Resilient and Safe Platooning Control of Connected Automated Vehicles Against Intermittent Denial-of-Service Attacks

Connected automated vehicles(CAVs)serve as a promising enabler for future intelligent transportation systems because of their capabilities in improving traffic efficiency and driving safety,and reducing fuel consumption and vehicle emissions.A fundamental issue in CAVs is platooning control that empowers a convoy of CAVs to be cooperatively maneuvered with desired longitudinal spacings and identical velocities on roads.This paper addresses the issue of resilient and safe platooning control of CAVs subject to intermittent denial-of-service(DoS)attacks that disrupt vehicle-to-vehicle communications.First,a heterogeneous and uncertain vehicle longitudinal dynamic model is presented to accommodate a variety of uncertainties,including diverse vehicle masses and engine inertial delays,unknown and nonlinear resistance forces,and a dynamic platoon leader.Then,a resilient and safe distributed longitudinal platooning control law is constructed with an aim to preserve simultaneous individual vehicle stability,attack resilience,platoon safety and scalability.Furthermore,a numerically efficient offline design algorithm for determining the desired platoon control law is developed,under which the platoon resilience against DoS attacks can be maximized but the anticipated stability,safety and scalability requirements remain preserved.Finally,extensive numerical experiments are provided to substantiate the efficacy of the proposed platooning method.

Attack-Resilient Distributed Cooperative Control of Virtually Coupled High-Speed Trains via Topology Reconfiguration

Dear Editor,This letter addresses the resilient distributed cooperative control problem of a virtually coupled train convoy under stochastic disturbances and cyber attacks.The main purpose is to achieve distributed coordination of virtually coupled high-speed trains with the prescribed inter-train distance and same cruise velocity.

The Impacts of Climate Change on Fitzroy River Basin, Queensland, Australia

An analysis of historical data of Fitzroy River, which lies in the east coast of Australia, reveals that there is an increasing trend in extreme floods and droughts apparently attributable to increased variability of blue and green waters which could be due to climate change. In order to get a better understanding of the impacts of climate change on the water resources of the study area for near future as well as distant future, SWAT （soil and water assessment tool） model was applied. The model is first tested for its suitability in capturing the basin characteristics with available data, and then, forecasts from six GCMs （general circulation model） with about half-a-century lead time to 2046-2064 and about one-century lead time to 2080-2100 are incorporated to evaluate the impacts of climate change under three marker emission scenarios： A2, A1B and B 1. The results showed worsening water resources regime into the future.

Impact of repeated loading on mechanical response of a reinforced sand

Pavements constructed over loosely compacted subgrades may not possess adequate California bearing ratio (CBR) to meet the requirements of pavement design codes,which may lead to a thicker pavement design for addressing the required strength.Geosynthetics have been proven to be effective for mitigating the adverse mechanical behaviors of weak soils as integrated constituents of base and sub-base layers in road construction.This study investigated the behaviors of unreinforced and reinforced sand with nonwoven geotextile using repeated CBR loading test (followed by unloading and reloading).The depth and number of geotextile reinforcement layers,as well as the compaction ratio of the soil above and below the reinforcement layer(s) and the compaction ratio of the sand bed,were set as variables in this context.Geotextile layers were placed at upper thickness ratios of 0.3,0.6 and 0.9 and the lower thickness ratio of 0.3.The compaction ratios of the upper layer and the sand bed varied between 85% and 97% to simulate a dense layer on a medium dense sand bed for all unreinforced and reinforced testing scenarios.Repeated CBR loading tests were conducted to the target loads of 100 kgf,150 kgf,200 kgf and 400 kgf,respectively (1 kgf=9.8 N).The results indicated that placing one layer of reinforcement with an upper thickness ratio of 0.3 and compacting the soil above the reinforcement to compaction ratio of 97% significantly reduced the penetration of the CBR piston for all target repeated load levels.However,using two layers of reinforcement sandwiched between two dense soil layers with a compaction ratio of 97% with upper and lower thickness ratios of 0.3 resulted in the lowest penetration.

