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Maintaining proper operation of adaptive protection schemes is one of the main challenges that must be considered for smart grid deployment. The use of reliable cyber detection and protection systems boosts the preparedness potential of the network as required by National Infrastructure Protection Plans (NIPPS). In an effort to enhance grid cyber-physical resilience, this paper proposes a tool to enable attack detection in protective relays to tackle the problem of compromising their online settings by cyber attackers. Implementing the tool first involves an offline phase in which Monte Carlo simulation is used to generate a training dataset. Using rough set classification, a set of If-Then rules is obtained for each relay and loaded to the relays at the initialization stage. The second phase occurs during online operation, with each updated setting checked by the corresponding relay’s built-in tool to determine whether the settings received are genuine or compromised. A test dataset was generated to assess tool performance using the modified IEEE 34-bus test feeder. Several assessment measures have been used for performance evaluation and their results demonstrate the tool’s superior ability to classify settings efficiently using physical properties only.

Background: Neoadjuvant chemotherapy (NAC) is the favored treatment of choice among locally advanced breast cancer patients because it significantly increases the possibility of breast-conserving surgery. However, for non-responders, an early prediction of response to NAC is essential. Magnetic resonance imaging (MRI) of the breast is an adjunct diagnostic procedure to mammography and ultrasound. Because of its high sensitivity and effectiveness in dense breast tissue, MRI can be a valuable addition to the diagnostic work-up of a patient with breast abnormality or biopsy-proven cancer. Aim of the Work: To highlight the role of advanced MRI techniques in the prediction and follow up of the response of breast cancer to neoadjuvant chemotherapy. Conclusion: Early change in tumor size measured on MR images is a good predictor of final response after Neoadjuvant chemotherapy (NAC). However, even if the cells respond to treatment, it takes some time for the tumor to shrink. Substantial research effort has been spent on investigating whether other information provided by MR imaging may serve as earlier response indicators than change in tumor size. Techniques that seem to be closest to clinical application, due to their feasibility and the promising results, are the pharmacokinetic analysis of DCE-MRI (Dynamic Contrast Enhanced-MRI), DW-MRI (Diffusion Weighted-MRI) and Spectroscopy.

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