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Extreme weather-related events are showing how infrastructure disruptions in hinterlands can affect cities. This paper explores the risks to city infrastructure services including transportation, electricity, communication, fuel supply, water distribution, stormwater drainage, and food supply from hinterland hazards of fire, precipitation, post-fire debris flow, smoke, and flooding. There is a large and growing body of research that describes the vulnerabilities of infrastructures to climate hazards, yet this work has not systematically acknowledged the relationships and cross-governance challenges of protecting cities from remote disruptions. An evidence base is developed through a structured literature review that identifies city infrastructure vulnerabilities to hinterland hazards. Findings highlight diverse pathways from the initial hazard to the final impact on an infrastructure, demonstrating that impacts to hinterland infrastructure assets from hazards can cascade to city infrastructure. Beyond the value of describing the impact of hinterland hazards on urban infrastructure, the identified pathways can assist in informing cross-governance mitigation strategies. It may be the case that to protect cities, local governments invest in mitigating hazards in their hinterlands and supply chains.

 

Tuning the topology of two‐dimensional (2D) covalent organic frameworks (COFs) is of paramount scientific interest but remains largely unexplored. Herein, we present a site‐selective synthetic strategy that enables the tuning of 2D COF topology by simply adjusting the molar ratio of an amine‐functionalized dihydrazide monomer (NH₂−Ah) and 4,4′,4′′‐(1,3,5‐triazine‐2,4,6‐triyl)tribenzaldehyde (Tz). This approach resulted in the formation of two distinct COFs: a clover‐like 2D COF with free amine groups (NH₂−Ah−Tz) and a honeycomb‐like COF without amine groups (Ah−Tz). Both COFs exhibited good crystallinity and moderate porosity. Remarkably, the clover‐shaped NH₂−Ah−Tz COF, with abundant free amine groups, displayed significantly enhanced adsorption capacities toward crystal violet (CV, 261 mg/g) and congo red (CR, 1560 mg/g) compared to the non‐functionalized honeycomb‐like Ah−Tz COF (123 mg/g for CV and 1340 mg/g for CR), underscoring the pivotal role of free amine functional groups in enhancing adsorption capacities for organic dyes. This work highlights that the site‐selective synthetic strategy paves a new avenue for manipulating 2D COF topology by adjusting the monomer feeding ratio, thereby modulating their adsorption performances toward organic dyes.

 

To promote a justice‐oriented approach to science education, we formed a research‐practice partnership between middle school science teachers, their students, curriculum designers, learning scientists, and experts in social justice to co‐design and test an environmental justice unit for middle school instruction. We examine teacher perspectives on the challenges and possibilities of integrating social justice into their standards‐aligned science teaching as they participate in co‐design and teach the unit. The unit supports students to investigate racially disparate rates of asthma in their community by examining pollution maps and historical redlining maps. We analyze interviews and co‐design artifacts from two teachers who participated in the co‐design and taught the unit in their classrooms. Our findings point to the benefits of a shared pedagogical framework and an initial unit featuring local historical content to structure co‐design. Findings also reveal that teachers can share similar goals for empowering students to use science knowledge for civic action while framing the local socio‐political factors contributing to the injustice differently, due in part to different institutional supports and constraints. Student interviews and a pre/postassessment illustrate how the unit facilitated students' progress in connecting socio‐political and science ideas to explain the impacts of particulate matter pollution and who is impacted most. Analyses illuminate how teachers' pedagogical choices may influence whether and how students discuss the impact of systemic racism in their explanations. The findings inform refinement of the unit and suggest supports needed for co‐design partnerships focused on integrating social justice and science.

 

The size and complexity reached by the large sky spectroscopic surveys require efficient, accurate, and flexible automated tools for data analysis and science exploitation. We present the Galaxy Spectra Network/GaSNet-II, a supervised multinetwork deep learning tool for spectra classification and redshift prediction. GaSNet-II can be trained to identify a customized number of classes and optimize the redshift predictions. Redshift errors are determined via an ensemble/pseudo-Monte Carlo test obtained by randomizing the weights of the network-of-networks structure. As a demonstration of the capability of GaSNet-II, we use 260k Sloan Digital Sky Survey spectra from Data Release 16, separated into 13 classes including 140k galactic, and 120k extragalactic objects. GaSNet-II achieves 92.4 per cent average classification accuracy over the 13 classes and mean redshift errors of approximately 0.23 per cent for galaxies and 2.1 per cent for quasars. We further train/test the pipeline on a sample of 200k 4MOST (4-metre Multi-Object Spectroscopic Telescope) mock spectra and 21k publicly released DESI (Dark Energy Spectroscopic Instrument) spectra. On 4MOST mock data, we reach 93.4 per cent accuracy in 10-class classification and mean redshift error of 0.55 per cent for galaxies and 0.3 per cent for active galactic nuclei. On DESI data, we reach 96 per cent accuracy in (star/galaxy/quasar only) classification and mean redshift error of 2.8 per cent for galaxies and 4.8 per cent for quasars, despite the small sample size available. GaSNet-II can process ∼40k spectra in less than one minute, on a normal Desktop GPU. This makes the pipeline particularly suitable for real-time analyses and feedback loops for optimization of Stage-IV survey observations.

 

Robots are increasingly being employed for diverse applications where they must work and coexist with humans. The trust in human–robot collaboration (HRC) is a critical aspect of any shared-task performance for both the human and the robot. The study of a human-trusting robot has been investigated by numerous researchers. However, a robot-trusting human, which is also a significant issue in HRC, is seldom explored in the field of robotics. Motivated by this gap, we propose a novel trust-assist framework for human–robot co-carry tasks in this study. This framework allows the robot to determine a trust level for its human co-carry partner. The calculations of this trust level are based on human motions, past interactions between the human–robot pair, and the human’s current performance in the co-carry task. The trust level between the human and the robot is evaluated dynamically throughout the collaborative task, and this allows the trust to change if the human performs false positive actions, which can help the robot avoid making unpredictable movements and causing injury to the human. Additionally, the proposed framework can enable the robot to generate and perform assisting movements to follow human-carrying motions and paces when the human is considered trustworthy in the co-carry task. The results of our experiments suggest that the robot effectively assists the human in real-world collaborative tasks through the proposed trust-assist framework.