Abstract Issues of space, place and politics run deep. There is a long history of the entanglement of the conceptualisation of space and place with the framing of political positions. The injunction to think space relationally is a very general one and, as this collection indicates, can lead in many directions. The particular avenue to be explored in this paper concerns the relationship between identity and responsibility, and the potential geographies of both.
In the urban environment, the transient electromagnetic method often suffers from severe electromagnetic interference because it employs coils to perceive the electromagnetic field. To address this issue, the DC method employs electrodes to shield the electromagnetic noise in the urban environment. In this paper, a new geophysical resistivity imaging sensing approach based on a DC inversion model is proposed to explore and identify the path of underground water to solve the water disaster problem. A real-world case study is carried out in Xinjiang Region. In order to investigate the underground water conducted path, an electrode network is designed and installed for the buildings of interest to collect the DC electrical current and DC electrical potential data. Then, the proposed method establishes a DC resistivity inversion model based on the forward theory and finite-volume technique to extract the geological information from the collected data and visualize the underground water conducted path. Different from existing popular numerical techniques, such as the finite element and finite difference, the finite-volume technique has clear theoretical explanations to make the obtained geological images understandable. As a result, the underground water path can be directly observed from the geological images calculated by the proposed approach to successfully identify and locate the underground water disaster in real-world applications.
Aigerim Assylkhanova, Gyula Nagy, Cezar Morar
et al.
The topic of dark tourism emerged in the last three decades as tourism became more accessible. It allows forgotten history to be revised and transferred to the public. This study aims to restructure existing categorization regarding dark tourism and address the research gaps in dark tourism studies. We collected studies from international publication databases – Scopus, Web of Science, and Google Scholar. We pre-processed the following data for each study: topic, authors’ location of university affiliation, study area, year of publication, top-cited articles, top productive journals in publishing dark tourism studies, keywords, and internality/externality of the author from the study area. With the current paper, we analysed review articles published from 1996 to 2024 (first quarter), applying qualitative methods. Based on these, a new analytical framework was generated. Furthermore, the connections between research topics were also analysed. The results of the analysis highlight specific research gaps in the literature on dark tourism and address poorly visible research fields in international journals, e.g. terrorism-related research, social media links of dark tourism, postcolonial contexts, or commemoration of communist past and heritage. Consequently, certain countries and regions are underrepresented in the literature. This critical review offers new research areas but also gives some directions to the theoretical enrichment of the dark tourism concept.
Ilias Diakonikolas, Daniel M. Kane, Sihan Liu
et al.
We study the task of testable learning of general -- not necessarily homogeneous -- halfspaces with adversarial label noise with respect to the Gaussian distribution. In the testable learning framework, the goal is to develop a tester-learner such that if the data passes the tester, then one can trust the output of the robust learner on the data.Our main result is the first polynomial time tester-learner for general halfspaces that achieves dimension-independent misclassification error. At the heart of our approach is a new methodology to reduce testable learning of general halfspaces to testable learning of nearly homogeneous halfspaces that may be of broader interest.
Antonia Eastwood, Alba Juárez‐Bourke, Scott Herrett
et al.
Abstract The values that people hold are thought to be key in bringing about the transformative change needed to halt biodiversity loss. Caring for nature has been conceptualised as a relational value and is thought to be largely shaped in childhood and adolescence. Relational values, arising from human–nature interactions, are increasingly being viewed as critical to halting biodiversity loss. However, caring for nature is not seen as sufficient to bring about pro‐environmental behaviour; a sense of efficacy is thought to also be required. As human societies become more urbanised, the greenspaces in and around our cites will become increasingly important as spaces where people can experience human–nature interactions. In our study, we explore participatory video (PV) as a tool for (a) providing new insights on young people's experience of greenspace, (b) enabling meaningful and transformative human–nature interactions and (c) building efficacy. The films produced by the young people showed that greenspaces were not for them; they were associated with violence and bullying or simply thought of as ‘boring’. The study also provided unexpected evidence for the potential of PV, to not only transform the way previously disengaged young people viewed their local greenspace but also how they use it, benefit from it and begin to change their behaviours towards it. The PV process also enabled young people, individually and collectively, to connect with nature and experience activities that enhanced their efficacy, confidence and sense of empowerment. We go on to discuss the potential of PV to help leverage the transformative change necessary to halt biodiversity loss and build more sustainable futures. A free Plain Language Summary can be found within the Supporting Information of this article.
Elnaz Yousefzadeh Barri, Steven Farber, Hadi Jahanshahi
et al.
