The narrative of evaluations: medically supervised injecting centers

Medically supervised injecting centers, or drug consumption rooms are officially sanctioned places where people can inject or smoke illegal drugs in hygienic conditions and under supervision. Their ostensible purposes are to protect the health of drug users and contain the nuisance potential of open drug markets. This article argues that the debates and arguments supporting the establishment and existence of medically supervised injecting centers follow four interweaving narratives. These narratives can be characterized as (1) Caring and humanitarian (2) Elimination of public nuisance (3) Governance of the drug-using subject (4) Neo-liberal, utilitarian, and bureaucratic. These narratives alternatively combine and oppose each other. This means that the analysis of the benefits and problems with such initiatives depends on the perspective of the actors involved and the claims made for their effectiveness.

Full house theory: A new theory for assessing demand for arts centers

In this article I will establish an underpinning theory to apply in measuring demand for a new arts center (theater, museum, gallery, multipurpose space, tourism destination, or cultural precinct). The new theory is called "Full House Theory"-so called because it aims to provide an equation among the factors that result in maximum occupancy and use of an arts center or cultural facility. Existing theories used in the retail sector offer a distance-and-time analysis of expected customer demand but do not include differentiated product-demand analysis. Cultural planning literature examines community need in relation to cultural development but fails to provide a formula to predict sustainable demand. In addition, I will analyze the theories and methodologies in current use as well as their weaknesses in assessing cultural facility demand.

Variation in age and physical status prior to total knee and hip replacement surgery : a comparison of centers in Australia and Europe

Objective : To investigate whether variation exists in the preoperative age, pain, stiffness, and physical function of people undergoing total knee replacement (TKR) and total hip replacement (THR) at several centers in Australia and Europe.
Methods : Individual Western Ontario and McMaster Universities Osteoarthritis Index data (range 0-100, where 0 = best and 100 = worst) collected within 6 weeks prior to primary TKR and THR were extracted from 16 centers (n = 2,835) according to specified eligibility criteria. Analysis of covariance was used to evaluate differences in pain, stiffness, and physical function between centers, with adjustment for age and sex.
Results : There was marked variation in the age of people undergoing surgery between the centers (TKR mean age 67-73 years; F[6,1004] = 4.21, P < 0.01, and THR mean age 63-72 years; F[14,1807] = 7.27, P < 0.01). Large differences in preoperative status were observed between centers, most notably for pain (TKR adjusted mean pain 52.5-61.1; F[6,1002] = 4.26, P < 0.01, and THR adjusted mean pain 49.2-65.7; F[14,1802] = 8.44, P < 0.01) and physical function (TKR adjusted mean function 52.7-61.4; F[6,1002] = 5.27, P < 0.01, and THR adjusted mean function 53.3-71.0; F[14,1802] = 6.71, P < 0.01). Large effect sizes (up to 0.98) reflect the magnitude of variation between centers and highlight the clinical relevance of these findings.
Conclusion : The large variations in age and preoperative status indicate substantial differences in the timing of joint replacement across the centers studied, with potential for compromised surgical outcomes due to premature or delayed surgery. Possible contributing factors include patient preferences, the absence of concrete indications for surgery, and the capacity of the health care systems.

Promoting children's social and emotional wellbeing in childcare centers within low socioeconomic areas: strategies, facilitators and challenges

Althought childcare centres have a vital role to play in the social and emotional development of children, the strategies used to promote children’s wellbeing in such settings are not well researched. This study aimed to identify the strategies, facilitators and key challenges for promoting children’s social and emotional wellbeing as reported by childcare directors and workers during semi-structured interviews. They reported mainly informal strategies with few formalised policies, curricula or strategies. Staff reported frequent difficulties communicating with parents and/or children due to many families speaking little or no English. Lack of staff training and inadequate resources for activities were other key challenges they identified. Perceived facilitators included staff having strong relationships with each other and sharing a common philosophy, as well as having an open door policy for parents. Systematic development of skills to promote children’s social and emotional wellbeing could help leverage childcare staff’s potential to promote children’s wellbeing during a crucial stage of child development.

