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The increasing demand for wireless broadband services poses the need for efficient utilisation of the backhaul network resources. To this end, schemes that use artificial neural networks in order to predict the forthcoming network traffic demand and proactively request the commitment of the necessary resources have been proposed. However, an up-to-date prediction model, required by these schemes, necessitates a regularly held training process, which incurs a high computational cost. This reported work investigates the trade-off between prediction accuracy and computational efficiency by employing evolutionary game theory and a novel scheme is proposed that can achieve both the aspects.

The increasing bandwidth demand of end-users renders the need for efficient resource management more compelling in next generation wireless networks. In the present work, a novel scheme incorporating the deployment of an intelligent agent capable of monitoring, storing, and predicting the forthcoming needs for resources of a base station (BS) is proposed. In this way, the BS can in advance commit the necessary resources for its backhaul connection, guaranteeing the end-user's quality of service. The prediction process is performed using machine learning techniques.

The increasing bandwidth demand of end-users renders the need for efficient resource management more compelling in next generation wireless networks. In the present work, a novel scheme incorporating the deployment of an intelligent agent capable of monitoring, storing, and predicting the forthcoming needs for resources of a base station (BS) is proposed. In this way, the BS can, in advance, commit the necessary resources for its backhaul connection, guaranteeing the end-user's quality of service. The prediction process is performed using machine learning techniques.

Acute type A aortic dissection requires timely diagnosis and intervention. Previous studies have examined risk factors associated with delayed diagnosis; however, the effect of socioeconomic status (SES) has not been previously studied. Our study examined the impact of various SES measures on time to diagnosis. We examined time to diagnosis in consecutive cases of acute type A aortic dissection at a single institution. SES variables included race/ethnicity, Medicaid eligibility, and residence in a zip code with an increased Distressed Communities Index—an aggregate measure of community SES. Delayed diagnosis was defined as time to diagnosis in the upper quartile of the study population (>6.6 hours). A model predicting risk factors for delayed diagnosis was created using multivariable logistic regression. Our study included 124 patients with a median time to diagnosis of 3.36 hours (interquartile range [IQR] 1.83 to 6.63). A total of 92 patients were in the nondelayed cohort (median diagnosis time of 2.59 hours, IQR 1.49 to 4.18) and 32 patients were in the delayed cohort (median diagnosis time of 15.57 hours, IQR 9.34 to 28.75). In multivariable logistic regression, residence in a high–Distressed Communities Index zip code was associated with diagnostic delay (adjusted odds ratio [aOR] 5.108, p = 0.008). Patient age (aOR 0.944, p = 0.011), chest pain at presentation (aOR 0.099, p = 0.004), back pain at presentation (aOR 0.247, p = 0.012), evidence of malperfusion syndrome (aOR 0.040, p <0.001), history of hyperlipidemia (aOR 3.507, p = 0.026), and history of congestive heart failure (aOR 0.061, p = 0.036) were also significantly associated. In conclusion, our findings suggest community-level SES affects time to diagnosis in acute type A aortic dissection.

One of the major challenges that mobile operators (MOs) are faced with nowadays is the transition to 4th Generation (4G) mobile communication technologies. The main reason for this lies on the reluctance of MOs to invest in a new technology without being sure about its success. The current paper investigates the decision-making procedures of a MO that wishes to migrate from its current technology type to 4G. Traditionally, the decision of deploying a new technology has been based on the analysis of similar implementations in other countries. However, such approaches can be inefficient and time consuming, as there are discrepancies concerning the technological progress among different countries. To this end, the authors employ evolutionary game theory to model the interactions of the MO’s decisions and the subscribers’ needs, and propose a practical and efficient qualitative model that identifies the circumstances under which the transition towards 4G networking can be facilitated. Specifically, the mathematical foundation of the decision making process is provided and the key role of the charging price and the quality of experience by the subscribers for using 4G connectivity is proven. With the process of 4G deployment still ongoing, this paper aims to present an analysis that can be used supplementary to the decision process of a MO that aims to evolve his network.

During the last decade, in parallel with the rapid growth of mobile communications and devices, location-based social networks have met a tremendous growth with the acceptance of the public being constantly increasing. Users have access to a plethora of venues and points of interest, while they are able to share their visits to various locations along with comments and ratings about their experience (a process which is often referred to as “check-ins”). Location recommendations based on users’ needs have been a subject of interest for many researchers, while location prediction schemes have been developed in order to provide user’s possible future locations. In this paper, we present a novel method for predicting a user’s location based on machine learning techniques. In addition, following the incremental trend towards data accumulation in social networks, we introduce a clustering based prediction method in order to enhance the recommender system. For the prediction process we propose a probabilistic neural network and confirm its superior performance against two other types of neural networks, while for the clustering process we use a K-means clustering algorithm. The dataset we used was based on input from a well-known location-based social network. Prediction results can be used in order to make appropriate suggestions for venues or points of interests to users, based on their interests and social connections.

Background Pseudoaneurysms occur at the rupture site of true aneurysms and appear as irregularly shaped and partially thrombosed outpouchings of the main sac. Recanalization of thrombi inside pseudoaneurysmal sac is one of the putative mechanisms of rebleeding of unsecured aneurysms and of coil migration after endovascular treatment. We document “real time” pseudoaneurysm formation after rerupture of an anterior communicating artery aneurysm. Methods Case report. Results A 55-year-old man with aneurysmal subarachnoid hemorrhage from an anterior communicating aneurysm underwent catheter angiography. After the diagnostic angiogram while awaiting for the anesthesia team to proceed with endotracheal general anesthesia, a seizure occurred. Rebleeding was suspected and confirmed by a dynamic CT in the angio suite. A repeat angiogram showed a pseudoaneurysm arising from the previously ruptured aneurysm which had not been present on the original angiogram a few minutes earlier. Uneventful coiling of the aneurysm was undertaken and the patient was discharged home a week later. Conclusions We document angiographic formation of a “pseudoaneurysm” at the site of rupture of an anterior communicating artery aneurysm. “Pseudoaneurysm” formation occurs after rupture of an intracranial aneurysm. They represent a weak spot in the aneurysm sac at the site of rupture and probably the result of persistent flow within the clot forming at the site of rupture. Presence of a pseudoaneurysm with characteristic angiographic features like the one herein described represents an unstable area within the aneurysm. This case also highlights the observation that, in patient harboring unsecured ruptured aneurysms, seizures or seizures-like phenomena are the clinical expression of rebleeding unless proven otherwise.