Development of a composite regional vulnerability index and its relationship with the impacts of the COVID‑19 pandemic

The interactions between vulnerability and human activities have largely been regarded in terms of the level of risk they pose, both internally and externally, for certain groups of disadvantaged individuals and regions/areas. However, to date, very few studies have attempted to develop a comprehensive composite regional vulnerability index, in relation to travel, housing, and social deprivation, which can be used to measure vulnerability at an aggregated level in the social sciences. Therefore, this research aims to develop a composite regional vulnerability index with which to examine the combined issues of travel, housing and socio-economic vulnerability (THASV index). It also explores the index’s relationship with the impacts of the COVID-19 pandemic, reflecting both social and spatial inequality, using Greater London as a case study, with data analysed at the level of Middle Layer Super Output Areas (MSOAs). The findings show that most of the areas with high levels of composite vulnerability are distributed in Outer London, particularly in suburban areas. In addition, it is also found that there is a spatial correlation between the THASV index and the risk of COVID-19 deaths, which further exacerbates the potential implications of social deprivation and spatial inequality. Moreover, the results of the multiscale geographically weighted regression (MGWR) show that the travel and socio-economic indicators in a neighbouring district and the related vulnerability indices are strongly associated with the risk of dying from COVID-19. In terms of policy implications, the findings can be used to inform sustainable city planning and urban development strategies designed to resolve urban socio-spatial inequalities and the potential related impacts of COVID-19, as well as guiding future policy evaluation of urban structural patterns in relation to vulnerable areas

Is it possible to compete with car use? How buses can facilitate sustainable transport

The need to prioritise the development of bus transport has attracted widespread attention in the literature. This study aims to investigate how buses can be used to facilitate a sustainable transport system, using Heze, in China, as a case study. Our results show that older people, unemployed residents, and those whose points of departure or arrival are within the city centre are more likely to travel by bus. In addition, compared to other travel modes, travel by bus tends to become more popular as travel time and distance increase. We predict the probabilities of people using buses for journeys of different travel times and over varying distances and rank them in order. The results suggest that bus travel could potentially replace car travel when the travel time is between 15 and 30 minutes or the travel distance is more than 9 km. In terms of policy implications, governments and planners should pay more attention to creating additional bus lanes, extending the bus network and its infrastructure, optimising bus-related facilities and services, particularly for older adults, and increasing the punctuality and reliability of bus travel

Multi-task learing for subspace segmentation

Subspace segmentation is the process of clustering a set of data points that are assumed to lie on the union of multiple linear or affine subspaces, and is increasingly being recognized as a fundamental tool for data analysis in high dimensional settings. Arguably one of the most successful approaches is based on the observation that the sparsest representation of a given point with respect to a dictionary formed by the others involves nonzero coefficients associated with points originating in the same subspace. Such sparse representations are computed independently for each data point via ℓ1-norm minimization and then combined into an affinity matrix for use by a final spectral clustering step. The downside of this procedure is two-fold. First, unlike canonical compressive sensing scenarios with ideally-randomized dictionaries, the data-dependent dictionaries here are unavoidably highly structured, disrupting many of the favorable properties of the ℓ1 norm. Secondly, by treating each data point independently, we ignore useful relationships between points that can be leveraged for jointly computing such sparse representations. Consequently, we motivate a multi-task learning-based framework for learning coupled sparse representations leading to a segmentation pipeline that is both robust against correlation structure and tailored to generate an optimal affinity matrix. Theoretical analysis and empirical tests are provided to support these claims.Y. Wang is sponsored by the University of Cambridge Overseas Trust. Y. Wang and Q. Ling are partially supported by sponsorship from Microsoft Research Asia. Q. Ling is also supported in part by NSFC grant 61004137. W. Chen is supported by EPSRC Research Grant EP/K033700/1 and the Natural Science Foundation of China 61401018.This is the final version of the article. It first appeared from JMLR via http://jmlr.org/proceedings/papers/v37/wangc15.htm

Investigation of the drivers of logistics outsourcing in the United Kingdom's pharmaceutical manufacturing industry

