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Purpose of review This review aims to identify the key factors, methods, and spatial units used in the development and validation of the heat vulnerability index (HVI) and discuss the underlying limitations of the data and methods by evaluating the performance of the HVI. Recent findings Thirteen studies characterizing the factors of the HVI development and relating the index with validation data were identified. Five types of factors (i.e., hazard exposure, demographic characteristics, socioeconomic conditions, built environment, and underlying health) of the HVI development were identified, and the top five were social cohesion, race, and/or ethnicity, landscape, age, and economic status. The principal component analysis/factor analysis (PCA/FA) was often used in index development, and four types of spatial units (i.e., census tracts, administrative area, postal code, grid) were used for establishing the relationship between factors and the HVI. Moreover, although most studies showed that a higher HVI was often associated with the increase in health risk, the strength of the relationship was weak. Summary This review provides a retrospect of the major factors, methods, and spatial units used in development and validation of the HVI and helps to define the framework for future studies. In the future, more information on the hazard exposure, underlying health, governance, and protection awareness should be considered in the HVI development, and the duration and location of validation data should be strengthened to verify the reliability of HVI.

Semantic segmentation is a popular research topic in computer vision, and many efforts have been made on it with impressive results. In this paper, we intend to search an optimal network structure that can run in real-time for this problem. Towards this goal, we jointly search the depth, channel, dilation rate and feature spatial resolution, which results in a search space consisting of about 2.78×10324 possible choices. To handle such a large search space, we leverage differential architecture search methods. However, the architecture parameters searched using existing differential methods need to be discretized, which causes the discretization gap between the architecture parameters found by the differential methods and their discretized version as the final solution for the architecture search. Hence, we relieve the problem of discretization gap from the innovative perspective of solution space regularization. Specifically, a novel Solution Space Regularization (SSR) loss is first proposed to effectively encourage the supernet to converge to its discrete one. Then, a new Hierarchical and Progressive Solution Space Shrinking method is presented to further achieve high efficiency of searching. In addition, we theoretically show that the optimization of SSR loss is equivalent to the L0-norm regularization, which accounts for the improved search-evaluation gap. Comprehensive experiments show that the proposed search scheme can efficiently find an optimal network structure that yields an extremely fast speed (175 FPS) of segmentation with a small model size (1 M) while maintaining comparable accuracy.

Background The solution space of an FBA-based model of cellular metabolism, can be characterised by extraction of a bounded, low dimensional kernel (the SSK) that facilitates perceiving it as a geometric object in multidimensional flux space. The aim is to produce an amenable description, intermediate between the single feasible extreme flux of FBA, and the intractable proliferation of extreme modes in conventional solution space descriptions. Fluxes that remain fixed are separated off while the focus of interest is put on the subset of variable fluxes that have a nonzero but finite range of values. For unbounded fluxes, a finite subrange that geometrically corresponds to the variable flux range is determined and is supplemented by a limited set of rays that encapsulates their unbounded aspects. In this way the kernel emphasises the realistic range of flux variation allowed in the interconnected biochemical network by e.g. limited nutrient uptake, an optimised objective and other model constraints. This work builds on the full presentation of the kernel approach in a research monograph. Methods Calculations are performed with the publicly available software package SSKernel, the source code and user manual of which is included as a supplementary file. Results It is demonstrated how knowledge of the SSK and accompanying rays can be exploited to explore representative flux states of the metabolic network. Noting that bioengineering interventions such as gene knockouts modify the solution space, new tools based on the kernel analysis are presented here that predict the effects of such interventions on a target flux constructed to represent a desired metabolic output. A simple metabolic model is used first to demonstrate the special concepts and constructions needed to define and compute the SSK. The demonstration model is tweaked to produce typical behaviours of larger models, but with kernels in 1, 2 or 3 dimensions that are explicitly displayed to visualise the concepts. General applicability to models where visualisation is inaccessible, is illustrated by showing evaluation of potential bioengineering strategies for a genome scale model. Conclusions SSKernel is a flexible interactive tool that facilitates an overview of the FBA solution space as a multidimensional geometric object, in terms of a manageable number of parameters. It allows exploration of effects on this solution space from metabolic interventions and can be used to investigate bioengineering strategies to manipulate cellular metabolism.

Purpose of Review South Asia is highly vulnerable to the impacts of climate change, owing to the high dependency on climate-sensitive livelihoods and recurrent extreme events. Consequently, an increasing number of households are adopting labour migration as a livelihood strategy to diversify incomes, spread risks, and meet aspirations. Under the Collaborative Adaptation Research Initiative in Africa and Asia (CARIAA) initiative, four research consortia have investigated migration patterns and their inherent linkages to adaptation to climate change in climate hotspots. This article synthesizes key findings in regional context of South Asia. Recent Findings The synthesis suggests that in climate-sensitive hotspots, migration is an important livelihood diversification strategy and a response to various risks, including climate change. Typically, one or more household members, often young men, migrated internally or internationally to work in predominantly informal sectors. Remittances helped spatially diversify household income, spread risks, and insure against external stressors. The outcomes of migration are often influenced by who moves, where to, and what capacities they possess. Summary Migration was found to help improve household adaptive capacity, albeit in a limited capacity. Migration was mainly used as a response to risk and uncertainty, but with potential to have positive adaptation co-benefits.

