Cyber Abuse Behaviour in Young Adults : Revenge Porn and how the Bournemouth University Community can learn from the issue.

This paper analyses the impact that victims of cyber extortion face in particularly with revenge pornography. It points out legislation available for remedial action in civil cases such as sextortion. The paper is written to help safeguard university students from online harassment and points out the channels available for reporting incidences of revenge porn.

The Binding Nature of the Ombudsman’s Remedial Actions in Lesotho: Lessons from South Africa

The Constitution of Lesotho is regarded as liberal, with the separation of powers of the executive, judiciary and legislature, but provision was made for institutions to oversee the exercise of such powers. One of these is the Office of the Ombudsman, which has wide-ranging powers to investigate malpractice in public administration and to make recommendations for remedial action. These powers notwithstanding, there is a paucity of judicial precedent and scholarship on the binding nature of the remedial action of the Ombudsman. The main question is whether the remedial action of the Ombudsman is binding, which this article seeks to investigate and determine whether South African jurisprudence could be helpful in the case of Lesotho. The binding nature of the Public Protector’s remedial action under the Constitution of South Africa has been a subject of considerable judicial and scholarly engagement in recent times. While the subject is still yet to be settled in South Africa, it nevertheless has developed principles that could assist in both the interpretation and reform of the Ombudsman’s powers in Lesotho.

Outside the Box: Innovative Application of Diversion as a Replacement for Bridge Plug

Effective stage-to-stage isolation is typically accomplished by setting a bridge plug in a properly cemented casing between stages. This isolation plays a vital role in a horizontal well multistage fracturing completion. Failure of isolation not only impacts the well productivity but also wastes fracturing materials. The challenges isolation failure poses for stimulation effectiveness include both detection and remediation. First, there has been historically no reliable and cost-effective solution to detect stage-to-stage isolation onsite. One may only start to realize this problem when inconsistent production is observed. Second, existing remedial actions are seldom satisfying in case of an isolation failure. Most commonly, a new plug is set to replace the failed one. However, because the perforation clusters of an unstimulated stage may create irregularities in well inside diameter (ID) (e.g., casing deformation or burr), there is a risk that the plug will be damaged or become stuck when it passes the perforation area. Also, when the plug passes a perforation cluster, the perforations start to take in the pump-down fluid, which can increase the difficulty of the pump-down job. A novel remedial action uses high-frequency pressure monitoring (HFPM) and diversion to solve both challenges. The stage isolation integrity is evaluated in quasi-real time by analyzing the water hammer after the pump shutdown. In the case of a plug failure, large-particle fracture diversion materials and techniques can establish temporary wellbore isolation through a quick and simple delivery process. To close the cycle, the effect of the diversion can be evaluated by HFPM, which can reveal the fluid entry point of the treatment fluid after diversion. The technique was applied to two cases in Ordos basin in which wellbore isolation failure interrupted the operation. The problem identification, development of the solution workflow, and observation from treatment analysis are discussed. In both cases, the stage-to-stage isolation was recovered, and the drilled sand body was successfully stimulated without involving costly and time-consuming well intervention. The stimulation operation of the entire well was successfully resumed in a timely manner.

Deep Learning-Based Adaptive Remedial Action Scheme with Security Margin for Renewable-Dominated Power Grids

The Remedial Action Scheme (RAS) is designed to take corrective actions after detecting predetermined conditions to maintain system transient stability in large interconnected power grids. However, since RAS is usually designed based on a few selected typical operating conditions, it is not optimal in operating conditions that are not considered in the offline design, especially under frequently and dramatically varying operating conditions due to the increasing integration of intermittent renewables. The deep learning-based RAS is proposed to enhance the adaptivity of RAS to varying operating conditions. During the training, a customized loss function is developed to penalize the negative loss and suggest corrective actions with a security margin to avoid triggering under-frequency and over-frequency relays. Simulation results of the reduced United States Western Interconnection system model demonstrate that the proposed deep learning–based RAS can provide optimal corrective actions for unseen operating conditions while maintaining a sufficient security margin.