Aviation-induced contrails significantly contribute to climate change, yet current strategies for their real-time prediction and mitigation remain limited. This paper presents AI approaches for a contrail prediction framework that integrates meteorological data and Automatic Dependent Surveillance Broadcast (ADS-B) flight trajectories. We use meteorological and ADS-B flight data to identify contrail-prone areas, which are a foundation for model training and simulations. The Schmidt Appleman Criterion (SAC) model are applied to predict these areas and devise a strategy for detecting contrails. The models successfully identify contrails-prone zones and recommend feasible flight paths that optimise environmental and operational parameters while complying with the research framework. The proposed framework offers a promising avenue for mitigating the environmental impact of contrails in aviation, focusing on the United Kingdom (UK) airspace with potential adaptation for other regions to support global sustainable aviation practices. Keywords: Contrail Detection, Sustainable Aviation, Flight Trajectory Analysis

The International Maritime Organization (IMO) enforced stricter sulphur abatement regulations since shipping emission has become one of the most major cause of the atmospheric pollution. Experts from the industry and academicians try to find the balanced solution among low-sulphur fuel, clean energy, and purposely fit scrubber by conventional statistical methods however failed to reach a satisfying conclusion. In addition, maritime datasets are usually massive, multi-source, and heterogeneous, it seems imperative for the maritime industry to adapt to the worldwide trend of intellectualisation and promote sustainable development.This work delineates and compares three main sulphur abatement solutions for ships through a thorough investigation of the current research state, and proposes a new framework based on a fusion model using modern big data and data mining algorithms. This work identifies and summarises major factors (with high impacts) in sulphur abatement solutions in the ocean shipping industry and integrate those high-level impacting factors to the proposed fusion model. The proposed framework can be optimised and utilised in determining suitable solutions for different ships, as well as shipping routes.

Distributed denial of service (DDoS) attacks pose a huge threat to the Internet. Follow the rapid usage of the Internet of things (IoT), the DDoS attack is no longer a mere traffic attack, the original attack on the application layer surpasses the attack on the network layer. Furthermore, DDoS attacks using Bonnets result more destructive effects. This research aims to propose a new collaborated active defense framework between Honeypot and cloud platform to detect and defend future DDoS attacks in the context of the IoT with the instantaneous malicious traffic measured in Terabytes.

This paper considers multiple network attacks in a Mobile Ad hoc Network (MANET). MANET is a type of network that does not require any infrastructure for nodes to communicate with each other. The general assumption in MANET is that each node is a trusted node. However, due to its lack of infrastructure and dynamic topology, the MANET is exposed to network layer attacks. In this paper, some of the network layer attacks such as the black hole attack, worm hole attack and gray hole attack are discussed. Frequent multiple network attacks are highlighted, and the identification of collaborative network attacks is discussed. There are several symptoms and observations in a network that signal the presence of an attack and some of the symptoms of these attacks will be highlighted. Finally, an analysis of existing multiple network attacks will be discussed, and the identification of prospective multiple network attacks is considered.

This discussion concerns 'metrics'. More specifically, we discuss quantitative metrics for evaluating Internet applications: what should we quantify, monitor and analyse in order to characterise, evaluate and develop Internet applications based on reusing existing Internet applications,which is widely available in the Internet. Due to the distinctive evolution nature of Internet applications, assessing quality of software will provide ease and higher accuracy for Web developers. However, there is a great gap between the rapid development of Internet applications and the slow speed of developing corresponding metric measures. To tackle this issue, we look into measuring the quality of Internet applications and enable Web developers to enhance the quality of their programs and identify reusable components from Internet-based resources.

Business processes and goals change rapidly due to the turbulent environment in which they operate. Their supporting and underpinning technologies may, as a result, require to change. Meanwhile, technological advances are being made at an increasing rate. This may trigger changes in business goals and processes to exploit these new advances; a notable example is e-commerce and e-learning. Understanding and analyzing the interplay and the dual effect between these two entities, technologies and business, is vital both for the prosperity of business and the success and further development of the technologies themselves. This paper proposes the K-Mediator framework together with its underpinning theory to facilitate the co-evolution between businesses and their supporting technologies.

If software metrics are useful in a forward software engineering environment, they are vital in a reverse engineering environment. We are endeavouring to discover approaches to developing reverse engineering metrics for software engineers who need them for reverse engineering legacy systems. The major contribution of the paper is the presentation of a systematic research base and a hierarchical approach to the development of software metrics for reverse engineering. Measurement is fundamental to the software engineering discipline as a whole. A software metric is a quantitative measure of the degree to which a system, component, or process possesses a given attribute (N.E. Fenton and S.L. Pfleeger, 1996). Software reverse engineering is the process of analysing a subject system to: identify the system's components and their interrelationships; and create representations of the system at a higher level of abstraction (E.J. Chikofsky and J.H. Cross II, 1990). The goal of developing reverse engineering metrics is to identify measures that are needed for assessing the status of reverse engineering projects, products, processes and resources, and helping engineers to understand both what is happening and what will happen during reverse engineering procedures. Consequently, these concrete reverse engineering measures make more visible to us aspects of process and product in reverse engineering, in particular, the product of specifications and the process of abstractions and transformations. This makes it possible to control reverse engineering projects. Aspects of measurement in reverse engineering are outlined.

We propose a sound and practical approach, based on a formal method (known as interval temporal logic), to cope with 'change' and analyse its effect. The approach allows lows to capture a snapshot of system's behaviour over which various interesting properties, such as liveness, timeliness and safety properties, can be validated compositionally. These properties may include invariants that are required to be valid after changes have taken place. We also present and evaluate the design and implementation of a formal tool, AnaTempura, which supports the developed approach. A case study is presented to illustrate our approach and the tool.