E-learning ecosystem based on service-oriented cloud computing architecture

From Rio to Iragua--sustainability versus efficiency and equity for preventive health interventions.

With the emerging diverse applications in data centers, the demands on quality of service in data centers also become diverse, such as high throughput of elephant flows and low latency of deadline-sensitive flows. However, traditional TCPs are ill-suited to such situations and always result in the inefficiency (e.g. missing the flow deadline, inevitable throughput collapse) of data transfers. This further degrades the user-perceived quality of service (QoS) in data centers. To reduce the flow completion time of mice and deadline-sensitive flows along with promoting the throughput of elephant flows, an efficient and deadline-aware priority-driven congestion control (PCC) protocol, which grants mice and deadline-sensitive flows the highest priority, is proposed in this paper. Specifically, PCC computes the priority of different flows according to the size of transmitted data, the remaining data volume, and the flows' deadline. Then PCC adjusts the congestion window according to the flow priority and the degree of network congestion. Furthermore, switches in data centers control the input/output of packets based on the flow priority and the queue length. Different from existing TCPs, to speed up the data transfers of mice and deadline-sensitive flows, PCC provides an effective method to compute and encode the flow priority explicitly. According to the flow priority, switches can manage packets efficiently and ensure the data transfers of high priority flows through a weighted priority scheduling with minor modification. The experimental results prove that PCC can improve the data transfer performance of mice and deadline-sensitive flows while guaranting the throughput of elephant flows.

Cloud computing (CC) could be a massive distributed computing driven by business, during which the services and resources are area unit delivered on request to external consumer via the Web. The distributed computing environment comprises of physical servers, virtual machines, data centers, and load balancers which are appended in an efficient way. With the increasing size of a number of physical servers and utilization of cloud services in data centers (DC), the power consumption is a critical and challenging research problem. Minimizing the operational cost and power in a DC becomes essential for cloud service provider (CSP). To resolve this problem, we introduced a novel approach that leads to nominal operational cost and power consumption in DCs. We propose a multi-objective modified differential evolution algorithm for first placement of virtual machine (VM) in the physical hosts and optimize the power consumption during resource allocation using live migration. The experimental results reveal that our proposed method is significantly better against state-of-the-art techniques in terms of limited power consumption and SLA for any given workload.

High Altitude Platforms (HAPs) is the name of a new technology to provide wireless narrowband and broadband services. A unique property of HAPs is the ability of preparing emergency telecommunication facilities once the catastrophic events occur. In these situations efficient use of limited spectrum resources would be of great importance. This paper presented a fuzzy channel allocation scheme in HAPs for emergency mobile communications. The suggested scheme is based on guard channel policy. It dynamically adjusts optimum number of reserved channels according to important quality of service (QoS) parameters to make an efficient use of resources, while prioritizes on-going calls over new calls. Simulation compares the proposed fuzzy scheme with fixed guard channel scheme and complete sharing scheme. Results show that, the fuzzy scheme is able to achieve a better grade of service (GoS), while can keep handoff failures low as network traffic load changes.

The global economic impact of basketball is measured in tens of billions of dollars and requires the efficient use of resources to maximize success on and off the court. Today, coaches, players, investors, and owners need to take full advantage of modern analytical methods and digital video software capabilities to make the most efficient use of a teams resources. This research is the first in a series that makes full use of modern analytic methods and begins to define new defensive and offensive criteria to supplement the decades old game box score performance information. Data envelopment analysis and statistical methods are used to evaluate two new defensive performance metrics on defensive efficiency. The two new defensive metrics are contested and uncontested passes that are fully defined in the articles appendix. Future research will expand the sample size and allow for more comprehensive models of basketball team defensive efficiency.

There is general agreement among the major stakeholders—providers, consumers, payors, and managed behavioral health companies—that managed care poses significant challenges for behavioral health professionals, particularly those whose primary work has been in independent practice rather than organized care settings. In traditional independent practice, clinicians were free to employ relatively unstructured, goal-free approaches to care with an emphasis on patient insight and self-knowledge. Formal written treatment plans were a rarity and there was little external pressure on clinicians to complete treatment within any specified period of time. Often, little attention was paid to the efficient use of financial and service resources, and decisions to begin and end treatment were the purview of the therapist and the patient. Patients were generally seen as financially responsible for their treatment regardless of employment based benefits. Indeed, such an approach to practice was consistent with the ways in which most behavioral health professionals were trained. In contrast, clinicians practicing in today's managed care environment are typically required to develop written, goal-focused, time limited treatment plans emphasizing the return of the patient to an adequate level of day-to-day functioning while making efficient use of resources (Austad & Berman, 1991). Decisions to begin and end treatment, at least the part to be paid for through the benefit plan, are made by the therapist and patient together with a third party—the managed care entity. In order to work effectively within this context, clinicians must be both willing and able to change their practice patterns, to at least accommodate

