properties of the common building stones of London, including limestone, marble, granite, sandstone, slate, flint as well as bricks, building on known characteristics including strength and durability that determine how and where they are placed in a building structure. The study reviews how these stones decay due to different processes such as salt weathering in sandstone, microcracking of quartz with kaolinisation of K-feldspar and biotite in granite and dissolution of calcite and dolomite, followed by precipitation of sulphate minerals in the carbonate rocks of limestone and marble. In the urban environment of London, with progressive build up in the concentration of atmospheric nitrogen oxides leading to an increasingly acidic environment and, with predicted climate change, the diverse stone-built heritage will be affected. For example, there can be enhanced carbonate dissolution in limestone with increased annual precipitation. Due to the prolonged wetter winter, any sandstone building stone will also undergo greater damage with a deeper wetting front. On the other hand, due to predicted wetter and warmer winter months, microcracking of any plagioclase in a granite is unlikely, thereby reducing the access of fluid and air pollutants to the Ca-rich core of the zoned crystals limiting the process of sericitisation. Management of the building stones in London should include routine expert visual inspection for signs of deterioration, along with mineralogical and compositional analyses and assessment of any recession rate. [ABSTRACT FROM AUTHOR]

This paper presents the application of Internet of Things (IoT) Technology and Building Energy Management System (BEMS) within the Marylebone Campus of the University of Westminster, located in central London, to improve the environmental performance of the existing building as well as enhance the learning experience on energy and sustainability. Sixty IoT sensors connected to minicomputers were planned to be deployed within three floors of the building to continuously measure the real-time environmental parameters, such as dry-bulb temperature, relative humidity, illuminance level, carbon dioxide, and sound levels. Experimental workshops were also arranged with undergraduate and post-graduate students at their classrooms using IoT sensors, portable Bluetooth sensors and online questionnaires to increase awareness of the effect of environmental and behavioural changes on energy saving through real-time visualisation. Users' subjective feedback on their workplace was also collected through Post Occupancy Evaluation (POE) questionnaire surveys. The results show the effectiveness of IoT systems and BEMS in supplying the building users and management with high-resolution, low-cost data acquisition systems highlighting the existing challenges and future scopes. The study also documents the process and the improvement in students' awareness of environmental and energy performance of their building through IoT data visualizations and POE. [ABSTRACT FROM AUTHOR]

In recent years, there has been a transformation in the value chain of different industrial sectors, like the electricity networks, with the appearance of smart grids. Currently, the underlying knowledge in raw data coming from numerous devices can mark a significant competitive advantage for utilities. It is the case of the Advanced Metering Infrastructure (AMI). Such technology gets user consumption characteristics at levels of detail that were previously not possible. In this context, the terms big data and data analytics become relevant, which are tools that allow using large volumes of information and the generation of valuable knowledge from raw data that can support data-driven decisions for operating on the grid. This paper presents the results of the big data implementation and data analytics techniques in a case study with smart metering data from the city of London. Implemented big data and data analytic techniques to show how to understand user consumption patterns on a broader horizon, the relationships with seasonal variables identify behaviors related to specific events and atypical consumptions. This knowledge helps support decision making about improving demand response programs and, in general, the planning and operation of the Smart Grid. [ABSTRACT FROM AUTHOR]

This paper evaluates the liquid water at the gas diffusion layer-channel (GDL-channel) interface of reconstructed GDL microstructures with uniform and non-uniform fiber diameters in wave-like channels. A non-uniform GDL microstructure is reconstructed for the first time at the GDL-channel interface to evaluate droplet motion. The three-layer GDL microstructures are generated using the stochastic technique and implemented using the OpenFOAM computational fluid dynamics (CFD) software (OpenFOAM-6, OpenFOAM Foundation Ltd., London, UK). The present study considers the relationship between reconstructed GDL surfaces with varying fiber diameters, wettability, superficial inlet velocity and droplet size. Results show that the droplet detachment and the average droplet velocity decrease with an increase in the fiber diameter as well as the structural arrangement of the fibers. Under the non-uniform fiber arrangement, the removal rate of water droplets is not significantly improved. However, the choice of smaller fiber diameters facilitates the transport of droplets, as hydrophobicity increases even at slight surface roughness. The results also indicate that the average droplet velocity decreases under low inlet velocity conditions while increasing under high inlet velocity conditions. Therefore, the structural make-up of the GDL-channel interface influences the droplet dynamics, and the implementation of a non-uniform GDL structure should also be considered in the GDL designs. [ABSTRACT FROM AUTHOR]

The daily commute represents a source of chronic stress that is positively correlated with physiological consequences, including increased blood pressure, heart rate, fatigue, and other negative mental and physical health effects. The purpose of this research is to investigate and predict the physiological effects of commuting in Greater London on the human body based on machine-learning approaches. For each participant, the data were collected for five consecutive working days, before and after the commute, using non-invasive wearable biosensor technology. Multimodal behaviour, analysis and synthesis are the subjects of major efforts in computing field to realise the successful human–human and human–agent interactions, especially for developing future intuitive technologies. Current analysis approaches still focus on individuals, while we are considering methodologies addressing groups as a whole. This research paper employs a pool of machine-learning approaches to predict and analyse the effect of commuting objectively. Comprehensive experimentation has been carried out to choose the best algorithmic structure that suit the problem in question. The results from this study suggest that whether the commuting period was short or long, all objective bio-signals (heat rate and blood pressure) were higher post-commute than pre-commute. In addition, the results match both the subjective evaluation obtained from the Positive and Negative Affect Schedule and the proposed objective evaluation of this study in relation to the correlation between the effect of commuting on bio-signals. Our findings provide further support for shorter commutes and using the healthier or active modes of transportation. [ABSTRACT FROM AUTHOR]

