The provision of the heterogeneous information acquisition and managing of emerging technologies with IoT, cloud-based storage, and improved communication services have filled the data scarcity gap on one hand but raised the challenge to extract, process, and comprehend relevant data of complex integrated multiple domains involving a large number of participants with diverse spatial terminologies and methodologies. To resolve this challenge various big data and natural language processing techniques were applied. Another widely used approach to resolve the challenges of heterogeneity, interoperability, and complexity of integrated domain is ontology-based semantic modeling. We proposed Ontology for River Flow and Flood Mitigation (ORFFM) for semantic knowledge formalization with semantic understandability of irrigation, disaster management, related administrative and agricultural domain concepts by humans and machines. The semantic modeling of distributed river flow network and associated flood disaster mitigation for effective coordination, collaborative response activities leads to reduce the impact of a disaster and improve information representation among stakeholders. Furthermore, semantic formalization and inference are supported by explicitly annotated information. We populated ORFFM with Pakistan's Indus river system, flood disaster management, and Sindh administrative authorities to develop a knowledgebase for knowledge sharing and representation. The formal semantically enriched knowledgebase would contribute towards streamflow optimization and flood mitigation through effective coordination and common conceptualization during disaster management phases. The semantic model of irrigation networks would also be useful for academic purposes to acquire domain knowledge for new entrants in the irrigation and disaster management field.

Network forensics can be an expansion associated with network security design which typically emphasizes avoidance and detection of community assaults. It covers the necessity for dedicated investigative abilities. When you look at the design, this indeed currently allows investigating harmful behavior in communities. It will help organizations to examine external and community this is undoubtedly around. It is also important for police force investigations. Network forensic techniques can be used to identify the source of the intrusion and the intruder’s location. Forensics can resolve many cybercrime cases using the methods of network forensics. These methods can extract intruder’s information, the nature of the intrusion, and how it can be prevented in the future. These techniques can also be used to avoid attacks in near future. Modern network forensic techniques face several challenges that must be resolved to improve the forensic methods. Some of the key challenges include high storage speed, the requirement of ample storage space, data integrity, data privacy, access to IP address, and location of data extraction. The details concerning these challenges are provided with potential solutions to these challenges. In general, the network forensic tools and techniques cannot be improved without addressing these challenges of the forensic network. This paper proposed a thematic taxonomy of classifications of network forensic techniques based on extensive. The classification has been carried out based on the target datasets and implementation techniques while performing forensic investigations. For this purpose, qualitative methods have been used to develop thematic taxonomy. The distinct objectives of this study include accessibility to the network infrastructure and artifacts and collection of evidence against the intruder using network forensic techniques to communicate the information related to network attacks with minimum false-negative results. It will help organizations to investigate external and internal causes of network security attacks.

Distribution networks are inherently radial and passive owing to the ease of operation and unidirectional power flow. Proper installation of Distributed Generators, on the one hand, makes the utility network active and mitigates certain power quality issues e.g., voltage dips, frequency deviations, losses, etc., but on the other hand, it disturbs the optimal coordination among existing protection devices e.g., over-current relays. In order to maintain the desired selectivity level, such that the primary and backup relays are synchronized against different contingencies, it necessitates design of intelligent and promising protection schemes to distinguish between the upstream and downstream power flows. This research proposes exploiting phase angle jump, an overlooked voltage sag parameter, to add directional element to digital over-current relays with inverse time characteristics. The decision on the direction of current is made on the basis of polarity of phase angle jump together with the impedance angle of the system. The proposed scheme at first is evaluated on a test system in a simulated environment under symmetrical and unsymmetrical faults and, secondly, as a proof of the concept, it is verified in real-time on a laboratory setup using a Power Hardware-in-loop (PHIL) system. Moreover, a comparative analysis is made with other state-of-the-art techniques to evaluate the performance and robustness of the proposed approach.

Advancement in depth-sensing technology has allowed mobile robots to visualize the surrounding environment in 3D models. Regardless of the sensing technology (i.e. active, passive, or laser-based), a complete system that integrates recent depth data in previous 3D models in real-time is done by employing Simultaneous Localization And Mapping (SLAM) algorithms followed by a 3D reconstruction engine. Unfortunately, both the SLAM algorithm and the 3D reconstruction engine are usually executed on a single computing device, making the whole system exceptionally costly and heavy and restricting the robot's mobility. This paper proposes a decentralized, modular reconstruction system capable of employing various sensors to facilitate online 3D reconstruction from a resource-limited mobile robot.