Effects of a long term water level reduction on the ecology and water quality in an eastern Mediterranean lake

Water level fluctuations play a significant role in the lake nutrient dynamics, and consequently may have a strong influence on the biological communities and productivity. In this article we investigated the effects of a long term water level reduction on key chemistry parameters and major biological communities in an eastern Mediterranean lake. Our approach is based on temporal data regarding water quality, fish, zooplankton and aquatic vegetation that are representative of different water level periods. The results revealed significant correlations between water level, conductivity and chloride concentration suggesting a clear effect of the water level reduction on the water quality. Among the key findings of this study is the significant increase of zoobenthivorous fish (roach and carp) from 1973 to 1999 that correlates with the water level reduction. A decline of charophytes is also noted whereas the reed beds appear to have expanded at the shallower parts of the lake. The zooplankton composition of the lake is mostly dominated by nauplii, rotifer and small-sized crustaceans indicating a possible effect of fish predation. Overall, this article has ascertained an alarming shift of water quality and composition of biological communities that can be attributed to the combined effects of eutrophication and the extreme water level decrease

Restless legs syndrome/Willis–Ekbom disease prevalence in beta thalassemia patients

Purpose Both beta thalassemia and restless legs syndrome (RLS) patients share some common pathophysiological characteristics related to iron handling. In the present study, the aim was to explore the prevalence of RLS as well as to explore potential association between the syndrome and various quality of life-related parameters in a sample of beta thalassemia patients. Methods One hundred fourteen (age 40 ± 11 yr, 59 M/55F) beta thalassemia patients participated in this cross-sectional descriptive study. Patients were screened for RLS based on the international RLS study group diagnostic criteria as well as a battery of validated questionnaires. Results The prevalence of RLS in this sample of beta thalassemia patients was zero. The quality of life score was low (78 ± 18). Iron levels were within normal range (191 ± 66 mcg/dL) while ferritin levels were high as expected (1836 ± 225 ng/dL). Conclusions Our sample of patients comes from central Greece where the prevalence of RLS in the general population is 4% while in renal failure patients is 27%. To our surprise, there was no presence of RLS among this sample of beta thalassemia patients. The adequate levels of iron and ferritin often seen in these patients could be the reason of the absence of RLS symptoms

ProsocialLearn: D2.5 evaluation strategy and protocols

This document describes the evaluation strategy for the assessment of game effectiveness, market value impact and ethics procedure to drive detailed planning of technical validation, short and longitudinal studies and market viability tests

FFMRA: A Fully Fair Multi-Resource Allocation Algorithm in Cloud Environments

The need for effective and fair resource allocation in cloud computing has been identified in the literature and in industrial contexts for some time now. Cloud computing, as a promising technology, offers usage-based payment, ondemand computing resources. However, in the recent decade, the growing complexity of the IT world resulted in making Quality of Service (QoS) in the cloud a challenging subject and an NP-hard problem. Specifically, fair allocation of resources in the cloud is one of the most important aspects of QoS that becomes more interesting especially when many users submit their tasks and requests include multiple resources. Research in this area has been considered since 2012 by introducing Dominant Resource Fairness (DRF) algorithm as an initial attempt to solve the resource fair allocation problem in the cloud. Although DRF has some good features in terms of fairness, it has been proven inefficient in some conditions. Remarkably, DRF and other works in its extension are not proven intuitively fair after all. These implementations have been unable to utilize all the resources in the system and more specifically, they leave the system in an imbalanced situation with respect to each specific resource. To tackle those problems, in this paper we propose a novel algorithm namely FFMRA inspired by DRF which allocate resources in a fully fair way considering both dominant and non-dominant shares. The results from the experiments show that our proposed method provides approximately 100% utilization of resources and distributes them fairly among the users and meets good fairness properties

H-FfMRA: A multi resource fully fair resources allocation algorithm in heterogeneous cloud computing

The allocation of multiple types of resources fairly and efficiently has become a substantial concern in state-of-the-art computing systems. Accordingly, the rapid growth of cloud computing has highlighted the importance of resource management as a complicated and NP-hard problem. Unlike traditional frameworks, in modern data centers, incoming jobs pose demand profiles, including diverse sets of resources such as CPU, memory, and bandwidth across multiple servers. Accordingly, the fair distribution of resources, respecting such heterogeneity appears to be a challenging issue. Furthermore, the efficient use of resources as well as fairness, establish trade-off that renders a higher degree of satisfaction for both users and providers. Dominant Resource Fairness (DRF) has been introduced as an initial attempt to address fair resource allocation in multi-resource cloud computing infrastructures. Dozens of approaches have been proposed to overcome existing shortcomings associated with DRF. Although all those developments have satisfied several desirable fairness features, there are still substantial gaps. Firstly, it is not clear how to measure the fair allocation of resources among users. Secondly, no particular trade-off considers non-dominant resources in allocation decisions. Thirdly, those allocations are not intuitively fair as some users are not able to maximize their allocations. In particular, the recent approaches have not considered the aggregate resource demands concerning dominant and non-dominant resources across multiple servers. These issues lead to an uneven allocation of resources over numerous servers which is an obstacle against utility maximization for some users with dominant resources. Correspondingly, in this paper, a resource allocation algorithm called H-FFMRA is proposed to distribute resources with fairness across servers and users, considering dominant and non-dominant resources. The experiments show that H-FFMRA achieves approximately %20 improvements on fairness as well as full utilization of resources compared to DRF in multi-server settings