Overcoming inconsistencies in placement assessment : the case for developmental assessment centers

Placements are integral to many university courses and to increasing student employability skills. Nevertheless, several complications, such as the assessment of placement experiences which often go against the principles of procedural justice, may limit placement effectiveness. For example, procedures are not applied uniformly across students; and evaluations of intangible qualities are susceptible to biases. As a result, effort and learning can be compromised. This paper advocates the use of developmental assessment centers to help solve these shortcomings. Developmental assessment centers are often used in organizations to evaluate capabilities of individuals and to facilitate development. Participants complete a series of work related and standardized tasks. Multiple raters then utilize a systematic approach to evaluate participants on a range of competencies, and consequently present constructive feedback to facilitate learning. Therefore, developmental assessment center principles match the key determinants of procedural justice and thus overcome many problems with traditional placement assessments.

Type-aware task placement in geo-distributed data centers with low OPEX using data center resizing

With the rising demands on cloud services, the electricity consumption has been increasing drastically as the main operational expenditure (OPEX) to data center providers. The geographical heterogeneity of electricity prices motivates us to study the type-aware task placement problem over geo-distributed data centers. With the consideration of the diversity of user requests and server clusters in modern data centers, we formulate an optimization problem that minimizes OPEX while guaranteeing the quality-of-service, i.e., the expected response time of tasks. Furthermore, an efficient solution is designed for this formulated problem. The experimental results show that our proposal achieves much higher cost-efficiency than the greedy algorithm and much approaches the optimal results. © 2014 IEEE.

A general communication cost optimization framework for big data stream processing in geo-distributed data centers

With the explosion of big data, processing large numbers of continuous data streams, i.e., big data stream processing (BDSP), has become a crucial requirement for many scientific and industrial applications in recent years. By offering a pool of computation, communication and storage resources, public clouds, like Amazon's EC2, are undoubtedly the most efficient platforms to meet the ever-growing needs of BDSP. Public cloud service providers usually operate a number of geo-distributed datacenters across the globe. Different datacenter pairs are with different inter-datacenter network costs charged by Internet Service Providers (ISPs). While, inter-datacenter traffic in BDSP constitutes a large portion of a cloud provider's traffic demand over the Internet and incurs substantial communication cost, which may even become the dominant operational expenditure factor. As the datacenter resources are provided in a virtualized way, the virtual machines (VMs) for stream processing tasks can be freely deployed onto any datacenters, provided that the Service Level Agreement (SLA, e.g., quality-of-information) is obeyed. This raises the opportunity, but also a challenge, to explore the inter-datacenter network cost diversities to optimize both VM placement and load balancing towards network cost minimization with guaranteed SLA. In this paper, we first propose a general modeling framework that describes all representative inter-task relationship semantics in BDSP. Based on our novel framework, we then formulate the communication cost minimization problem for BDSP into a mixed-integer linear programming (MILP) problem and prove it to be NP-hard. We then propose a computation-efficient solution based on MILP. The high efficiency of our proposal is validated by extensive simulation based studies.

Optimal power allocation and load distribution for multiple heterogeneous multicore server processors across clouds and data centers

For multiple heterogeneous multicore server processors across clouds and data centers, the aggregated performance of the cloud of clouds can be optimized by load distribution and balancing. Energy efficiency is one of the most important issues for large-scale server systems in current and future data centers. The multicore processor technology provides new levels of performance and energy efficiency. The present paper aims to develop power and performance constrained load distribution methods for cloud computing in current and future large-scale data centers. In particular, we address the problem of optimal power allocation and load distribution for multiple heterogeneous multicore server processors across clouds and data centers. Our strategy is to formulate optimal power allocation and load distribution for multiple servers in a cloud of clouds as optimization problems, i.e., power constrained performance optimization and performance constrained power optimization. Our research problems in large-scale data centers are well-defined multivariable optimization problems, which explore the power-performance tradeoff by fixing one factor and minimizing the other, from the perspective of optimal load distribution. It is clear that such power and performance optimization is important for a cloud computing provider to efficiently utilize all the available resources. We model a multicore server processor as a queuing system with multiple servers. Our optimization problems are solved for two different models of core speed, where one model assumes that a core runs at zero speed when it is idle, and the other model assumes that a core runs at a constant speed. Our results in this paper provide new theoretical insights into power management and performance optimization in data centers.