Energy Scenario: Production, Consumption and Prospect of Renewable Energy in Australia

Australia is the world’s 9th largest energy producer, 17th largest consumer of non-renewable energy resources and ranks 18th on a per person energy consumption basis.Australia’s energy consumption is primarily composed of non-renewable energy resources (coal, oil, gas and related products), which represent 96% of total energy consumption. Renewables, the majority of which is bioenergy (wood and wood waste, biomass, and biogas) combined with clear energy namely wind, solar hot water, solar electricity, hydroelectricity account for the remaining 4% consumption.Australia’s renewable energy resources are largely undeveloped which will contribute directly to the Australian economy. In this article, a review of literature on energy scenario is presented and discussed.Australia’s total energy production, consumption, storage and export (including renewable and non-renewable) data has been analyzed and discussed in this study. The main objective of the study is to analyze the prospect of renewable energy inAustralia. This study concludes that Australian economy will grow faster if its undeveloped renewable energies can be used efficiently for electricity generation and transport sector.

Decentralized content sharing in mobile ad-hoc networks:A survey

The evolution of smart mobile devices has significantly impacted the way we generate and share contents and introduced a huge volume of Internet traffic.To address this issue and take advantage of the short-range communication capabilities of smart mobile devices,the decentralized content sharing approach has emerged as a suitable and promising alternative.Decentralized content sharing uses a peer-to-peer network among colocated smart mobile device users to fulfil content requests.Several articles have been published to date to address its different aspects including group management,interest extraction,message forwarding,participation incentive,and content replication.This survey paper summarizes and critically analyzes recent advancements in decentralized content sharing and highlights potential research issues that need further consideration.

Impacts of Climate Change on Water Resources in Diyala River Basin, Iraq

Diyala River is the third largest tributary of the Tigris River running 445 km length and draining an area of 32,600 km2. The river is the major source of water supply for Diyala City for municipal, domestic, agriculture and other purposes. Diyala River Basin currently is suffering from water scarcity and contamination problems. Up-to-date studies have shown that blue and green waters of a basin have been demonstrating increasing variability contributing to more severe droughts and floods seemingly due to climate change. To obtain better understanding of the impacts of climate change on water resources in Diyala River Basin in near 2046-2064 and distant future 2080~2100, SWAT （soil and water assessment tool） was used. The model is first examined for its capability of capturing the basin characteristics, and then, projections from six GCMs （general circulation models） are incorporated to assess the impacts of climate change on water resources under three emission scenarios： A2, AIB and B1. The results showed deteriorating water resources regime into the future.

Climate Change Impacts on Water Resources of Greater Zab River, Iraq

Greater Zab is the largest tributary of the Tigris River in lraq where the catchment area is currently being plagued by water scarcity and pollution problems. Contemporary studies have revealed that blue and green waters of the basin have been manifesting increasing variability contributing to more severe droughts and floods apparently due to climate change. In order to gain greater appreciation of the impacts of climate change on water resources in the study area in near and distant future, SWAT （Soil and Water Assessment Tool） has been used. The model is first tested for its suitability in capturing the basin characteristics, and then, forecasts from six GCMs （general circulation models） with about half-a-century lead time to 2046-2064 and one-century lead time to 2080-2100 are incorporated to evaluate the impacts of climate change on water resources under three emission scenarios： A 1 B, A2 and BI. The results showed worsening water resources regime into the future.

