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Background: Critical limb ischemia is considered the most severe form of peripheral artery disease. High morbidity and mortality rates are associated with this pathology due to poor economic management of complications. Case summary: A 68-year-old female patient underwent a routine Doppler ultrasound which revealed the obstruction of both the common and superficial right femoral arteries, as well as the right deep femoral artery. In this case, an iliofemoral bypass was performed using the right deep femoral artery as the outflow artery. Conclusion: Revascularization of the deep femoral artery has a reasonable chance of preserving the ischemic lower limb whenever the common and superficial femoral arteries suffer major atherosclerotic blockages.

Introduction: In this report, we present a rare case of aortic aneurysm with associated left ureter obstruction and consequent hydronephrosis. Case presentation: A 62-year-old man was admitted for epigastric and periumbilical pain, extended in the spine, and anuria. As for associated diseases, he presented arterial hypertension stage II/III, chronic ischemic cardiomyopathy, acidosis, hyperpotassemia, spondylosis with radiculopathy, acute renal failure, renal lithiasis, and eating difficulties. After performing a CT scan, the patient was diagnosed with giant abdominal infrarenal aortic aneurysm with ureter compression, and retroperitoneal rupture with common and internal iliac arteries occlusion. We performed a xifo-pubian laparotomy with endoaneurysmorrhaphy and aortoiliac bilateral bypass using silver Dacron vascular prosthesis (16 × 8 mm in diameter). Douglas drainage was necessary, ending with laparoraphy. The patient presented a favorable postoperative evolution. Conclusion: Open surgery remains the gold standard method of treatment for large aortic aneurysms, given the inability of endovascular procedures to solve problems caused by compression.

Abstract. Background: The role of vascular calcifications in iliac arteries for predicting global atherosclerotic burden in still unknown. The aim of this study was to investigate whether iliac calcium score (ICS), a new computed tomographic angiography (CTA) derived biomarker of vascular calcification, can predict the severity and complexity of peripheral arterial disease (PAD) as well as the global atherosclerotic burden. Patients and methods: This was a single centre, non-randomized, observational prospective study on 84 consecutive patients with symptomatic peripheral arterial disease, undergoing peripheral CTA examination of the lower limbs, divided into high (n = 42) and low ICS (n = 42) groups with a median value for ICS of 3934 HU. Results: Patients with high ICS were significantly older (66.2 ± 8.0 vs. 62.8 ± 11.2, p < 0.0001) and were more frequently diabetic (61.9 vs. 38.1 %, p = 0.04). ICS was significantly higher in patients with Rutherford stage 5–6 vs. 1–2 (p = 0.03) and in TASC D or TASC C vs. TASC B class (p = 0.01). Mean iliac intima-media thickness (i-IMT) was significantly higher in the high ICS group compared to the low ICS group (1.3 ± 0.2 vs. 0.9 ± 0.2, p < 0.0001). Linear regression analysis demonstrated a very good correlation between ICS and i-IMT (r = 0.59, p < 0.0001 for right, r = 0.57, p < 0.0001 for left and r = 0.67, p < 0.0001 for both iliac arteries averaged). Patients with high ICS presented a significantly lower left ventricular ejection fraction compared to those with low ICS (45.3 ± 4.3 vs. 53.8 ± 4.8, p < 0.0001). Linear regression analysis demonstrated significant inverse correlation between ICS and left ventricular EF (r = –0.54, p < 0.0001). Conclusions: Increased values of ICS, a new CTA marker of vascular calcification, are associated with a higher severity and complexity of PAD and a more depressed left ventricular function. High ICS values are also associated with increased i-IMT. Both can represent new surrogate markers of an increased atherosclerotic burden.

Over the past years, the Cloud phenomenon had an impressive increase in popularity in both the software industry and research worlds. The most interesting feature that Cloud Computing brings, from a Cloud client's point of view, is the on-demand resource provisioning model. This allows Cloud client platforms to be scaled up in order to accommodate more incoming clients and to scale down when the platform has unused resources, and this can all be done while the platform is running. As a result, the physical resources are used more efficiently and the Cloud client saves expenses. Achieving the above mentioned is not trivial and is done by leveraging more direct functionalities that Clouds provide. Three of these key functionalities are automatic scaling, load balancing and monitoring. They represent the focus of the current work. This report is divided into three main parts, one for each of the three main topics of interest: auto-scaling, load balancing and monitoring. We detail each of the three topics and present details on their presence and implementation in the commercial, open-source and research worlds. Among the commercial Cloud platform providers we have focused on Amazon EC2, Microsoft Azure, GoGrid and RackSpace. From the open-source initiatives we have focused our attention on Nimbus, Eucalyptus and OpenNebula. We have also detailed endeavours in the research world that are relevant to the topic of interest and are not connected to any of the Cloud providers. By studying this report, the reader will be able to understand the role that each of the three Cloud features plays for a Cloud client, will understand their inner workings and will be aware of the state-of-the-art available in current commercial and open-source Cloud platforms.

