RMIT University

Urban air pollution poses a significant challenge in India’s major cities, including Delhi, Noida, and Gurgaon, exacerbated by rapid urbanization and industrialization. Stubble burning, a prominent contributor to air pollution, adds to the woes of the National Capital Territory. This process accounts for a substantial portion of global biomass-burning incidents, with India alone burning 14 million metric tonnes of stubble annually. Other factors, such as coal plants, vehicular emissions, and deforestation, exacerbate the issue. Indirect causes, including historical agricultural practices and inadequate government policies, compound the problem. The resultant primary pollutants degrade air quality and disrupt the climate system by releasing harmful greenhouse gases and adversely affecting soil productivity and carbon–nitrogen equilibrium. Health impacts are severe, with Delhi residents experiencing a significant reduction in life expectancy due to air pollution-related ailments such as asthma and acute respiratory infections. The economic toll is also substantial, with air pollution’s cumulative impact rising to 7.7% of GDP in 2018. Nature-based solutions, such as urban forestry and green spaces, offer a promising avenue for combating air pollution in densely populated cities like Delhi. These interventions may support urban agriculture, mitigate urban heat islands, and reduce soil erosion, noise pollution, and energy consumption. The Delhi government has initiated several measures to increase urban greenery, including biodiversity parks and green ambulance services. However, more concerted efforts and policy interventions are needed to effectively address the multifaceted challenge of urban air pollution.

Green taxes hold significant potential as effective tools for financing crucial initiatives, promoting sustainable urban design, and addressing the challenges of urbanization and climate change. This chapter explores citizen engagement in green taxation decision-making, from levying taxes to mitigate pollution impacts to allocating funds for citizen-centric urban forestry management. It emphasizes the role of taxation policies in shaping public behavior and awareness regarding environmental conservation and the adverse effects of pollution on forests and biodiversity. The chapter examines successful global green taxation initiatives, such as Amsterdam’s green restoration project, Sweden’s NOx emission tax, and France’s forest tax, illustrating how environmental incentives can positively impact the environment. It underscores the need for moderation and a balanced approach between environmental and economic factors to implement circular economy and green taxation policies effectively. The economic evaluation of urban green infrastructure is discussed, highlighting methods like Discounted Cash Flow analysis and LiDAR remote sensing to measure ecosystem services and understand the financial benefits of urban greenery. The chapter highlights global successes in urban forestry through citizen participation and green taxation. Additionally, it focuses on the status of urban forestry in India, policies to enhance citizen engagement in green taxation, and the need to address social and economic concerns to improve participation in climate mitigation measures. It also examines reasons for past failures in mitigating climate change, such as decreased funding and continued investment in fossil fuels, which hinder effective policy implementation. The chapter argues for the importance of public participation in green taxation measures, underscoring its critical role in fostering sustainable urban environments and successful climate mitigation efforts.

Supercapacitors (SCs) have emerged as promising candidates for efficient and sustainable energy storage devices due to their unique merits, including high power density and long lifespan. However, despite these advantages, SCs face significant challenges related to their relatively low energy density. Current collectors are critical components of SCs, which significantly impacts the efficiency and overall performance by connecting active materials and external devices. However, the reviews on SCs are predominantly focused on electrode active materials or electrolyte materials, with insufficient comprehensive summaries regarding current collectors. This review focuses on the research progress related to current collectors in SCs. Firstly, the article outlines the modification objectives mechanism and inherent nature of SC current collectors. Building on this foundation, the authors further classify the current collector materials towards metallic, carbon‐based, polymers and other ones and highlights their modification strategies. Finally, the future development trends and challenges of SCs current collectors are comprehensively discussed.

Monolayer 2D transition metal dichalcogenides (TMDs) are known for their direct bandgaps and pronounced excitonic effects, which facilitate efficient light absorption and high photoluminescence (PL). In this study, we report...