Discussion on "Effects of lime addition on geotechnical properties of sedimentary soil in Curitiba, Brazil" [J Rock Mech Geotech Eng 10(2018)188-194]

The present discussion aims at complementing the original work published by Baldovino et al.(2018) by outlining a novel point of view. In light of the inherent limitations associated with the empirical model suggested in the original article, the dimensional analysis technique was introduced to the soil-lime strength problem, thereby leading to the development of simple and physically meaningful dimensional models capable of predicting the unconfined compressive and splitting tensile strengths of compacted soil-lime mixtures as a function of the mixture's index properties, i.e. lime content, initial placement(or compaction) condition, initial specific surface area and curing time. The predictive capacity of the proposed dimensional models was examined and validated by statistical techniques. The proposed dimensional models contain a limited number of fitting parameters, which can be calibrated by minimal experimental effort and hence implemented for predictive purposes.

Performance Evaluation of Hybrid Green Roof System in a Subtropical Climate Using Fluent

Energy disaster is one of the major obstacles in the progress of human society. There are some on-going researches to overcome this for a sustainable environment. Green roof system is one of them which assist to reduce energy consumption of the buildings. The green roof system for a building involves a green roof that is partially or completely covered with vegetation and plant over a waterproofing membrane. Green roofs provide shade and remove heat from the air through evapotranspiration, reducing temperatures of the roof surface and the surrounding air. This paper reports the thermal performance of hybrid green roof system for a hot and humid subtropical climatic zone in Queensland, Australia. A thermal model is developed for the green roof system using ANSYS Fluent. Data were collected from two modelled rooms, one connected with green roof system and other non-green roof system. The rooms were built from two shipping containers and?installed at Central Queensland University, Rockhampton, Australia. Impact of air temperature on room cooling performance is assessed in this study. A temperature reduction of 0.95°C was observed in the room with green roof which will save energy cost in buildings. Only 1.7% variation in temperature was found in numerical result in comparison with experimental result.

Decentralizing Volunteer Computing Coordination

This paper attempted to decentralize volunteer computing (VC) coordination with the goal of reducing the reliance on a central coordination server, which had been criticized for performance bottleneck and single point of failure. On analyzing the roles and functions that the VC components played for the centralized master/worker coordination model, this paper proposed a decentralized VC coordination framework based on distributed hash table (DHT) and peerto-peer (P2P) overlay and then successfully mapped the centralized VC coordination into distributed VC coordination. The proposed framework has been implemented on the performance-proven DHT P2P overlay Chord. The initial verification has demonstrated the effectiveness of the framework when working in distributed environments.

The Scalability of Volunteer Computing for MapReduce Big Data Applications

Volunteer Computing(VC)has been successfully applied to many compute-intensive scientific projects to solve embarrassingly parallel computing problems.There exist some efforts in the current literature to apply VC to data-intensive(i.e.big data)applications,but none of them has confirmed the scalability of VC for the applications in the opportunistic volunteer environments.This paper chooses MapReduce as a typical computing paradigm in coping with big data processing in distributed environments and models it on DHT(Distributed Hash Table)P2P overlay to bring this computing paradigm into VC environments.The modelling results in a distributed prototype implementation and a simulator.The experimental evaluation of this paper has confirmed that the scalability of VC for the MapReduce big data(up to 10 TB)applications in the cases,where the number of volunteers is fairly large(up to 10K),they commit high churn rates(up to 90%),and they have heterogeneous compute capacities(the fastest is 6 times of the slowest)and bandwidths(the fastest is up to 75 times of the slowest).

A review of monitoring systems of pavement condition in paved and unpaved roads

The rising number of vehicles on roadways expedites the urge to increase efforts in implementing monitoring systems that look after road pavement conditions. This rising in number of vehicles on roadways also cause more damages and distresses on road pavement. Road pavement conditions should be accurately evaluated to identify the severity of pavement damages and types of pavement distress. Therefore, monitoring systems are considered a significant step of maintenance processes. Paved roads and unpaved roads require regular maintenance to provide for and preserve users' usability, accessibility, and safety. Transport agents and researches would spend a lot of time and money in inspecting some sections of the roadway surface;that inspection would then be followed by results recording and data analysis to diagnose the type of treatment required. These monitoring systems have been developed using various methods that include smart technologies and prepared equipment. Many related studies evaluate road pavement degradation and distress, while others focus on identifying the best maintenance monitoring approach in terms of time and cost. This paper set out to explore different monitoring techniques used to evaluate road pavement surface condition. Also, this study introduces dynamic and static monitoring systems used in both paved and unpaved roads to identify the severity of pavement degradations and types of pavement distress on road surfaces and also this study explains the used equipment in the previous monitoring studies.