Computed tomography and magnetic resonance imaging of desmoplastic fibroma with simultaneous manifestation in two unusual locations: a case report

<p>Abstract</p> <p>Introduction</p> <p>Desmoplastic fibroma is an extremely rare primary benign bone tumor. It occurs most often in the mandible, followed by the femur and pelvis. To the best of our knowledge, fewer than 200 cases have been described in the published literature. Furthermore, this case is the first report of desmoplastic fibroma with simultaneous presentation in two different locations.</p> <p>Case presentation</p> <p>We present an unusual case of desmoplastic fibroma in a 56-year-old Caucasian man, who presented to our hospital with lumbar pain. Computed tomography and magnetic resonance imaging were performed, demonstrating two lytic expansile lesions affecting both his left iliac bone and his left sacral wing. Curettage and cortical-cancellous grafting was performed, followed by postoperative computed tomography and magnetic resonance imaging.</p> <p>Conclusion</p> <p>Desmoplastic fibroma with unusual and simultaneous manifestations in two different locations has never been reported previously to the best of our knowledge. The purpose of this case report is to present the computed tomography and magnetic resonance imaging features of this rare tumor before and after the surgical treatment. Furthermore, the radiological findings with the description of the characteristics and the clinical presentation of this rare tumor, contribute to the wide spectrum of manifestations of this tumor, in order to recognize it and to have the appropriate management.</p

On the Complexity of Query Result Diversification

Query result diversification is a bi-criteria optimization problem for ranking query results. Given a database D, a query Q and a positive integer k, it is to find a set of k tuples from Q(D) such that the tuples are as relevant as possible to the query, and at the same time, as diverse as possible to each other. Subsets of Q(D) are ranked by an objective function defined in terms of relevance and diversity. Query result diversification has found a variety of applications in databases, information retrieval and operations research. This paper studies the complexity of result diversification for relational queries. We identify three problems in connection with query result diversification, to determine whether there exists a set of k tuples that is ranked above a bound with respect to relevance and diversity, to assess the rank of a given k-element set, and to count how many k-element sets are ranked above a given bound. We study these problems for a variety of query languages and for three objective functions. We establish the upper and lower bounds of these problems, all matching, for both combined complexity and data complexity. We also investigate several special settings of these problems, identifying tractable cases. 1

Gaze training enhances laparoscopic technical skill acquisition and multi-tasking performance: A randomized, controlled study

Background: The operating room environment is replete with stressors and distractions that increase the attention demands of what are already complex psychomotor procedures. Contemporary research in other fields (e.g., sport) has revealed that gaze training interventions may support the development of robust movement skills. This current study was designed to examine the utility of gaze training for technical laparoscopic skills and to test performance under multitasking conditions. Methods: Thirty medical trainees with no laparoscopic experience were divided randomly into one of three treatment groups: gaze trained (GAZE), movement trained (MOVE), and discovery learning/control (DISCOVERY). Participants were fitted with a Mobile Eye gaze registration system, which measures eye-line of gaze at 25 Hz. Training consisted of ten repetitions of the "eye-hand coordination" task from the LAP Mentor VR laparoscopic surgical simulator while receiving instruction and video feedback (specific to each treatment condition). After training, all participants completed a control test (designed to assess learning) and a multitasking transfer test, in which they completed the procedure while performing a concurrent tone counting task. Results: Not only did the GAZE group learn more quickly than the MOVE and DISCOVERY groups (faster completion times in the control test), but the performance difference was even more pronounced when multitasking. Differences in gaze control (target locking fixations), rather than tool movement measures (tool path length), underpinned this performance advantage for GAZE training. Conclusions: These results suggest that although the GAZE intervention focused on training gaze behavior only, there were indirect benefits for movement behaviors and performance efficiency. Additionally, focusing on a single external target when learning, rather than on complex movement patterns, may have freed-up attentional resources that could be applied to concurrent cognitive tasks. © 2011 The Author(s).published_or_final_versionSpringer Open Choice, 21 Feb 201