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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]

• Child pedestrian injuries in Greater London are studied. • A host of exposure measures, built environment and socio-economic factors are considered. • School enrolment was found to be a better proxy exposure measure compared to number of schools. • Social inequities are found to be strongly associated with child pedestrian safety. Introduction: Child pedestrian safety remains a challenge despite the remarkable progress that has been attained in recent years, particularly, in high income jurisdictions such as London. This study sought to identify and quantify the magnitude of the effects of various explanatory variables, from the domains of transport, built and natural environment, socio-demographic and economic factors, on ward level child pedestrian injury frequencies in Greater London. Method: We adopted a multilevel random parameters model to investigate the factors associated with child pedestrian injuries given the hierarchical nature of the data comprising of wards nested within boroughs. Results: We found that crime, the Black, Asian, and Minority Ethnic (BAME) population, school enrollment, and the proportion of the population who walk five times a week had an increasing effect on the number of child pedestrian casualties. Conversely, the proportion of the population with a level 4 qualification and the number of cars per household had a decreasing effect. Conclusions: Our study identified high child pedestrian injury frequency wards and boroughs: Stratford and New Town had the highest expected child pedestrian injury frequencies followed by Selhurst, Westend, and Greenford Broadway. Some inner London boroughs are among the highest injury frequency areas; however, a higher number of high child pedestrian injury boroughs are in outer London. Practical Applications: The paper provides recommendations for policy makers for targeted child pedestrian safety improvement interventions and prioritization to optimize the utilization of often constrained resources. The study also highlights the importance of considering social inequities in policies that aim at improving child traffic safety. [ABSTRACT FROM AUTHOR]

Background: Although age is the biggest known risk factor for dementia, there remains uncertainty about other factors over the life course that contribute to a person's risk for cognitive decline later in life. Furthermore, the pathological processes leading to dementia are not fully understood. The main goals of Insight 46—a multi-phase longitudinal observational study—are to collect detailed cognitive, neurological, physical, cardiovascular, and sensory data; to combine those data with genetic and life-course information collected from the MRC National Survey of Health and Development (NSHD; 1946 British birth cohort); and thereby contribute to a better understanding of healthy ageing and dementia. Methods/Design: Phase 1 of Insight 46 (2015–2018) involved the recruitment of 502 members of the NSHD (median age = 70.7 years; 49% female) and has been described in detail by Lane and Parker et al. 2017. The present paper describes phase 2 (2018–2021) and phase 3 (2021–ongoing). Of the 502 phase 1 study members who were invited to a phase 2 research visit, 413 were willing to return for a clinic visit in London and 29 participated in a remote research assessment due to COVID-19 restrictions. Phase 3 aims to recruit 250 study members who previously participated in both phases 1 and 2 of Insight 46 (providing a third data time point) and 500 additional members of the NSHD who have not previously participated in Insight 46. Discussion: The NSHD is the oldest and longest continuously running British birth cohort. Members of the NSHD are now at a critical point in their lives for us to investigate successful ageing and key age-related brain morbidities. Data collected from Insight 46 have the potential to greatly contribute to and impact the field of healthy ageing and dementia by combining unique life course data with longitudinal multiparametric clinical, imaging, and biomarker measurements. Further protocol enhancements are planned, including in-home sleep measurements and the engagement of participants through remote online cognitive testing. Data collected are and will continue to be made available to the scientific community. [ABSTRACT FROM AUTHOR]

Load shapes derived from smart meter data are frequently employed to analyze daily energy consumption patterns, particularly in the context of applications like Demand Response (DR). Nevertheless, one of the most important challenges to this endeavor lies in identifying the most suitable consumer clusters with similar consumption behaviors. In this paper, we present a novel machine learning based framework in order to achieve optimal load profiling through a real case study, utilizing data from almost 5000 households in London. Four widely used clustering algorithms are applied specifically K-means, K-medoids, Hierarchical Agglomerative Clustering and Density-based Spatial Clustering. An empirical analysis as well as multiple evaluation metrics are leveraged to assess those algorithms. Following that, we redefine the problem as a probabilistic classification one, with the classifier emulating the behavior of a clustering algorithm, leveraging Explainable AI (xAI) to enhance the interpretability of our solution. According to the clustering algorithm analysis the optimal number of clusters for this case is seven. Despite that, our methodology shows that two of the clusters, almost 10% of the dataset, exhibit significant internal dissimilarity. As a result, these clusters have been excluded from consideration for DR programs. The scalability and versatility of our solution makes it an ideal choice for power utility companies aiming to segment their users for creating more targeted DR programs. • Assessment of four ML clustering algorithms of different nature. • Re-definition of the problem using probabilistic classification and xAI. • Optimization of demand-side management clusters combining ML and empirical knowledge. • Experimental application on historical data from 4438 households in London. [ABSTRACT FROM AUTHOR]

The growth of public transport networks in the late-nineteenth and early-twentieth centuries had profound effects on commuting in the industrialized world, yet the consequences for labor markets during this important period of historical development remains largely unstudied. This paper draws on a unique dataset combining individual commuting and wage information for working-class residents of London, circa 1930, to analyze, for the first time, the nature of and returns to commuting shortly after when networks were first built. A sizeable majority of working-class Londoners worked within a short walk of their residence in 1890. By 1930, over 70 percent commuted at least one kilometer. Commuting allowed workers to search for jobs over a wider geographic area and across a larger number of potential employers. This, in turn, potentially increased workers' bargaining power and improved employer-employee matching. We show that wage returns to commuting were on the order of 1.5–3.5 percent per kilometer travelled. Access to public transport increased both the probability of commuting and distance commuted but had little or no direct effect on the probability of being employed or on earnings. We argue that these results are consistent with a search and matching framework; commuting led to workers finding jobs more suited to their skills and to better matches with employers. We also provide descriptive evidence from contemporary sources to describe the impact of commuting on improving quality of life by reducing urban crowding. [ABSTRACT FROM AUTHOR]