Identifying local governance capacity needs for implementing climate change adaptation in Mauritius

UIDB/04647/2020 UIDP/04647/2020The IPCC 1.5°C Report lists identifying local capacity needs as key for enabling multi-level governance to effectively respond to climate change. Mauritius, as a Small Island State, is disproportionately affected by climate change, primarily due to its exposure to impacts, as well as various constraints in size and resources. Identifying and integrating local capacity needs into recommendations for policy measures is therefore urgently required to support the United Nations Framework Convention on Climate Change and its National Adaptation Plan process. This study carries out a local governance assessment based on evaluative criteria to identify local capacity needs for implementing climate change adaptation in Mauritius. Results from the assessment indicate that local governance suffers from issues inherent to Small Island States, such as lack of technical know-how, financial and human resources, stringent legislation and effective monitoring mechanisms, preventing effective climate change adaptation. Through participatory, bottom-up stakeholder engagement with local and national government representatives, eight recommendations for policy formulation were then co-developed to address the identified capacity needs, and to improve cooperation between local and national institutions for more effective implementation of climate change adaptation. Key policy insights Local climate change adaptation needs have to be integrated into policy formulation for an effective response to climate change impacts. Roles and responsibilities of government levels for climate change adaptation in Mauritius are currently in need of clarification. Capacity building measures at the local level for implementing climate change actions from national government are urgently required. Stringent legislation and effective monitoring mechanisms need to be implemented to ensure planning regulations are adhered to. Increased collaboration between local and national levels of government in Mauritius is necessary for synthesizing a common approach to climate change adaptation.publishersversionpublishe

The 'last mile' for climate data supporting local adaptation

UIDB/04647/2020 UIDP/04647/2020Non-technical summary The 'last mile' is a transportation planning term that describes the movement of people and goods from a transportation hub to a final destination; a local place such as a home or a shop. This is the final step of the logistics process that unites the product with its new owner. We present and explain challenges of science-guided adaptation at the local level, and how this is an equivalent 'last mile' challenge for climate adaptation. Technical summary The 'last mile' issue, a term used in transportation planning, describes the movement of people and goods from a transportation hub to a final destination, a local place such as a home or a shop. This is the critical final step of the logistics process that unites the product with its new owner, and the point of the value chain. This analogy aptly describes the last steps between presenting scientific evidence of climate change to decision-makers for use in local adaptation and planning. Climate change data (observational and model simulation data e.g. climate change projections and predictions) remain under-utilised, especially by local institutions and actors for which adaptation is a priority. The assumptions and assertions of the classical data-information-knowledge-wisdom are challenged, and a derivative form of the information hierarchy is proposed. Elements of the classical information hierarchy are offset by four balancing elements of access (to data); usability (of information); governance (of knowledge) and politics (of wisdom). These balancing elements and their relatedness coincide with newer models of innovation relating to the interaction between different stakeholders across the different levels of governance, the inclusion of stakeholder expectations, transparency and accountability. Social media summary Climate data to wise decision-making in the 'last mile': a novel perspective on science-guided local adaptation.publishersversionpublishe

Efficient Graph Reconstruction and Representation Using Augmented Persistence Diagrams

Persistent homology is a tool that can be employed to summarize the shape of data by quantifying homological features. When the data is an object in $\mathbb{R}^d$, the (augmented) persistent homology transform ((A)PHT) is a family of persistence diagrams, parameterized by directions in the ambient space. A recent advance in understanding the PHT used the framework of reconstruction in order to find finite a set of directions to faithfully represent the shape, a result that is of both theoretical and practical interest. In this paper, we improve upon this result and present an improved algorithm for graph -- and, more generally one-skeleton -- reconstruction. The improvement comes in reconstructing the edges, where we use a radial binary (multi-)search. The binary search employed takes advantage of the fact that the edges can be ordered radially with respect to a reference plane, a feature unique to graphs.Comment: This work originally appeared in the 2022 proceedings of the Canadian Conference on Computational Geometry (CCCG). We have updated the proof of Theorem 2 in Appendix A for clarity and correctness. We have also corrected and clarified Section 3.2, as previously, it used slightly stricter general position assumptions than those given in Assumption

A Faithful Discretization of the Augmented Persistent Homology Transform

The persistent homology transform (PHT) represents a shape with a multiset of persistence diagrams parameterized by the sphere of directions in the ambient space. In this work, we describe a finite set of diagrams that discretize the PHT such that it faithfully represents the underlying shape. We provide a discretization that is exponential in the dimension of the shape (making it Furthermore, we provide an output-sensitive algorithm; that is, the algorithm reports the discretization in time proportional to the size of the discretization. Finally, our approach relies only on knowing the heights and dimensions of topological events, meaning that it can be adapted to provide discretizations of other dimension-returning topological transforms, including the Betti curve transform