In this survey article, at first, the author describes how he was involved in the late 1990s on Econophysics, considered in those times an emerging science. Inside a group of colleagues the methods of the Fractional Calculus were developed to deal with the continuous-time random walks adopted to model the tick-by-tick dynamics of financial markets Then, the analytical results of this approach are presented pointing out the relevance of the Mittag-Leffler function. The consistence of the theoretical analysis is validated with fitting the survival probability for certain futures (BUND and BTP) traded in 1997 at LIFFE, London. Most of the theoretical and numerical results (including figures) reported in this paper were presented by the author at the first Nikkei symposium on Econophysics, held in Tokyo on November 2000 under the title "Empirical Science of Financial Fluctuations" on behalf of his colleagues and published by Springer. The author acknowledges Springer for the license permission of re-using this material. [ABSTRACT FROM AUTHOR]

The Lee Tunnel was constructed as the first part of the Thames Tideway Improvement scheme, between 2010 and 2016. With tunnelling for the East section of the main Thames Tideway Tunnel, which joins the Lee Tunnel at Abbey Mills Pumping Station, beginning in early 2020, this paper investigates patterns of deformation in East London during construction of the Lee Tunnel. An unexpected geological feature, later identified as a drift filled hollow, was discovered during tunnelling. This study demonstrates that had eight years of ERS Persistent Scatterer Interferometry (PSI) data been analysed prior to tunnelling, the unusual pattern of displacement may have been recognised and further targeted borehole investigations taken place before the launch of the tunnel boring machine. Results also show how areas of different land use, including cemeteries and historic landfill, exhibit differences in settlement behaviour, compared with surrounding terraced housing. This research highlights the challenges in interpreting PSI results in an urban area with ongoing construction and the value of a long archive of data, which now spans almost three decades in London, that can be used to establish a baseline prior to construction. [ABSTRACT FROM AUTHOR]

Climate change amplified by rapidly increasing urbanization is resulting in rising temperatures within urban environments. In recent years, to mitigate this the design and construction of new buildings has emphasized "smart" methods and materials for individual buildings rather than landscape-level planning and evaluation of new developments. Remote Sensing potentially offers a cost-effective means to monitor effectiveness of landscape-level urban design and guide developers to improve thermal regimes. This paper focuses on satellite monitoring of thermal variation in the area of London most affected by construction in 2010–2015. Split-window Land Surface Temperature (LST) models were applied to ASTER and Landsat 8 satellite imagery, requiring separate investigation of thermal trends due to temporal inconsistency. Getis-Ord-Gi* cluster analysis of the ASTER image identified three main thermal hot spots: Eastern, Stratford railway/underground station, and Stratford High Street. It is assumed that increased thermal stress within these areas is mainly from anthropogenic heat. However, local thermal variations for Eastern Olympic Village are attributed to changing meteorological conditions, facade materials, canyon morphology and orientation, or insufficient shading and ventilation. Future development of a new cultural hub at this location will significantly affect distribution of green spaces and influence their cooling ability. [ABSTRACT FROM AUTHOR]

Passengers travelling on the London underground tubes currently have no means of knowing their whereabouts between stations. The challenge for providing such service is that the London underground tunnels have no GPS, Wi-Fi, Bluetooth, or any kind of terrestrial signals to leverage. This paper presents a novel yet practical idea to track passengers in realtime using the smartphone accelerometer and a training database of the entire London underground network. Our rationales are that London tubes are self-driving transports with predictable accelerations, decelerations, and travelling time and that they always travel on the same fixed rail lines between stations with distinctive bumps and vibrations, which permit us to generate an accelerometer map of the tubes' movements on each line. Given the passenger's accelerometer data, we identify in realtime what line they are travelling on and what station they depart from, using a pattern-matching algorithm, with an accuracy of up to about 90% when the sampling length is equivalent to at least 3 station stops. We incorporate Principal Component Analysis to perform inertial tracking of passengers' positions along the line when trains break away from scheduled movements during rush hours. Our proposal was painstakingly assessed on the entire London underground, covering approximately 940 km of travelling distance, spanning across 381 stations on 11 different lines. [ABSTRACT FROM AUTHOR]

Air pollution contributes to 9400 deaths annually in London and diesel vehicles are considered a major source of lethal air pollutants. Consequently, the UK government announced its intention to ban diesel vehicles by 2040 to achieve a sustainable zero-carbon road transport system. Since no empirical studies have used a bottom-up approach to seek Londoners' views, it is therefore worth investigating the public opinion regarding this forthcoming ban. This paper aims to fill this research gap by taking London as a case study. A survey was designed, and fieldwork was conducted to distribute questionnaires to Londoners. Completed questionnaires were analysed using both quantitative and qualitative methods. The findings revealed that the majority of Londoners would be in favour of the ban if they were sufficiently exposed to the appropriate sources of information and were favourably disposed towards environmental protection measures. The results also showed that Londoners were more likely to switch to electric vehicles (EVs) if they were offered generous incentives and encouraged to use scrappage schemes. The present study makes a strong case for enforcing the ban well before 2040. The significance of this research is to provide clearer signals regarding the future of diesel vehicles, which in turn will strengthen the EV policy and uptake. [ABSTRACT FROM AUTHOR]