Infrastructure as a Service clouds are a flexible and fast way to obtain (virtual) resources as demand varies. Grids, on the other hand, are middleware platforms able to combine resources from different administrative domains for task execution. Clouds can be used by grids as providers of devices such as virtual machines, so they only use the resources they need. But this requires grids to be able to decide when to allocate and release those resources. Here we introduce and analyze by simulations an economic mechanism (a) to set resource prices and (b) resolve when to scale resources depending on the users’ demand. This system has a strong emphasis on fairness, so no user hinders the execution of other users’ tasks by getting too many resources. Our simulator is based on the well-known GridSim software for grid simulation, which we expand to simulate infrastructure clouds. The results show how the proposed system can successfully adapt the amount of allocated resources to the demand, while at the same time ensuring that resources are fairly shared among users.

International audience; This paper surveys the risks brought by multitenancy in software platforms, along with the most prominent solutions proposed to address them. A multitenant platform hosts and executes software from several users (tenants). The platform must ensure that no malicious or faulty code from any tenant can interfere with the normal execution of other users' code or with the platform itself. This security requirement is specially relevant in Platform-as-a-Service (PaaS) clouds. PaaS clouds offer an execution environment based on some software platform. Unless PaaS systems are deemed as safe environments users will be reluctant to trust them to run any relevant application. This requires to take into account how multitenancy is handled by the software platform used as the basis of the PaaS offer. This survey focuses on two technologies that are or will be the platform-of-choice in many PaaS clouds: Java and .NET. We describe the security mechanisms they provide, study their limitations as multitenant platforms and analyze the research works that try to solve those limitations. We include in this analysis some standard container technologies (such as Enterprise Java Beans) that can be used to standardize the hosting environment of PaaS clouds. Also we include a brief discussion of Operating Systems (OSs) traditional security capacities and why OSs are unlikely to be chosen as the basis of PaaS offers. Finally, we describe some research initiatives that reinforce security by monitoring the execution of untrusted code, whose results can be of interest in multitenant systems.

International audience; The Cloud phenomenon brings along the cost-saving benefit of dynamic scaling. As a result, the question of efficient resource scaling arises. Prediction is necessary as the virtual resources that Cloud computing uses have a setup time that is not negligible. We propose an approach to the problem of workload prediction based on identifying similar past occurrences of the current short-term workload history. We present in detail the Cloud client resource auto-scaling algorithm that uses the above approach to help when scaling decisions are made, as well as experimental results by using real-world Cloud client application traces. We also present an overall evaluation of this approach , its potential and usefulness for enabling efficient auto-scaling of Cloud user resources.

International audience; Over the past years, the Cloud phenomenon had an impressive increase in popularity in both the software industry and research worlds. The most interesting feature that Cloud Computing brings, from a Cloud client's point of view, is the on-demand resource provisioning model. This allows Cloud client platforms to be scaled up in order to accommodate more incoming clients and to scale down when the platform has unused resources, and this can all be done while the platform is running. As a result, the physical resources are used more efficiently and the Cloud client saves expenses. Achieving the above mentioned is not trivial and is done by leveraging more direct functionalities that Clouds provide. Three of these key functionalities are automatic scaling, load balancing and monitoring. They represent the focus of the current work. This report is divided into three main parts, one for each of the three main topics of interest: auto-scaling, load balancing and monitoring. We detail each of the three topics and present details on their presence and implementation in the commercial, open-source and research worlds. Among the commercial Cloud platform providers we have focused on Amazon EC2, Microsoft Azure, GoGrid and RackSpace. From the open-source initiatives we have focused our attention on Nimbus, Eucalyptus and OpenNebula. We have also detailed endeavours in the research world that are relevant to the topic of interest and are not connected to any of the Cloud providers. By studying this report, the reader will be able to understand the role that each of the three Cloud features plays for a Cloud client, will understand their inner workings and will be aware of the state-of-the-art available in current commercial and open-source Cloud platforms.

International audience; The Cloud phenomenon brings along the cost-saving benefit of dynamic scaling. As a result, the question of efficient resource scaling arises. Prediction is necessary as the virtual resources that Cloud computing uses have a setup time that is not negligible. We propose an approach to the problem of workload prediction based on identifying similar past occurrences of the current short-term workload history. We present in detail the Cloud client resource auto-scaling algorithm that uses the above approach to help when scaling decisions are made, as well as experimental results by using real-world traces from Cloud and Grid platforms. We also present an overall evaluation of this approach, its potential and usefulness for enabling efficient auto-scaling of Cloud user resources.

International audience; The Cloud phenomenon is quickly growing towards becoming the de facto standard of Internet Computing, storage and hosting both in industry and academia. The large scalability possibilities offered by Cloud platforms can be harnessed not only for services and applications hosting but also as a raw on-demand computing resource. This paper proposes the use of a Cloud system as a raw computational on-demand resource for a Grid middleware. We illustrate a proof of concept by considering the DIET-Solve Grid middleware and the EUCALYPTUS open-source Cloud platform.

A low cost solution is proposed for measuring the vibrations induced by rotating parts. Attaching very sensitive acceleration sensors allows measuring these vibrations. The signal acquired from the sensors will present a periodic behaviour which is correlated to the rotational speed of the axle. Beside the indication of the RPM of the axle, more detailed information can be extracted using signal processing techniques in order to determine the signatures associated with specific faults.