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Explainable deep learning-enabled malware attack detection for IoT-enabled intelligent transportation systems
Get PDFThe Internet of Things (IoT) has the potential to improve the complementary of communication, control, and information processing within the public transportation system. The IoT-enabled Intelligent Transportation System (ITS) ensures that automated transportation is networked and operated collaboratively. The IoT-enabled ITS has revolutionized the transportation industry by enabling the seamless integration of a wide range of devices and systems. It makes the strategic use of networked devices, sensors, and data analytics to improve transportation network efficiency, safety, and environmental friendliness. The usage of the IoT in the ITS has grown in popularity due to its capacity to improve traffic control, reduce congestion, facilitate live monitoring, and optimize transportation operations. The IoT-enabled ITS systems and devices must be protected from cyber-attacks for various reasons, including preserving sensitive data, guaranteeing privacy, preventing unauthorized access, and protecting against the risk of interruptions or manipulations. Malware attacks affect the working and performance of the deployed smart IoT devices. We propose a secure deep learning- enabled malware attack detection for IoT-enabled ITS (in short, SDLMA-IITS). The approach of explainable artificial intelligence (XAI) has been utilized for the effective detection of malware. A deep security analysis of the proposed SDLMA-IITS is presented to prove its security against various potential attacks. The comparative performance analysis of SDLMA-IITS is given with the other similar existing schemes. Finally, a practical implementation of SDLMA-IITS is provided to measure its impact on the security of the IoT-enabled ITS systems and devices
Semantic communications for healthcare applications: opportunities and challenges
Get PDFIn this paper, we introduce the healthcare system where Semantic Communication (SC) technology is applied to improve the quality of service for healthcare and medical applications. We first show the concepts and possible architecture of SC. Then, we show different types of SC in the healthcare system. Next, some examples of SC-enhanced healthcare applications are discussed. Finally, we give research challenges and future research directions
Virtual integration of satellite and in-situ observation networks (VISION) v1.0: in-Situ observations simulator (ISO_simulator)
Get PDFThis work presents the first step in the development of the VISION toolkit, a set of Python tools that allows easy, efficient, and more meaningful comparison between global atmospheric models and observational data. Whilst observational data and modelling capabilities are expanding in parallel, there are still barriers preventing these two data sources from being used in synergy. This arises from differences in spatial and temporal sampling between models and observational platforms: observational data from a research aircraft, for example, are sampled on specified flight trajectories at very high temporal resolution. Proper comparison with model data requires generating, storing, and handling a large number of highly temporally resolved model files, resulting in a process which is data-, labour-, and time-intensive. In this paper we focus on comparison between model data and in situ observations (from aircraft, ships, buoys, sondes, etc.). A standalone code, In-Situ Observations Simulator, or ISO_simulator for short, is described here: this software reads modelled variables and observational data files and outputs model data interpolated in space and time to match observations. These model data are then written to NetCDF files that can be efficiently archived due to their small sizes and directly compared to observations. This method achieves a large reduction in the size of model data being produced for comparison with flight and other in situ data. By interpolating global gridded hourly files onto observation locations, we reduce data output for a typical climate resolution run, from ∼3 Gb per model variable per month to ∼15 Mb per model variable per month (a 200-times reduction in data volume). The VISION toolkit is relatively fast to run and can be automated to process large volumes of data at once, allowing efficient data analysis over a large number of years. Although this code was initially tested within the Unified Model (UM) framework, which is shared by the UK Earth System Model (UKESM), it was written as a flexible tool and it can be extended to work with other models
Integrating multiple evidence streams to understand insect biodiversity change
Get PDFInsects dominate animal species diversity yet face many threats from anthropogenic drivers of change. Many features of insect ecology make them a challenging group, and the fragmented state of knowledge compromises our ability to make general statements about their status. Here, we discuss the challenges of assessing insect biodiversity change. We describe how multiple lines of evidence – time series, spatial comparisons, experiments, and expert opinion – can be integrated to provide a synthesis overview of how insect biodiversity responds to drivers. Applying this approach will generate testable predictions of insect biodiversity across space, time, and changing drivers. Given the urgency of accelerating human impacts across the environment, this approach could yield a much-needed rapid assessment of insect biodiversity change
Global carbon budget 2024
Get PDFAccurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere in a changing climate is critical to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe and synthesise datasets and methodologies to quantify the five major components of the global carbon budget and their uncertainties. Fossil CO2 emissions (EFOS) are based on energy statistics and cement production data, while emissions from land-use change (ELUC) are based on land-use and land-use change data and bookkeeping models. Atmospheric CO2 concentration is measured directly, and its growth rate (GATM) is computed from the annual changes in concentration. The global net uptake of CO2 by the ocean (SOCEAN, called the ocean sink) is estimated with global ocean biogeochemistry models and observation-based fCO2-products. The global net uptake of CO2 by the land (SLAND, called the land sink) is estimated with dynamic global vegetation models. Additional lines of evidence on land and ocean sinks are provided by atmospheric inversions, atmospheric oxygen measurements and Earth System Models. The sum of all sources and sinks results in the carbon budget imbalance (BIM), a measure of imperfect data and incomplete understanding of the contemporary carbon cycle. All uncertainties are reported as ±1σ.