Canonical Generations and the British Left: The Narrative Construction of the Miners’ Strike 1984–85

‘Generations’ have been invoked to describe a variety of social and cultural relationships, and to understand the development of self-conscious group identity. Equally, the term can be an applied label and politically useful construct; generations can be retrospectively produced. Drawing on the concept of ‘canonical generations’ – those whose experiences come to epitomise an event of historic and symbolic importance – this article examines the narrative creation and functions of ‘generations’ as collective memory shapes and re-shapes the desire for social change. Building a case study of the canonical role of the miners’ strike of 1984–85 in the narrative history of the British left, it examines the selective appropriation and transmission of the past in the development of political consciousness. It foregrounds the autobiographical narratives of activists who, in examining and legitimising their own actions and prospects, (re)produce a ‘generation’ in order to create a relatable and useful historical understanding

Session availability as a result of prior injury impacts the risk of subsequent injury in elite male Australian footballers

Prior injury is a commonly identified risk factor for subsequent injury. However, a binary approach to classifying prior injury (i.e., yes/no) is commonly implemented and may constrain scientific findings, as it is possible that variations in the amount of time lost due to an injury will impact subsequent injury risk to differing degrees. Accordingly, this study investigated whether session availability, a surrogate marker of prior injury, influenced the risk of subsequent non-contact lower limb injury in Australian footballers. Data were collected from 62 male elite Australian footballers throughout the 2015, 2016, and 2017 Australian Football League seasons. Each athlete’s participation status (i.e., full or missed/modified) and any injuries that occurred during training sessions/matches were recorded. As the focus of the current study was prior injury, any training sessions/matches that were missed due to reasons other than an injury (e.g., load management, illness and personal reasons) were removed from the data prior to all analyses. For every Monday during the in-season periods, session availability (%) in the prior 7, 14, 21, 28, 35, 42, 49, 56, 63, 70, 77, and 84 days was determined as the number of training sessions/matches fully completed (injury free) relative to the number of training sessions/matches possible in each window. Each variable was modeled using logistic regression to determine its impact on subsequent injury risk. Throughout the study period, 173 non-contact lower limb injuries that resulted in at least one missed/modified training session or match during the in-season periods occurred. Greater availability in the prior 7 days increased injury probabilities by up to 4.4%. The impact of session availability on subsequent injury risk diminished with expanding windows (i.e., availability in the prior 14 days through to the prior 84 days). Lesser availability in the prior 84 days increased injury probabilities by up to 14.1%, only when coupled with greater availability in the prior 7 days. Session availability may provide an informative marker of the impact of prior injury on subsequent injury risk and can be used by coaches and clinicians to guide the progression of training, particularly for athletes that are returning from long periods of injury

The discerning eye of computer vision: can it measure Parkinson's finger tap bradykinesia?

Objective: The worldwide prevalence of Parkinson's disease is increasing. There is urgent need for new tools to objectively measure the condition. Existing methods to record the cardinal motor feature of the condition, bradykinesia, using wearable sensors or smartphone apps have not reached large-scale, routine use. We evaluate new computer vision (artificial intelligence) technology, DeepLabCut, as a contactless method to quantify measures related to Parkinson's bradykinesia from smartphone videos of finger tapping. Methods: Standard smartphone video recordings of 133 hands performing finger tapping (39 idiopathic Parkinson's patients and 30 controls) were tracked on a frame-by-frame basis with DeepLabCut. Objective computer measures of tapping speed, amplitude and rhythm were correlated with clinical ratings made by 22 movement disorder neurologists using the Modified Bradykinesia Rating Scale (MBRS) and Movement Disorder Society revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS). Results: DeepLabCut reliably tracked and measured finger tapping in standard smartphone video. Computer measures correlated well with clinical ratings of bradykinesia (Spearman coefficients): -0.74 speed, 0.66 amplitude, -0.65 rhythm for MBRS; -0.56 speed, 0.61 amplitude, -0.50 rhythm for MDS-UPDRS; -0.69 combined for MDS-UPDRS. All p Conclusion: New computer vision software, DeepLabCut, can quantify three measures related to Parkinson's bradykinesia from smartphone videos of finger tapping. Objective 'contactless' measures of standard clinical examinations were not previously possible with wearable sensors (accelerometers, gyroscopes, infrared markers). DeepLabCut requires only conventional video recording of clinical examination and is entirely 'contactless'. This next generation technology holds potential for Parkinson's and other neurological disorders with altered movements