The international guidelines for surveillance following the finding of a small tubular adenoma vary between no surveillance or colonoscopy at 5 or 10 years, whereas surveillance after an advanced adenoma is 3 years. Optimization of surveillance reduces the risk of colorectal cancer (CRC) with efficient use of colonoscopy resources. We assessed the risks of advanced colorectal neoplasia following a baseline finding of a small adenoma compared with advanced adenoma. A retrospective audit was undertaken of patients enrolled in a CRC surveillance program, wherein regular colonoscopies and screening with faecal immunochemical test (FIT) were provided. Patients diagnosed with either small or advanced adenoma followed by at least one surveillance colonoscopy were included. Advanced adenoma included adenomas with features of villous change, size of at least 10 mm, high-grade dysplasia, three or more small tubular adenomas and traditional and sessile serrated adenomas. Subdistribution hazard ratios were calculated for advanced neoplasia (CRC or advanced adenoma). Overall, 378 patients (62.6±11.2 years, 57.9% male) were included, with 44.2% diagnosed with small adenoma and 55.5% with advanced adenoma at baseline. The crude cumulative incidence of advanced neoplasia at first surveillance was 13.2 and 18.5% after small and advanced adenoma (P=0.16) (at 45.9 and 35.6 months, respectively), which became significant for advanced adenoma after adjustment (subdistribution hazard ratio=2.55, 95% confidence interval=1.49-4.35, P<001). A positive FIT was the only independent predictor of advanced neoplasia after a small adenoma at baseline colonoscopy (odds ratio=5.05, 95% confidence interval=1.27-20.02, P=0.02). The risk of advanced neoplasia following a small adenoma was lower than that following an advanced adenoma, but was strongly predicted by a positive FIT. Reducing frequency of colonoscopy while providing regular FIT might be a more efficient use of resources for this population.

This article reviews the principles involved in establishing the cost effectiveness of mass drug therapy for the major intestinal nematodes and the intestinal form of schistosomiasis, as well as the extent to which the available studies have provided definitive answers. For governments or agencies that have decided to introduce a control programme, there is considerable evidence about the comparative cost effectiveness of different types of delivery strategies. For example, mass treatment is more cost effective than selective treatment (treatment after individual screening) in high prevalence areas and at observed prices. Mass treatment targeted at known high risk groups, such as schoolchildren, for the major intestinal helminths (apart from hookworm) can be more cost effective than population-based mass treatment. However, this raises the question whether any form of mass anthelmintic chemotherapy is an efficient use of scare health resources in view of the competing demands for scarce public funds, and for the scarce resources of other funders of healthcare including donors, nongovernmental organisations and missions. Some evidence that it is an efficient use of resources is available; this article concludes by outlining the type of information that is required to strengthen the argument for mass therapy.

Resource use efficiency (RUE) is an ecological concept that measures the proportion of supplied resources, which is converted into new biomass, i.e. it relates realized to potential productivity. It is also commonly perceived as one of the main mechanisms linking biodiversity to ecosystem functioning based on the assumption that higher species numbers lead to more complementary and consequently more efficient use of the available resources. While there exists a large body of literature lending theoretical and experimental support to this hypothesis, there are a number of inconsistencies regarding its application: First, empirical tests use highly divergent approaches to calculate RUE. Second, the quantification of RUE is commonly based on measures of standing stock instead of productivity rates and total pools of nutrients instead of their bioavailable fractions, which both vary across systems and therefore can introduce considerable bias. Third, conceptual studies suggest that the relationship between biodiversity, productivity and RUE involves many more mechanisms than complementary resource use, resulting in variable magnitude and direction of biodiversity effects on productivity. Moreover, RUE has mainly been applied to single elements, ignoring stoichiometric or metabolic constraints that lead to co-limitation by multiple resources. In this review we illustrate and discuss the use of RUE within and across systems and highlight how the various drivers of RUE affect the diversity-productivity relationship with increasing temporal and spatial scales as well as under anthropogenic global change. We illustrate how resource supply, resource uptake and RUE interactively determine ecosystem productivity. In addition, we illustrate how in the context of biodiversity and ecosystem functioning, the addition of a species will only result in more efficient resource use and consequently higher community productivity if the species’ traits related to resource uptake and RUE are positively correlated.