Designing strongly interacting vehicle components in the early development phase is challenging because numerous requirements, uncertainties, and conflicting objectives significantly limit feasible design solutions. Achieving optimal commonality is particularly complex when a single component must satisfy the requirements of multiple systems. Solution space engineering is an effective method for identifying robust common solutions and has been successfully applied to components with linear properties. However, its applicability is limited for components with non-linear properties, as their properties vary with the operating point. Consequently, evaluating component commonality across systems cannot rely solely on functional properties, since these are operating-point-dependent and system-specific. Both boundary conditions and quantities of interest differ between systems and must be considered to avoid unnecessary restriction of the solution space during development. This paper presents an extension of solution space engineering for developing common components with non-linear properties, explicitly accounting for differing system requirements at identical operating points. An enhanced layering technique is introduced that establishes commonality at the level of component design variables. The proposed approach is demonstrated through the design of rear axle subframe mounts.

Purpose of Review Managed retreat will be inevitable where other adaptation options, such as protective structures or building restrictions, provide only temporary respite or are otherwise uneconomic, technically impractical or both. Here, we focus on the implementation of pre-emptive managed retreat, providing examples of how it can be sequenced, socialised and given the governance enablers necessary for implementation. Recent Findings Ongoing sea-level rise during the twenty-first century and beyond poses huge adaptation challenges, especially for low-lying coastal and floodplain settlements. Settlements are already functionally disrupted from repetitive non-extreme flooding and research shows that sea-level rise will impact far more people, far sooner than previously thought, as more powerful storms, heavy rainfall and rising groundwater coincide with higher tides. To date, most examples of managed retreat have been post-disaster responses following damage and disruption. Pre-emptive managed retreat, by contrast, has yet to become a well-accepted and widely practised adaptation response. Nevertheless, there are increasing examples of research and practice on how pre-emptive managed retreat can be designed, sequenced and implemented alongside other forms of adaptation within anticipatory forms of governance . Summary The current state of knowledge about managed retreat is reviewed and critical insights and lessons for governance and policy-making are given. Several novel examples from New Zealand are presented to address some of the implementation gaps. Goals and principles are enunciated to inform long-term adaptation strategies.

Purpose of Review This paper discusses three scientific frontiers that need to be advanced in order to support decision-makers and practitioners in charge of operational decisions and action on the design and implementation of concrete adaptation policies and actions. These frontiers refer to going beyond the (1) incremental vs. transformational and (2) maladaptation vs. adaptation dichotomies and to advancing knowledge on (3) adaptation measures’ effectiveness and roles in designing context-specific adaptation pathways. Recent Findings Dealing with adaptation to climate change on the ground often means answering three obvious but critical questions: what to do, where and when? These questions challenge the scientific community’s capacity to link conceptual advances (e.g. on transformative adaptation) and ground-rooted needs across sectors and regions (on solutions, governance arrangements, etc.). Summary We argue that the three abovementioned frontiers represent the most burning challenges to the Adaptation Science community to help addressing climate-related societal needs. We also demonstrate that they are intertwined as moving one frontier forward will facilitate moving the others forward.

Design solution space (DSS) exploration is a pivotal process for comprehending design challenges and identifying diverse solution alternatives based on varying requirements. Computer-aided design (CAD) approaches, such as parametric design, knowledge-based design, and generative design, have proven successful in DSS exploration. However, a comparative study evaluating their performance is lacking in the technical literature. This paper addresses this gap by conducting a comparative analysis of these approaches regarding their performance in exploring DSS. The research begins by providing an overview of parametric design, knowledge-based design, and generative design, establishing the foundation for the study. Six evaluation criteria are identified based on the DSS exploration process. A qualitative analysis is then conducted, considering these criteria, to objectively assess the performance of each modeling approach. The results highlight the strengths and weaknesses of each approach, revealing that DSS exploration success is directly tied to the quantity of implemented knowledge. The results also emphasize the complementarity of those approaches, as their strengths and weaknesses are based on different problem-solving logics, demonstrating the synergy that can be achieved through strategic combinations of them. Additionally, the paper discusses open issues related to DSS exploration, contributing valuable insights for future developments in this field.

Solution spaces are regions of good designs in a potentially high-dimensional design space. Good designs satisfy by definition all requirements that are imposed on them as mathematical constraints. In previous work, the complete solution space was approximated by a hyper-rectangle, i.e., the Cartesian product of permissible intervals for design variables. These intervals serve as independent target regions for distributed and separated design work. For a better approximation, i.e., a larger resulting solution space, this article proposes to compute the Cartesian product of two-dimensional regions, so-called 2d-spaces, that are enclosed by polygons. 2d-spaces serve as target regions for pairs of variables and are independent of other 2d-spaces. A numerical algorithm for non-linear problems is presented that is based on iterative Monte Carlo sampling.

Purpose of Review This review (1) describes the intersecting literature on climate change adaptation (CCA) and uncertainty ( N = 562), and (2) synthesizes the findings of empirical studies about decision-maker uncertainty ( n = 97). Recent Findings Uncertainty can be a barrier to adaptation, yet it is most often studied in relation to the scientific process, while uncertainties in people’s decision-making and their impact on CCA are less studied. Summary Despite the predominance of scientific uncertainties (52%), we see an upward-trend in studies of decision-making uncertainty (24%), and in combining natural and social sciences approaches (24%). Multiple sources of uncertainty influence CCA decisions besides climate trends, and their saliency and people’s responses vary depending on the role/function of the decision-maker and the timeframe of the decision. Concerns involve situational uncertainties, response options, and their consequences. Decision-makers are more likely to incorporate uncertainties in their adaptation decisions than suppress them or delay action, although the response is sensitive to the type of information sought and timeframes.