For the year 2023, EFOS increased by 1.3% relative to 2022, with fossil emissions at 10.1 ± 0.5 GtC yr-1 (10.3 ±0.5 GtC yr-1 when the cement carbonation sink is not included), ELUC was 1.0 ± 0.7 GtC yr-1, for a total anthropogenic CO2 emission (including the cement carbonation sink) of 11.1 ± 0.9 GtC yr-1 (40.6 ± 3.2 GtCO2 yr-1). Also, for 2023, GATM was 5.9 ± 0.2 GtC yr-1 (2.79 ± 0.1 ppm yr-1), SOCEAN was 2.9 ± 0.4 GtC yr-1 and SLAND was 2.3 ± 1.0 GtC yr-1, with a near zero BIM (-0.02 GtC yr-1). The global atmospheric CO2 concentration averaged over 2023 reached 419.31 ± 0.1 ppm. Preliminary data for 2024, suggest an increase in EFOS relative to 2023 of +0.8% (-0.2% to 1.7%) globally, and atmospheric CO2 concentration increased by 2.87 ppm reaching 422.45 ppm, 52% above pre-industrial level (around 278 ppm in 1750). Overall, the mean and trend in the components of the global carbon budget are consistently estimated over the period 1959-2023, with a near-zero overall budget imbalance, although discrepancies of up to around 1 GtC yr-1 persist for the representation of annual to semi-decadal variability in CO2 fluxes. Comparison of estimates from multiple approaches and observations shows: (1) a persistent large uncertainty in the estimate of land-use change emissions, (2) a low agreement between the different methods on the magnitude of the land CO2 flux in the northern extra-tropics, and (3) a discrepancy between the different methods on the mean ocean sink.
This living data update documents changes in methods and datasets applied to this most-recent global carbon budget as well as evolving community understanding of the global carbon cycle. The data presented in this work are available at https://doi.org/10.18160/GCP-2024 (Friedlingstein et al., 2024)
Increasingly seasonal jet stream raises risk of co‐occurring flooding and extreme wind in Great Britain
Get PDFInsurers and risk managers for critical infrastructure such as transport or power networks typically do not account for flooding and extreme winds happening at the same time in their quantitative risk assessments. We explore this potentially critical underestimation of risk from these co‐occurring hazards through studying events using the regional 12 km resolution UK Climate Projections for a 1981–1999 baseline and projections of 2061–2079 (RCP8.5). We create a new wintertime (October–March) set of 3427 wind events to match an existing set of fluvial flow extremes and design innovative multi‐event episodes (Δt of 1–180 days long) that reflect how periods of adverse weather affect society (e.g., through damage). We show that the probability of co‐occurring wind‐flow episodes in Great Britain (GB) is underestimated 2–4 times if events are assumed independent. Significantly, this underestimation is greater both as severity increases and episode length reduces, highlighting the importance of considering risk from closely consecutive storms (Δt ~ 3 days) and the most severe storms. In the future (2061–2079), joint wind‐flow extremes are twice as likely as during 1981–1999. Statistical modelling demonstrates that changes may significantly exceed thermodynamic expectations of higher river flows in a wetter future climate. The largest co‐occurrence increases happen in mid‐winter (DJF) with changes in the North Atlantic jet stream an important driver; we find the jet is strengthened and squeezed into a southward‐shifted latitude window (45°–50° N) giving typical future conditions that match instances of high flows and joint extremes impacting GB today. This strongly implies that the large‐scale driving conditions (e.g., jet stream state) for a multi‐impact ‘perfect storm’ will vary by country; understanding regional drivers of weather hazards over climate timescales is vital to inform risk mitigation and planning (e.g., diversification and mutual aid across Europe)
Multi-objective optimization of supersonic separator for gas removal and carbon capture using three-field two-phase flow model and non-dominated sorting Genetic Algorithm-II (NSGA-II)
Get PDFSupersonic separator is an efficient technology for gas removal and carbon capture. To enhance its performance, many researchers have studied its structure; however, existing studies have primarily used traditional computational fluid dynamics (CFD) models for single-objective structural optimization of the separator’s separation performance. However, in the supersonic separators, separation efficiency and pressure-loss ratio are the most important and conflicting performance parameters, and evaluating separation performance in isolation from either one is incomplete. In the present study, we develop a gas–liquid two-phase three-field CFD model considering liquid films. This mathematical model is combined with the non-dominated Sorting Genetic Algorithm-II (NSGA-II) for multi-objective optimization of the coupled multiple structural parameters with the objective of the separation efficiency and pressure-loss ratio. The results indicate that the maximum relative errors between simulated and predicted values for the four Pareto optimal solutions in computing pressure loss ratio and separation efficiency are 5.4% and 5.3%, respectively. The optimized solutions achieve the maximum reduction in pressure loss ratio of 28.3% at the same 90% separation efficiency compared to the original structure