Smart Grid for a Sustainable Future

Advances in micro-electro-mechanical systems (MEMS) and information communication technology (ICT) have facilitated the development of integrated electrical power systems for the future. A recent major issue is the need for a healthy and sustainable power transmission and distribution system that is smart, reliable and climate-friendly. Therefore, at the start of the 21st Century, Government, utilities and research communities are working jointly to develop an intelligent grid system, which is now known as a smart grid. Smart grid will provide highly consistent and reliable services, efficient energy management practices, smart metering integration, automation and precision decision support systems and self healing facilities. Smart grid will also bring benefits of seamless integration of renewable energy sources to the power networks. This paper focuses on the benefits and probable deployment issues of smart grid technology for a sustainable future both nationally and internationally. This paper also investigates the ongoing major research programs in Europe, America and Australia for smart grid and the associated enabling technologies. Finally, this study explores the prospects and characteristics of renewable energy sources with possible deployment integration issues to develop a clean energy smart grid technology for an intelligent power system.

An Interpretable Artificial Intelligence Based Smart Agriculture System

With increasing world population the demand of food production has increased exponentially.Internet of Things(IoT)based smart agriculture system can play a vital role in optimising crop yield by managing crop requirements in real-time.Interpretability can be an important factor to make such systems trusted and easily adopted by farmers.In this paper,we propose a novel artificial intelligence-based agriculture system that uses IoT data to monitor the environment and alerts farmers to take the required actions for maintaining ideal conditions for crop production.The strength of the proposed system is in its interpretability which makes it easy for farmers to understand,trust and use it.The use of fuzzy logic makes the system customisable in terms of types/number of sensors,type of crop,and adaptable for any soil types and weather conditions.The proposed system can identify anomalous data due to security breaches or hardware malfunction using machine learning algorithms.To ensure the viability of the system we have conducted thorough research related to agricultural factors such as soil type,soil moisture,soil temperature,plant life cycle,irrigation requirement and water application timing for Maize as our target crop.The experimental results show that our proposed system is interpretable,can detect anomalous data,and triggers actions accurately based on crop requirements.

Spatial Decision Support System for Coastal Zone Management under a Changing Climate in Victoria, Australia

The ultimate aim of using spatial datasets and spatial data modelling is focused on enabling a sustainable environment by bringing the public policies into practice. The consequence will be sustainable spatially aware strategic planning for all levels of Australian government. Geographical Information Systems (GIS) are the platform that can serve this aim provided that model, current process and spatial datasets are fit for purpose. To bring public policy into practice a broad range of knowledge from different disciplines is needed. Most decision making processes are pressured in terms of time and driving forces and also the process is beyond the knowledge of individuals in the various disciplines. There is a need for immediate uptake models and tools which are relevant to the target subject that will facilitate this decision making process. This paper focuses on realizing the utility in spatial data and spatial data handling in order to help climate change adaptation programs at local government level. Web-based mapping tools can assist planners prepare for the changing climate conditions in Bass Coast Shire Council. The GIS team has gathered data from various climate research organizations to understand projections of what different climate scenarios might look like over the next 100-year period. From this website demo it is hoped that the user will understand how the tool works, background information on different GIS platforms, access to interactive mapping, online geospatial analysis tools, videos, open source resource, sea level tools, modelling, 3D visualization and direct download access to various planning and natural resource data sets relating to environment management. Some results from our elevation data analyses through these Web map visualization tools are provided.

