On the Validation of a Multiple-Network Poroelastic Model Using Arterial Spin Labeling MRI Data

The Multiple-Network Poroelastic Theory (MPET) is a numerical model to characterize the transport of multiple fluid networks in the brain, which overcomes the problem of conducting separate analyses on individual fluid compartments and losing the interactions between tissue and fluids, in addition to the interaction between the different fluids themselves. In this paper, the blood perfusion results from MPET modeling are partially validated using cerebral blood flow (CBF) data obtained from arterial spin labeling (ASL) magnetic resonance imaging (MRI), which uses arterial blood water as an endogenous tracer to measure CBF. Two subjects—one healthy control and one patient with unilateral middle cerebral artery (MCA) stenosis are included in the validation test. The comparison shows several similarities between CBF data from ASL and blood perfusion results from MPET modeling, such as higher blood perfusion in the gray matter than in the white matter, higher perfusion in the periventricular region for both the healthy control and the patient, and asymmetric distribution of blood perfusion for the patient. Although the partial validation is mainly conducted in a qualitative way, it is one important step toward the full validation of the MPET model, which has the potential to be used as a testing bed for hypotheses and new theories in neuroscience research

Safeguarding critical infrastructures from cyber attacks : A case study for offshore natural gas Assets

The majority of operations, as well as the physical and chemical processes, which take place on offshore Natural Gas installations are controlled by computer systems. These computer systems are vulnerable to cyber-attacks. If successful, such attacks can have disastrous and far-reaching consequences, including human casualties, large-scale pollution, and immense financial cost. In this paper we identify one possible way that an attacker can inflict material damage, by altering the parameters of the gas hydrate inhibition system. The formation of gas hydrates can completely halt operations for a prolonged period of time, could damage equipment, and directly endanger human lives. To raise the level of protection we propose the implementation of two lines of defense the second based on machine learning algorithms. Appreciating the sophistication of attacks, the inherent risks and complexity of multi-billion offshore energy assets we highlight the need for further research intended to address safety loopholes