Although AI technologies reportedly can address accessibility issues and the risks have been documented, debates around AI have left developing countries and people with disabilities (PwDs) behind. Despite the global marketization of AI technologies, the understanding of AI and disability in developing countries in the Global South remains scant. Through semi-structured interviews with key personnel of disabled people organizations in Indonesia and Vietnam, this study found that a pocket of the disabled viewed AI as formidable but foreign because of the persistent information void within the disabled community. AI potentially magnifies the existing bias against the disabled, but their unique features and lived experiences are irreplaceable by AI. The findings seek attention from developers, activists, and policy makers in emerging markets as the benefits of AI have reached wider audiences but PwDs and the risks of AI–human interactions to them have been narrowly discussed in Southeast Asia (SEA).

Headway, namely the distance between vehicles, is a key design factor for ensuring the safe operation of autonomous driving systems. There have been studies on headway optimization based on the speeds of leading and trailing vehicles, assuming perfect sensing capabilities. In practical scenarios, however, sensing errors are inevitable, calling for a more robust headway design to mitigate the risk of collision. Undoubtedly, augmenting the safety distance would reduce traffic throughput, highlighting the need for headway design to incorporate both sensing errors and risk tolerance models. In addition, prioritizing group safety over individual safety is often deemed unacceptable because no driver should sacrifice their safety for the safety of others. In this study, we propose a multi-objective optimization framework that examines the impact of sensing errors on both traffic throughput and the fairness of safety among vehicles. The proposed framework provides a solution to determine the Pareto frontier for traffic throughput and vehicle safety. ComDrive, a communication-based autonomous driving simulation platform, is developed to validate the proposed approach. Extensive experiments demonstrate that the proposed approach outperforms existing baselines.

This study introduces two novel approaches to enhance the energy efficiency of satellite-to-ground communication systems by using multiple Reconfigurable Intelligent Surfaces (RISs) on terrestrial platforms. We explore scenarios in both ideal environments (IE), which assume no shadowing loss, and non-ideal environments (NIE), which consider practical environmental influences such as shadowing. In the IE scenario, energy efficiency is optimized through a dual strategy: first, by maximizing power reception through precise phase shift adjustments of each RIS element; second, by employing Selective Diversity combined with Binary Particle Swarm Optimization (BPSO) to minimize power consumption. This theoretical benchmark in the IE scenario sets the stage for the NIE scenario, where the Adaptive Moment Estimation (Adam) optimization algorithm is employed to systematically enhance energy efficiency under realworld operational conditions. Simulation results demonstrate the significant potential of the proposed methods for energy savings in satellite-RIS systems.

The integration of artificial intelligence has become a hot topic in the field of education. However, current studies primarily focus on personalization for learners, with the aim of accurately modeling learners’ knowledge level based on their learning history and providing better personalized services, while overlooking the needs of educators. In contrast to the focus on personalization of learners, educators place greater emphasis on accurately assessing collective performance and relative differences within a group, which serves as a qualitative measure of the teaching quality. In this study, we investigate collective knowledge tracing (CeKT), a method designed to estimate the average knowledge level of all students in a course based on a sequence of exercises. To achieve this objective, we propose a graph-based solution capable of estimating the average knowledge level solely from the exercise sequence as well as capturing the intrinsic structure of the exercise sequence (i.e., the sequential order of exercises and the repetition of exercises). Through experimental validation, we affirm that our approach enables a precise estimation of the average knowledge mastery of all students given an exercise sequence and also holds distinct value in three applications within the education domain.

Accurately forecasting long-term future wind power is critical to achieve safe power grid integration. This problem is quite challenging due to wind power's high volatility and randomness. In this paper, we propose a novel time series forecasting method, namely Deep Conditional Generative Spatio-Temporal model (DCGST), and its high accuracy is achieved by tackling two critical issues simultaneously: a proper handling of the non-stationarity of multiple wind power time series, and a fine-grained modeling of their complicated yet dynamic spatio-temporal dependencies. Specifically, we first formally define the Spatio-Temporal Concept Drift (STCD) problem of wind power, and then we propose a novel deep conditional generative model to learn probabilistic distributions of future wind power values under STCD. Three different tailored neural networks are designed for distributions parameterization, including a graph-based prior network, an attention-based recognition network, and a stochastic seq2seq-based generation network. They are able to encode the dynamic spatio-temporal dependencies of multiple wind power time series and infer one-to-many mappings for future wind power generation. Compared to existing methods, DCGST can learn better spatio-temporal representations of wind power data and learn better uncertainties of data distribution to generate future values. Comprehensive experiments on real-world datasets including the largest public turbine-level wind power dataset verify the effectiveness, efficiency, generality and scalability of our method. Compared with state-of-the-art methods, DCGST can reduce the lone-term wind power prediction error by up to 22.8% and improve the online prediction efficiency by up to 76.8%.

