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Background: Brain cooling therapy is one of the subjects of interest, and currently, data on direct brain cooling are lacking. Hence, the objective is to investigate the clinical outcomes and discuss the thermodynamics aspect of direct brain cooling on severely injured brain patients. Methods: This pilot study recruited the severely injured brain patients who were then randomized to either a direct brain cooling therapy group using a constant cooling temperature system or a control group. All studied patients must be subjected to an emergency neurosurgical procedure of decompressive craniectomy and were monitored with intracranial pressure, brain oxygenation, and temperature. Further, comparison was made with our historical group of patients who had direct brain cooling therapy through the old technique. Results: The results disclosed the direct brain cooling treated patients through a newer technique obtained a better Extended Glasgow Outcome Score than a control group (P < 001). In addition, there is a significant outcome difference between the combined cooling treated patients (new and old technique) with the control group (P < 0.001). Focal brain oxygenation and temperature are likely factors that correlate with better outcomes. Conclusion: Direct brain cooling is feasible, safe, and affects the clinical outcomes of the severely traumatized brain, and physics of thermodynamics may play a role in its pathophysiology.

Sleepiness has been recognized as a causal factor in many round-the-clock industries. While individuals can subjectively express their momentary sleepiness level, sleepiness-related contextual factors (CF) can influence their perception of sleepiness and cognitive performance. In this paper, the selfreported sleepiness value (vSRS) was improved by transforming it into a kernel density estimate and the assignment of the class's score is done using a likelihood ratio test (IvSRS). We integrated multiple CF and IvSRS to model sleepiness using a Bayesian network (BN). The BN produced a single probability estimate calculated based on the prior and posterior probability of the CF and IvSRS. The results showed IvSRS performed better (p < 0.05) in classifying sleepiness to three states, compared to non-modified vSRS.Considering each CF and IvSRS as stand alone indicators, integrating all these information under a BN significantly improved the systems performance (p ≤ 0.05). In addition to being able to function well in the event of missing vSRS, the proposed system has a prediction horizon of 12 h, with F 1 -measure > 78%.