The implications of COVID-19 for urban planning and governance are wide ranging and have triggered a rethinking of how policies related to housing, transportation, sustainability, climate change and governance might be redeveloped to be better suited for post-pandemic cities. Over the past five years, both academic and popular accounts have highlighted commonalities between adaptations that cities are making to become more resilient to infectious disease outbreaks like COVID-19, while also addressing sustainable development and climate change indicators and goals. Much of this literature has suggested that a shift towards greater implementation of urban blue and green space - along with other interventions, such as encouraging compact city design, reducing sprawl and encouraging active transit modes - can contribute to healthier and socio-environmentally friendly cities, while also reducing carbon emissions and achieving urban climate goals. However, this article argues that there have been significant barriers faced in doing so that have hampered their effectiveness and implementation. These include the entrenched ideologies favouring 'grey' over 'green' infrastructure; political-economic and financial constraints; reactive forms of urban and climate governance; and what we call the temporal politics of urban climate governance vis-& agrave;-vis infectious disease responses. We suggest how these barriers can be potentially overcome through more engaged forms of grassroots planning and governance in cities which can implement more rapid and place-specific responses. Our review is largely based on scholarship that has emerged in the wake of the COVID-19 pandemic in the fields of urban and environmental studies. (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic),(sic)(sic),(sic)(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).(sic)(sic)(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic), (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).(sic)(sic)(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).(sic)(sic),(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).(sic)(sic)(sic)(sic)(sic)(sic):(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)"(sic)(sic)"(sic)(sic)"(sic)(sic)"(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic);(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic);(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic);(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)((sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).
The dramatic increase in IoT devices in a smart ecosystem like smart cities, transportation systems, and healthcare and industrial automation has greatly improved network connectivity and data-driven informed decisions. But this extraordinary level of connectivity generates important concerns associated with sensitive information and security risks. Therefore, this study proposes a novel framework for secure and sustainable IoT network and devices through a combination of a Hybrid Federated Learning Framework and GenAI. The proposed framework focuses on extending a secure learning platform for all different IoT devices through a Federated Learning Framework and utilizing GenAI capabilities for advanced information augmentation and customized anomaly detection. To improve the level of guaranteed privacy, this framework will utilize differential privacy techniques and a blockchain-assisted model validation process. Moreover, techniques for energy-efficient model optimization and edge intelligence in making decisions are considered to improve sustainability. The proposed work will examine and develop this novel hybrid model through intensive simulations and lab-based testing for its application in a building and energy management field. The impact will include a new federative generative architecture that offers enhanced cyber threat resilience, lower overhead costs of communication, and ensures user confidentiality of data. The end goal of this proposed project is to contribute positively towards advancing the state-of-the-art in sustainable AI for a secure and environment-conscious IoT.
Robust robot navigation in outdoor environments requires accurate perception systems capable of handling visual challenges such as repetitive structures and changing appearances. Visual feature matching is crucial to vision-based pipelines but remains particularly challenging in natural outdoor settings due to perceptual aliasing. We address this issue in vineyards, where repetitive vine trunks and other natural elements generate ambiguous descriptors that hinder reliable feature matching. We hypothesise that semantic information tied to keypoint positions can alleviate perceptual aliasing by enhancing keypoint descriptor distinctiveness. To this end, we introduce a keypoint semantic integration technique that improves the descriptors in semantically meaningful regions within the image, enabling more accurate differentiation even among visually similar local features. We validate this approach in two vineyard perception tasks: (i) relative pose estimation and (ii) visual localisation. Our method improves matching accuracy across all tested keypoint types and descriptors, demonstrating its effectiveness over multiple months in challenging vineyard conditions.
This study used a combination of analytical techniques - chemical fractionation, XANES and 31P NMR spectroscopies - to investigate and quantify the effects of different long-term tillage practices [conventional tillage (CT) with plowing at 20 cm-depth and harrowing, and no-tillage (NT) systems] and phosphate fertilizer inputs (triple superphosphate-TSP and Gafsa reactive phosphate rock-RPR applied in the sowing furrow or broadcast) on the nature and bioavailability of phosphorus (P) in the topsoil (0-20 cm) of a strongly weathered Oxisol in Brazil. The soil was sampled after 17 years of phosphate application (35 kg ha-1 yr-1) and after eight years of maize (Zea mays L.) cropping without P input. Results showed that soil legacy P accumulated from P inputs was mainly present as non-labile inorganic P adsorbed on iron (P-Fe) and aluminum (P-Al) oxides (average of 79 %). Accumulation of soil legacy P in labile pool was greater under NT (65 mg kg- 1) compared with CT (32 mg kg- 1), while organic P as monoester and diester was also greater in soil maintained under NT than CT (81 vs 61 mg kg-1). Maize grown after cessation of P inputs mobilized and depleted both labile (average of -8 mg kg-1) and non-labile (average of -47 mg kg-1) pools of legacy P. Calcium-bound P acted as labile reserve of P, especially when RPR was applied under NT, while P-Fe and P-Al were less available to maize, increasing their proportion under P suppression. Long-term tillage and phosphate fertilization strategies influence the bioavailability of legacy P in tropical soil, and conservation systems enhance the potential for crops to access and utilize the legacy P reserves.
Navigating within narrow spaces is a fundamental challenge in robotics, requiring precise localisation, localisation error recovery, dynamic path planning, and adaptive control for effective manoeuvring. This paper presents a modular and perception-driven navigation framework designed for constrained environments, focusing primarily on agricultural applications. The proposed method integrates a multi-step point cloud processing pipeline for robust local perception, including pole detection, boundary line estimation, and trajectory refinement to ensure safe and precise traversal by refining initial trajectories based on detected environmental constraints and dynamically adapting to kinematic limitations. Experimental validation in a real strawberry polytunnel demonstrates superior trajectory accuracy and control stability compared to state-of-the-art navigators, achieving an average lateral deviation of 0.08 ± 0.01 m. The adaptive trajectory tracking and regulated pure pursuit control of the framework contribute to consistent navigation, even under increased velocity constraints, outperforming the resilient timed elastic band (RTEB) and model predictive path integral (MPPI) methods. This modular and generalisable framework offers significant potential for advancing autonomous navigation in narrow-space applications.