Wireless power transfer (WPT) systems have received more and more attention in undersea applications in recent years. The seawater between the transmitter and the receiver is a highly-conductive medium, which can be regarded as an unknown topology that will introduce eddy current loss and unknown dynamic behavior during the power transfer process. Hence, the dynamic modeling and control design methods for WPT systems in air may not apply. This paper proposes an observer-free model predictive control (MPC) strategy for WPT systems in seawater environment. Considering the unknown topology introduced by the seawater medium, the control system is designed using a data-driven dynamic model obtained by simplified refined instrumental variable (SRIV) method. In a further step, the SRIV-based dynamic model is converted to a special state-space model by choosing a set of state variables corresponding to the input and output variables. Thus, the state observer design is avoided. Besides, operational constraints are imposed into the MPC algorithm to guarantee that the control input is implemented in an appropriate range. Experiments are performed to demonstrate that the proposed MPC system has superior performance in both reference tracking and parametric robustness in comparison to a proportional-integral (PI) control system.

Edge computing (EC) enables low-latency services by pushing computing resources to the network edge. Due to the geographic distribution and limited capacities of edge servers, EC systems face the challenge of edge distributed denial-of-service (DDoS) attacks. Existing systems designed to fight cloud DDoS attacks cannot mitigate edge DDoS attacks effectively due to new attack characteristics. In addition, those systems are typically activated upon detected attacks, which is not always realistic in EC systems. DDoS mitigation needs to be cohesively integrated with workload migration at the edge to ensure timely responses to edge DDoS attacks. In this paper, we present EdgeShield, a novel DDoS mitigation system that leverages edge servers' computing resources collectively to defend against edge DDoS attacks without the need for attack detection. Aiming to maximize system throughput over time without causing significant service delays, EdgeShield monitors service delays and migrates workloads across an EC system with adaptive mitigation strategies. The experimental results show that EdgeShield significantly outperforms state-of-the-art solutions in both system throughput and service delays.

Federated Learning (FL) achieves great popularity in the Internet of Things (IoT) as a powerful interface to offer intelligent services to customers while maintaining data privacy. Under the orchestration of a server, edge devices (also called clients in FL) collaboratively train a global deep-learning model without sharing any local data. Nevertheless, the unequal training contributions among clients have made FL vulnerable, as clients with heavily biased datasets can easily compromise FL by sending malicious or heavily biased parameter updates. Furthermore, the resource shortage issue of the network also becomes a bottleneck. Due to overwhelming computation overheads generated by training deep-learning models on edge devices, and significant communication overheads for transmitting deep-learning models across the network, enormous amounts of resources are consumed in the FL process. This encompasses computation resources like energy and communication resources like bandwidth. To comprehensively address these challenges, in this paper, we present FLrce, an efficient FL framework with a r elationship-based c lient selection and e arly-stopping strategy. FLrce accelerates the FL process by selecting clients with more significant effects, enabling the global model to converge to a high accuracy in fewer rounds. FLrce also leverages an early stopping mechanism that terminates FL in advance to save communication and computation resources. Experiment results show that, compared with existing efficient FL frameworks, FLrce improves the computation and communication efficiency by at least 30% and 43% respectively.