A classification of healthcare facilities: toward the development of energy performance benchmarks for day surgery centers in Australia

OBJECTIVE: In the literature, there is no consistent classification of healthcare facilities. In order to benchmark, assess, and compare the environmental performance of these buildings, it is important to clearly identify the typology within the scope of a particular research. This article identifies the different typologies within the healthcare sector, particularly in Australia, with the aim of the development of energy performance benchmarks for day surgery/procedure centers. BACKGROUND: Healthcare buildings encompass a wide range of facilities. They all share the same purpose of healing and offering a health service for patients. However, they vary significantly in terms of patient type and service provided. These buildings consume a considerable amount of energy, and as a result of the different designs and sizes, their pattern of energy consumption varies. METHODS: The research used a systematic review of the literature to determine how the term "healthcare facility" has been employed in different contexts. In order to better understand the differences in healthcare facilities, definitions and the origin of hospitals and healthcare facilities are introduced and a framework for the classification of healthcare facilities and hospitals is proposed. RESULTS: Healthcare facilities are classified into the following six categories: patient type, care provided, management and ownership, level of care, facility size, and location. Based on these classifications, a categorization for the studies of energy performance in healthcare is introduced. CONCLUSIONS: This study provides a basis for assessment and comparison for a particular healthcare building typology that will assist researchers working in the field of design and energy assessment of healthcare facilities.

Optimal task placement with QoS constraints in geo-distributed data centers using DVFS

 With the rising demands on cloud services, the electricity consumption has been increasing drastically as the main operational expenditure (OPEX) to data center providers. The geographical heterogeneity of electricity prices motivates us to study the task placement problem over geo-distributed data centers. We exploit the dynamic frequency scaling technique and formulate an optimization problem that minimizes OPEX while guaranteeing the quality-of-service, i.e., the expected response time of tasks. Furthermore, an optimal solution is discovered for this formulated problem. The experimental results show that our proposal achieves much higher cost-efficiency than the traditional resizing scheme, i.e., by activating/deactivating certain servers in data centers.

An energy-aware QoS enhanced method for service computing across clouds and data centers

QoS plays a key role in evaluating a service or a service composition plan across clouds and data centers. Currently, the energy cost of a service's execution is not covered by the QoS framework, and a service's price is often fixed during its execution. However, energy consumption has a great contribution in determining the price of a cloud service. As a result, it is not reasonable if the price of a cloud service is calculated with a fixed energy consumption value, if part of a service's energy consumption could be saved during its execution. Taking advantage of the dynamic energy-Aware optimal technique, a QoS enhanced method for service computing is proposed, in this paper, through virtual machine (VM) scheduling. Technically, two typical QoS metrics, i.e., the price and the execution time are taken into consideration in our method. Moreover, our method consists of two dynamic optimal phases. The first optimal phase aims at dynamically benefiting a user with discount price by transparently migrating his or her task execution from a VM located at a server with high energy consumption to a low one. The second optimal phase aims at shortening task's execution time, through transparently migrating a task execution from a VM to another one located at a server with higher performance. Experimental evaluation upon large scale service computing across clouds demonstrates the validity of our method.

Cost optimal resource provisioning for live video forwarding across video data centers

Live video forwarding for IP cameras has become a popular service in video data centers. In the forwarding service, requests of end users from different regions arrive in real-time to gain live video streams of IP cameras from inter-connected video data centers. A fundamental scheduling problem is how to assign resources with the global optimal resource cost and forwarding delay to forward live video streams. We introduce the resource provisioning cost as the combination of media server cost, connection bandwidth cost, and forwarding delay cost. In this paper, a multi-objective resource provisioning (MORP) approach is proposed to deal with the online inter-datacenter resource provisioning problem. The approach aims at minimizing the resource provisioning cost during live video forwarding. It adaptively allocates media servers in appropriate video data centers and connects the chosen media servers together to provide system scalability and connectivity. Different from previous works, MORP takes both resource capacity and diversity (e.g. location and price) into consideration during live video forwarding. Finally, the experimental results show that MORP approach not only cuts the resource provisioning cost of 3% to 10% comparing to the bench mark approach, but also shortens the resource provisioning delay.