Research on the costs of delivering cancer chemotherapy has typically focused on drug administration, but assessing the overall costs must also consider the human resources necessary. Furthermore, supportive care, such as using colonystimulating factors to manage chemotherapy-induced neutropenia, can greatly affect the costs of care. This study evaluated the human resource costs associated with delivering chemotherapy and managing chemotherapy-induced neutropenia in the community oncology setting. A model of medical visits associated with 21-day chemotherapy regimens and common approaches to managing neutropenia was defined. Four hundred medical professionals at 20 community oncology practices in the United States completed a semistructured interview to provide data on practice patterns and the time and costs of performing medical tasks. The results indicate that chemotherapy- and neutropenia-related medical visits require substantial human resource time and costs. The human resource cost of a single treatment with pegfilgrastim or filgrastim was equivalent, but, because of the greater number of visits required with filgrastim, the total time and costs with pegfilgrastim (2.4 hours and $57.30) were less than those with filgrastim (14.8 hours and $364.66). This study provides important data on medical visits that can be used in future analyses to model the implications of chemotherapy and supportive care for anemia and neutropenia. Implementing strategies to lessen the number of visits can reduce human resource time and costs and help focus these resources on other treatment-related activities.

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.

Nowadays, e-learning systems have known a major revolution, especially with the emergence of new information and communication technologies and the enormous growth in the number of learners, educational content and resources. which causes many challenges in terms of optimizing resources such as compute, storage and communication, highlighting the need to use a model that meets evolving requirements and cost control. In this context, cloud computing is proving to be a promising model because of its permanent scalability and the efficient use of resources. This paper presents the characteristics of current e-learning systems, then analyzes the concept of cloud computing and describes the architecture of it by combining the features of e-learning. We propose an approach of using and harnessing Cloud computing services in e-learning systems to build an e-learning environment based on cloud computing.

As defined by the National Institute of Standards and Technology, cloud computing is “a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction.” Despite the omnipresent role of computers in radiotherapy, cloud computing has yet to achieve widespread adoption in clinical or research applications, though the transition to such “on-demand” access is underway. As this transition proceeds, new opportunities for aggregate studies and efficient use of computational resources are set against new challenges in patient privacy protection, data integrity, and management of clinical informatics systems. In this Session, current and future applications of cloud computing and distributed computational resources will be discussed in the context of medical imaging, radiotherapy research, and clinical radiation oncology applications. Learning Objectives: 1. Understand basic concepts of cloud computing. 2. Understand how cloud computing could be used for medical imaging applications. 3. Understand how cloud computing could be employed for radiotherapy research.4. Understand how clinical radiotherapy software applications would function in the cloud.

BackgroundThe South African allied health (AH) primary healthcare (PHC) workforce is challenged with the complex rehabilitation needs of escalating patient numbers. The application of evidence-based care using clinical practice guidelines (CPGs) is one way to make efficient and effective use of resources. Although CPGs are common for AH in high-income countries, there is limited understanding of how to do this in low- to middle-income countries. This paper describes barriers and enablers for AH CPG uptake in South African PHC.MethodsSemi-structured individual interviews were undertaken with 25 South African AH managers, policymakers, clinicians and academics to explore perspectives on CPGs. Interviews were conducted by researcher dyads, one being familiar with South African AH PHC practice and the other with CPG expertise. Rigour and transparency of data collection was ensured. Interview transcripts were analysed by structuring content into codes, categories and themes. Exemplar quotations were extracted to support themes.ResultsCPGs were generally perceived to be relevant to assist AH providers to address the challenges of consistently providing evidence-based care in South African PHC settings. CPGs were considered to be tools for managing clinical, social and economic complexities of AH PHC practice, particularly if CPG recommendations were contextusalised. CPG uptake was one way to deal with increasing pressures to make efficient use of scarce financial resources, and to demonstrate professional legitimacy. Themes comprised organisational infrastructures and capacities for CPG uptake, interactions between AH actors and interaction with broader political structures, the nature of AH evidence in CPGs, and effectively implementing CPGs into practice.ConclusionCPGs contextualised to local circumstances offer South African PHC AH services with an efficient vehicle for putting evidence into practice. There are challenges to doing this, related to local barriers such as geography, AH training, workforce availability, scarce resources, an escalating number of patients requiring complex rehabilitation, and local knowledge. Concerted attempts to implement locally relevant CPGs for AH primary care in South Africa are required to improve widespread commitment to evidence-based care, as well as to plan efficient and effective service delivery models.

With special reference to the banking industry, the objective of this study is to address managerial concerns over the impact of labor‐saving technologies on efficiency in the use of human resources. A bank is viewed as a collection of human, technology, and capital resources. Labor‐saving technologies are represented by two categories of technology resources—information technologies and patented in‐house process innovations. The estimation of a stochastic frontier manpower‐requirement function shows that, whereas information technology resources have a direct impact on efficiency in the use of human resources, in‐house process innovations have an indirect impact through spillovers. The reduction in labor costs resulting from a more efficient use of human resources is more than enough to cover the required increase in information technology expenditures. This cost‐reducing impact is stronger for firms currently employing a lower level of information technologies. The empirical findings also suggest a complementary relationship between information technologies and spillovers of in‐house process innovations. The empirical framework proposed in this study can help decision makers determine the optimal input mix of technology and human resources.