Background: Bow Hunter's syndrome/stroke is defined as symptomatic, vertebrobasilar insufficiency provoked by physiologic head rotation. It is a diagnostically challenging cause of posterior circulation stroke in children. While there have been prior reports of this rare disorder, we describe an exceptional case of pediatric Bow Hunter's stroke resulting from a near complete occlusion the right vertebral artery (VA) secondary to an anomalous spur emanating from the right occipital condyle. Surgical and endovascular options and approaches are also detailed herein. Case Description: A 16-year-old male presented with multiple posterior circulation ischemic strokes. A dynamic computerized tomography angiogram performed with the patient's head in a rotated position revealed a near complete occlusion of the V3 segment of the right VA from a bone spur arising from his occipital condyle. The spur caused a focal dissection of the distal right VA with associated thrombus. He was initially managed with a cervical collar, antiplatelet therapy with aspirin 81 mg and anticoagulation with coumadin (INR goal 2-3) for 3 months. Despite the management plan, he had a subsequent thromboembolic event and a right VA sacrifice with coil embolization was then performed. At the 3-month follow-up, the patient was doing well with no reports of any subsequent strokes. Conclusion: We report the first reported pediatric case of Bow Hunter's stroke due to dynamic right VA occlusion from an occipital condylar bone spur. The vascular compression from this spur led to a right VA dissection and thrombus formation and ultimately caused multiple posterior circulation thromboembolic strokes. Endovascular treatment options including vessel sacrifice should be considered in cases that have failed maximal medical management.

Currently, there is great interest in reducing the consumption of fossil fuels (and other non-renewable energy sources) in order to preserve the environment; smart buildings are commonly proposed for this purpose as they are capable of producing their own energy and using it optimally. However, at times, solar energy is not able to supply the energy demand fully; it is mandatory to know the quantity of energy needed to optimize the system. This research focuses on the prediction of output temperature from a solar thermal collector. The aim is to measure solar thermal energy and optimize the energy system of a house (or building). The dataset used in this research has been taken from a real installation in a bio-climate house located on the Sotavento Experimental Wind Farm, in north-west Spain. A hybrid intelligent model has been developed by combining clustering and regression methods such as neural networks, polynomial regression, and support vector machines. The main findings show that, by dividing the dataset into small clusters on the basis of similarity in behavior, it is possible to create more accurate models. Moreover, combining different regression methods for each cluster provides better results than when a global model of the whole dataset is used. In temperature prediction, mean absolute error was lower than 4 ${}^{\circ }$ C.

Background: Tumors of the fourth ventricle are exceedingly rare; however, such lesions are formidable due to the severe postoperative neurological complications (pNCs) which often occur. The adoption of the telovelar approach over the transvermian was created to supposedly mitigate the pNCs; however, there is a lack of sufficient data supporting this theory. Methods: Records from six hospitals were reviewed for patients surgically treated for a single tumor within the 4^th ventricle from 2016 to 2022. The pNCs which had 10 or more occurrences among the patients were individually assessed as the dependent variable in a binary logistic regression model against covariates which included the surgical approach. Results: This study of 67 patients confirms no significant differences in risk for pNCs between the transvermian and telovelar approach. Rather, multivariate analysis identified neurophysiological monitoring (IONM) as a protective factor for postoperative speech and swallowing defects (odds ratio [OR]: 0.076, 95% confidence interval [CI] 0.011–0.525). Furthermore, intraoperative external ventricular drainage (EVD) was a protective factor for postoperative gait and focal motor defects (OR: 0.075, 95% CI 0.009–0.648) and for postoperative hydrocephalus (OR: 0.020, 95% CI 0.002–0.233). A univariate meta-analysis pooling the present study’s patients and an additional 304 patients from the three additional studies in the literature confirms no significant differences in risk between the transvermian and telovelar approach for pNCs. Conclusion: Intraoperative adjuncts including IONM and EVD may play a significant role in the postoperative outcome. Despite the present study’s sample size being a major limitation, the findings may provide great value to neurosurgeons given the scarcity of the current literature.

Present research proposes the application of unsupervised and supervised machine‐learning techniques to characterize Android malware families. More precisely, a novel unsupervised neural‐projection method for dimensionality‐reduction, namely, Beta Hebbian Learning (BHL), is applied to visually analyze such malware. Additionally, well‐known supervised Decision Trees (DTs) are also applied for the first time in order to improve characterization of such families and compare the original features that are identified as the most important ones. The proposed techniques are validated when facing real‐life Android malware data by means of the well‐known and publicly available Malgenome dataset. Obtained results support the proposed approach, confirming the validity of BHL and DTs to gain deep knowledge on Android malware.

Mackel CE, et al. Brain Tumor Res Treat. 2023 Apr;11(2):145-152. https://doi.org/10.14791/btrt.2023.0002

The present research work focuses on overcoming cybersecurity problems in the Smart Grid. Smart Grids must have feasible data capture and communications infrastructure to be able to manage the huge amounts of data coming from sensors. To ensure the proper operation of next-generation electricity grids, the captured data must be reliable and protected against vulnerabilities and possible attacks. The contribution of this paper to the state of the art lies in the identification of cyberattacks that produce anomalous behaviour in network management protocols. A novel neural projectionist technique (Beta Hebbian Learning, BHL) has been employed to get a general visual representation of the traffic of a network, making it possible to identify any abnormal behaviours and patterns, indicative of a cyberattack. This novel approach has been validated on 3 different datasets, demonstrating the ability of BHL to detect different types of attacks, more effectively than other state-of-the-art methods.