Spreadsheet quality assurance:a literature review

Spreadsheets are very common for information processing to support decision making by both professional developers and non-technical end users.Moreover,business intelligence and artificial intelligence are increasingly popular in the industry nowadays,where spreadsheets have been used as,or integrated into,intelligent or expert systems in various application domains.However,it has been repeatedly reported that faults often exist in operational spreadsheets,which could severely compromise the quality of conclusions and decisions based on the spreadsheets.With a view to systematically examining this problem via survey of existing work,we have conducted a comprehensive literature review on the quality issues and related techniques of spreadsheets over a 35.5-year period(from January 1987 to June 2022)for target journals and a 10.5-year period(from January 2012 to June 2022)for target conferences.Among other findings,two major ones are:(a)Spreadsheet quality is best addressed throughout the whole spreadsheet life cycle,rather than just focusing on a few specific stages of the life cycle.(b)Relatively more studies focus on spreadsheet testing and debugging(related to fault detection and removal)when compared with spreadsheet specification,modeling,and design(related to development).As prevention is better than cure,more research should be performed on the early stages of the spreadsheet life cycle.Enlightened by our comprehensive review,we have identified the major research gaps as well as highlighted key research directions for future work in the area.

Enhancing Telemarketing Success Using Ensemble-Based Online Machine Learning

Telemarketing is a well-established marketing approach to offering products and services to prospective customers.The effectiveness of such an approach,however,is highly dependent on the selection of the appropriate consumer base,as reaching uninterested customers will induce annoyance and consume costly enterprise resources in vain while missing interested ones.The introduction of business intelligence and machine learning models can positively influence the decision-making process by predicting the potential customer base,and the existing literature in this direction shows promising results.However,the selection of influential features and the construction of effective learning models for improved performance remain a challenge.Furthermore,from the modelling perspective,the class imbalance nature of the training data,where samples with unsuccessful outcomes highly outnumber successful ones,further compounds the problem by creating biased and inaccurate models.Additionally,customer preferences are likely to change over time due to various reasons,and/or a fresh group of customers may be targeted for a new product or service,necessitating model retraining which is not addressed at all in existing works.A major challenge in model retraining is maintaining a balance between stability(retaining older knowledge)and plasticity(being receptive to new information).To address the above issues,this paper proposes an ensemble machine learning model with feature selection and oversampling techniques to identify potential customers more accurately.A novel online learning method is proposed for model retraining when new samples are available over time.This newly introduced method equips the proposed approach to deal with dynamic data,leading to improved readiness of the proposed model for practical adoption,and is a highly useful addition to the literature.Extensive experiments with real-world data show that the proposed approach achieves excellent results in all cases(e.g.,98.6%accuracy in classifying customers)and outperforms recent competing models in the literature by a considerable margin of 3%on a widely used dataset.

Load forecasting for diurnal management of community battery systems

This paper compares three methods of load forecasting for the optimum management of community battery storages. These are distributed within the low voltage(LV) distribution network for voltage management,energy arbitrage or peak load reduction. The methods compared include: a neural network(NN) based prediction scheme that utilizes the load history and the current metrological conditions; a wavelet neural network(WNN)model which aims to separate the low and high frequency components of the consumer load and an artificial neural network and fuzzy inference system(ANFIS) approach.The batteries have limited capacity and have a significant operational cost. The load forecasts are used within a receding horizon optimization system that determines the state of charge(SOC) profile for a battery that minimizes a cost function based on energy supply and battery wear costs. Within the optimization system, the SOC daily profile is represented by a compact vector of Fourier series coefficients. The study is based upon data recorded within the Perth Solar City high penetration photovoltaic(PV)field trials. The trial studied 77 consumers with 29 rooftop solar systems that were connected in one LV network. Data were available from consumer smart meters and a data logger connected to the LV network supply transformer.