Logistics outsourcing is a practice commonly used by firms to allow them to access capabilities that they lack internally. Although the main drivers of outsourcing in general are fairly well known, the question of what explains logistics outsourcing decisions within the UK pharmaceutical manufacturing industry, in particular, remains under-researched. Therefore, this study aims to bridge the aforementioned gap in the literature. We surveyed 49 drug manufacturers located in the UK using a web-based questionnaire. The data collected were analysed using logistics regression, exploratory factor analysis, and t-tests. We found that UK drug manufacturers regard improving quality and reliability and reducing logistics costs as the most significant reasons for outsourcing logistics services. We also found a direct positive relationship between the service provider's techno-commercial offerings and delivery performance, and the likelihood of being selected to provide these services. We further explored materials transportation, product delivery, research and development, and clinical trials, which are among the most frequently outsourced logistics activities in the UK pharmaceutical manufacturing industry. The study contributes to the wider literature on logistics outsourcing, and more specifically to that on the UK pharmaceutical manufacturing industry. Findings from this research can also be used to guide outsourcing practitioners’ decisions about the selection of logistics service providers. In addition, the study can help to enhance the service providers' understanding of why firms buy logistics services and which services they are likely to buy

TimeMachine: Timeline Generation for Knowledge-Base Entities

We present a method called TIMEMACHINE to generate a timeline of events and relations for entities in a knowledge base. For example for an actor, such a timeline should show the most important professional and personal milestones and relationships such as works, awards, collaborations, and family relationships. We develop three orthogonal timeline quality criteria that an ideal timeline should satisfy: (1) it shows events that are relevant to the entity; (2) it shows events that are temporally diverse, so they distribute along the time axis, avoiding visual crowding and allowing for easy user interaction, such as zooming in and out; and (3) it shows events that are content diverse, so they contain many different types of events (e.g., for an actor, it should show movies and marriages and awards, not just movies). We present an algorithm to generate such timelines for a given time period and screen size, based on submodular optimization and web-co-occurrence statistics with provable performance guarantees. A series of user studies using Mechanical Turk shows that all three quality criteria are crucial to produce quality timelines and that our algorithm significantly outperforms various baseline and state-of-the-art methods.Comment: To appear at ACM SIGKDD KDD'15. 12pp, 7 fig. With appendix. Demo and other info available at http://cs.stanford.edu/~althoff/timemachine

Vertically Aligned Gold Nanorod Monolayer on Arbitrary Substrates: Self-Assembly and Femtomolar Detection of Food Contaminants

Cataloged from PDF version of article.Public attention to the food scandals raises an urgent need to develop effective and reliable methods to detect food contaminants. The current prevailing detections are primarily based upon liquid chromatography, mass spectroscopy, or colorimetric methods, which usually require sophisticated and time-consuming steps or sample preparation. Herein, we develop a facile strategy to assemble the vertically aligned monolayer of Au nanorods with a nominal 0.8 nm gap distance and demonstrate their applications in the rapid detection of plasticizers and melamine contamination at femtomolar level by surface-enhanced Raman scattering spectroscopy (SERS). The SERS signals of plasticizers are sensitive down to 0.9 fM concentrations in orange juices. It is the lowest detection limit reported to date, which is 7 orders of magnitude lower than the standard of United States (6 ppb). The highly organized vertical arrays generate the reproducible "SERS-active sites" and can be achieved on arbitrary substrates, ranging from silicon, gallium nitride, glass to flexible poly(ethylene naphthalate) substrates

Effect of synthesized temperature on the assembly and properties of four lanthanide supramolecular frameworks

Four new lanthanide coordination polymers, [H3O][Ln3(HPA)10(H2O)3·2H2O] (Ln = Pr for 1, Ln = Nd for 2), [Ln2(HPA)6(H2O)4·2H2O] (Ln = Sm for 3, Ln = Tb for 4) (HHPA =3-(4-hydroxyphenyl)propanoic acid), were successfully synthesized and characterized. 1 and 2 are isostructural and have 1D metal chain structure, while 3 and 4 show 0D network with binuclear subunits. The results indicated that the effect of reaction temperature can modulate the final structures. The HPA ligands adopt bidentate chelating and tridentate chelating bridging modes to coordinate with Ln(III) ions in 1-4. It has been shown that 4 can act as a fluorescent sensor for highly sensitive detection of nitroaromatics and Fe3+.               KEY WORDS: Sensor, Lanthanide, Structure, Temperature Bull. Chem. Soc. Ethiop. 2019, 33(1), 113-125DOI: https://dx.doi.org/10.4314/bcse.v33i1.1