Autonomous vehicles (AVs) have a significant impact on the expansion of greenhouse gas emissions as well as driving safety. Consequently, ensuring safety while improving the energy efficiency of AVs has gained increasing importance. In this study, we offer an optimal intelligent system (OIS) by applying a multi-objective evolutionary optimization algorithm to an integrated control system, including an Adaptive Cruise Control (ACC) and an Intelligent Energy Management System (IEMS) that augments safety and lessens the energy consumption for Conventional AVs. In this -system, a predictive model is developed by defining the desired acceleration of the ego vehicle. The vehicle then follows a longitudinal path to track the lead vehicle on the same highway lane, ensuring a safe following distance while minimizing tracking errors. Subsequently, an Intelligent Energy Management System (IEMS) is introduced to optimize the torque output of the internal combustion engine, aimed at reducing the energy consumption of the ego vehicle. Additionally, a sensitivity analysis of the ego vehicle is conducted to account for disturbances and signal loss scenarios. In this way, a band-limited white noise is considered for road power demand (RPD) and measuring signal of lead vehicle velocity, simultaneously. Moreover, two different scenarios are designed regarding signal-losing circumstances and interruptions in receiving the signal of lead vehicle velocity. The optimal solutions reveal a strong independence between safety and fuel consumption, showing that their performances significantly affect each other. The optimal solutions reveal a strong interdependence between safety and fuel consumption, showing that their performances significantly affect each other. The results demonstrate that the optimal approach can significantly reduce fuel consumption while maintaining safety and effective collision avoidance performances.

With the expansive deployment of ground base stations, low Earth orbit (LEO) satellites, and aerial platforms such as unmanned aerial vehicles (UAVs) and high altitude platforms (HAPs), the concept of space-air-ground integrated network (SAGIN) has emerged as a promising architecture for future 6G wireless systems. In general, SAGIN aims to amalgamate terrestrial nodes, aerial platforms, and satellites to enhance global coverage and ensure seamless connectivity. Moreover, beyond mere communication functionality, computing capability is increasingly recognized as a critical attribute of sixth generation (6G) networks. To address this, integrated communication and computing have recently been advocated as a viable approach. Additionally, to overcome the technical challenges of complicated systems such as high mobility, unbalanced payloads, limited resources, and various demands in communication and computing among different network segments, various solutions have been introduced recently. Consequently, this paper offers a comprehensive survey of the technological advances in communication and computing within SAGIN for 6G, including the system architecture, network characteristics, general communication, and computing technologies. Subsequently, we summarize the pivotal technologies of SAGIN-enabled 6G, including the physical layer, medium access control (MAC) layer, and network layer. Finally, we explore the technical challenges and future trends in this field.

Auxetic honeycomb sandwich structures (AHS) composed of a single material generally exhibit comparatively lower energy absorption (EA) and platform stress, as compared to traditional non-auxetic sandwich structures (TNS). To address this limitation, the present study examines the use of aluminum foam (AF) as a filling material in the re-entrant honeycomb sandwich structure (RS). Filling the AHS with AF greatly enhances both the EA and platform stress in comparison to filling the TNS with AF, while the auxetic composite honeycomb sandwich structure effectively addresses interface delamination observed in traditional non-auxetic composite sandwich structures. Subsequently, the positive–negative Poisson’s ratio coupling designs are proposed to strengthen the mechanical features of a single honeycomb sandwich structure. The analysis results show that the coupling structure optimizes the mechanical properties by leveraging the high bearing capacity of the hexagonal honeycomb and the great interaction between the re-entrant honeycomb and the filling material. In contrast with traditional non-auxetic sandwich structures, the proposed auxetic composite honeycomb sandwich structures demonstrate superior EA and platform stress performance, suggesting their immense potential for utilization in protective engineering.

Aligned with global goals for healthy and sustainable cities, urban compactness significantly promotes residents’ transport-related physical activity and health. In Japan, amid demographic changes, municipalities have begun formulating Location Normalization Plans focused on making cities more compact. This study examines the associations between health and transport-related physical activity considerations in urban plans, including Location Normalization Plans, the demographic characteristics of municipalities, and the development of infrastructure that supports leisure-time physical activity. Analyzing responses from 725 of 1,374 Japanese municipalities, our research reveals that 38% of urban plans integrated health and physical activity in goals/visions, and 28% recognized these as challenges. The development of physical activity infrastructure such as sports facilities (48%), metropolitan parks (21%), and bicycle paths (14%) was more prevalent in larger municipalities. However, the incorporation of health and physical activity in planning goals/visions or challenges was not exclusive to larger municipalities; smaller ones also demonstrated this capability. Logistic regression analyses revealed that municipalities that included health considerations in their plans’ goals/visions and challenges were more likely to develop or renovate physical activity infrastructure. Prioritizing health and physical activity in planning has the potential to support leisure-time and transport-related physical activity, helping to create healthy and sustainable cities.