Building an accurate model of travel behaviour based on individuals' characteristics and built environment attributes is of importance for policy-making and transportation planning. Recent experiments with big data and Machine Learning (ML) algorithms toward a better travel behaviour analysis have mainly overlooked socially disadvantaged groups. Accordingly, in this study, we explore the travel behaviour responses of low-income individuals to transit investments in the Greater Toronto and Hamilton Area, Canada, using statistical and ML models. We first investigate how the model choice affects the prediction of transit use by the low-income group. This step includes comparing the predictive performance of traditional and ML algorithms and then evaluating a transit investment policy by contrasting the predicted activities and the spatial distribution of transit trips generated by vulnerable households after improving accessibility. We also empirically investigate the proposed transit investment by each algorithm and compare it with the city of Brampton's future transportation plan. While, unsurprisingly, the ML algorithms outperform classical models, there are still doubts about using them due to interpretability concerns. Hence, we adopt recent local and global model-agnostic interpretation tools to interpret how the model arrives at its predictions. Our findings reveal the great potential of ML algorithms for enhanced travel behaviour predictions for low-income strata without considerably sacrificing interpretability.
Let $(g_n)_{n\geq 1}$ be a sequence of independent and identically distributed random elements with law $μ$ on the general linear group $\textup{GL}(V)$, where $V=\mathbb R^d$. Consider the random walk $G_n : = g_n \ldots g_1$, $n \geq 1$. Under suitable conditions on $μ$, we establish the first-order Edgeworth expansion for the coefficients $\langle f, G_n v \rangle$ with $v \in V$ and $f \in V^*$, in which a new additional term appears compared to the case of vector norm $\|G_n v\|$.
Let $X$ be either a general hypersurface of degree $n+1$ in $\mathbb P^n$ or a general $(2,n)$ complete intersection in $\mathbb P^{n+1}, n\geq 4$. We construct balanced rational curves on $X$ of all high enough degrees. If $n=3$ or $g=1$, we construct rigid curves of genus $g$ on $X$ of all high enough degrees. As an application we construct some rigid bundles on Calabi-Yau threefolds. In addition, we construct some low-degree balanced rational curves on hypersurfaces of degree $n + 2$ in $\mathbb P^n$.
The interconnectedness and interdependence of modern graphs are growing ever more complex, causing enormous resources for processing, storage, communication, and decision-making of these graphs. In this work, we focus on the task graph sparsification: an edge-reduced graph of a similar structure to the original graph is produced while various user-defined graph metrics are largely preserved. Existing graph sparsification methods are mostly sampling-based, which introduce high computation complexity in general and lack of flexibility for a different reduction objective. We present SparRL, the first generic and effective graph sparsification framework enabled by deep reinforcement learning. SparRL can easily adapt to different reduction goals and promise graph-size-independent complexity. Extensive experiments show that SparRL outperforms all prevailing sparsification methods in producing high-quality sparsified graphs concerning a variety of objectives.
Juliana Ferreira de Lima, Jair Batista de Souza, Alex da Silva Barbosa
A adoção de práticas agroecológicas como consórcio e sistemas agroflorestais mostram-se eficazes na melhoria e manutenção da qualidade dos solos, sendo imprescindível a delimitação de fatores que possam estar relacionados a preservação da fertilidade dos solos. Os indicadores de sustentabilidade de solos participativos caracterizam-se pela sua facilidade na aplicação, podendo serem utilizados pelos próprios agricultores e contribuindo com melhorias nos sistemas de produção da agricultura familiar. Objetivou-se quantificar os níveis de resiliência e diversidade nos diferentes agroecossistemas localizados no município de Serraria, Paraíba. Os agroecossistemas estudados possuíam três consórcios, três monocultivos e três áreas em transição para um sistema agroflorestal. Para o estudo foi utilizado o Diagnóstico Rural Participativo como ferramenta combinada com o método de atribuição de notas aplicados para os indicadores sociais, econômicos e sustentáveis. Em todos os sistemas não são utilizados fertilizantes químicos solúveis industrializados e agrotóxicos em seu manejo, sendo o modelo de produção familiar. A partir das análises pode-se concluir que em áreas pequenas de monocultivo é possível garantir um certo nível de resiliência do agroecossistema, quando trabalhado de maneira em atinência aos princípios do manejo e conservação.
We extend the variational approach to regularity for optimal transport maps initiated by Goldman and the first author to the case of general cost functions. Our main result is an $ε$-regularity result for optimal transport maps between Hölder continuous densities slightly more quantitative than the result by De Philippis-Figalli. One of the new contributions is the use of almost-minimality: if the cost is quantitatively close to the Euclidean cost function, a minimizer for the optimal transport problem with general cost is an almost-minimizer for the one with quadratic cost. This further highlights the connection between our variational approach and De Giorgi's strategy for $ε$-regularity of minimal surfaces.