Dynamic evaluation method for planning sustainable landfills using GIS and multi-criteria in areas of urban sprawl with land-use conflicts

Landfill site selection is problematic in many countries, especially developing nations where there is rapid population growth, which leads to high levels of inadequate waste disposal. To find sustainable landfill sites in sprawling cities, this study presents an approach that combines geographic information system (GIS) with multi-criteria (social, environmental and, technical criteria) and the population growth projection. The greater Maputo area in Mozambique was selected as a representative city for the study, which is undergoing rapid urbanization. Six criteria, i.e., land use, transport networks, hydrology, conservation reserve, geology and slope, were considered and overlaid in the GIS using an analytic hierarchy process (AHP). The arithmetic projection of the population trend suggests that the greater Maputo area is experiencing a rapid and uncontrolled population growth, especially in Matola city. These pronounced changes in population then significantly change the landfill placement decision making. Dynamic and static scenarios were created, based on the analysis of multi-criteria and the areas likely to undergo future increased population growth. A comparative evaluation in a scenario of dynamic behavior considering future population showed that suitable areas for landfill sites have been drastically modified due to social and environmental factors affected by population distribution in some regions. The results indicate that some suitable areas can generate land use conflicts due to population growth with unplanned land use expansion. Finally, the western part of Matola city is recognized as the most recommendable landfill site, which can serve both Maputo and Matola city with affordable costs. This study provides an effective landfill placement decision making approach, which is possible to be applied anywhere, especially in developing countries to improve sustainable municipal solid waste management systems.

An Attachment-Based Parental Capacity Assessment to Orient Decision-Making in Child Protection Cases: A Randomized Control Trial

Two parenting capacity assessment (PCA) protocols, with a short parent-child intervention embedded in each protocol, evaluated the potential for enhanced parenting to orient child placement decision. Parents ( n = 69), with substantiated reports of maltreatment by child protective services, and their children (0–6) were randomly assigned to one of two PCAs with either the Attachment Video-feedback (PCA-AVI) or a psychoeducational intervention (PCA-PI) as the embedded intervention component. The PCA-AVI group showed the highest increases in parent-child interaction quality at post-test. Also, at PCA completion, evaluators’ conclusions about the parents’ capacity to care for both PCA groups were associated with parent-child interactive improvements at post-test, the court’s placement decision at post-test, and child placement one year later. However, only conclusions drawn by PCA-AVI evaluators were predictive of child re-reports of maltreatment in the year following PCA. PCAs, relying on short attachment interventions to assess the potential for enhanced parenting, are promising tools to orient child placement decisions.

The Psychosocial Process Underlying Residential Care Placement Decisions

Ecological models on decision-making in child protection determine how many different factors influence that process, starting with case-specific characteristics, organizational factors, and external factors, as well as decision-maker factors. However, how that psychosocial process occurs, how the decision-maker integrates those multiple factors from the decision-making ecology, is still empirically unclear. This chapter, first, reviews the state of the art in the study of caseworkers’ psychosocial process underlying the out-of-home placement decision. It summarizes cues from empirical studies sustaining the role played by caseworkers’ attitudes, social values, social norms, experience, emotions in out-of-home placement decisions. The authors, then, describe social psychology decision-making models and present the principal results of an empirically tested model of residential-care placement decision-making that, based on a dual version of the theory of planned behavior model, integrates those multiple psychosocial factors into the decision process. A structural equation modeling analysis revealed that the caseworker’s motivation (intention) to propose a residential care placement decision of a neglected child is highly explained by a positive evaluation of that behavior (Attitude), but also by significant others’ approval of that behavior (Subjective Norm) and by how much relevance the worker attributes to child’s interests and protection (Value of Child). Both theoretical and social policy implications are discussed.

Adaptive Energy-Aware Algorithms to Minimize Power Consumption and SLA Violation in Cloud Computing

: With the establishment of virtualized datacenters on a large scale, cutting-edge technology requires more energy to deliver the services 24*7 hours. With this expansion and accumulation of information on a massive scale on datacenters, the consumption of excessive amount of power results in high operational costs and power consumption. Therefore, there is an urgent need to make the environment more adaptive and dynamic, where the overutilization and underutilization of hosts is well known to the system and active measures can be taken accordingly. To serve this purpose, an energy efficient method for the detection of overloaded and under-loaded hosts has been proposed in this paper. For implementing VM migration, VM placement decision has also been taken to save energy and reduce SLA (Service Level Agreement) rate over the cloud. In the paper, a novel adaptive heuristics approach has been presented that concerns with the utilization of resources for a dynamic consolidation of VMs based on the mustered data from the usage of resources by VMs, while ensuring the high level of relevancy to the SLA. After identification of under-load and overload hosts, VM placement decision has been taken in the way that takes minimum energy consumption. Minimum migration policy has been adopted in the proposed methodology to minimize execution time. The validation of effectiveness and efficiency of the suggested approach has been performed by using real-world workload traces in CloudSim simulator.

Normative and Positive Social Work in the Context of the Placement Decision: A Defence of Social Workers

Two fundamental questions for social work are considered: one normative and one positive. First, is it possible for social work practice to be based on an objective that maximises social welfare; and second, does social work practice actually conform to some objective, which may or may not maximise social welfare? These two questions are addressed in the context of one of the most important decisions made by social workers - the placement decision. It is argued that deriving a societal objective faces formidable theoretical problems, and that even if a well-defined criterion was available, actual social work decisions would still be inconsistent due to a lack of the requisite information and different interpretations of the available data. It is argued that the substantial empirical evidence from around the world on the placement decision provides little evidence of consistent decision making. This may be because the statistical analyses have lacked the data and techniques necessary to detect the underlying patterns, or because placement decisions are largely random. The apparent absence of clear objectives, either specified by society or accepted custom and practice, places social workers in a very difficult position, making them open to press criticism and victimization, even though they acted entirely competently.

Model-Based Extraction of Knowledge about the Effect of Cloud Application Context on Application Service Cost and Quality of Service

With the increased usage of cloud computing in production environments, both for scientific workflows and industrial applications, the focus of application providers shifts towards service cost optimisation. One of the ways to achieve minimised service execution cost is to optimise the placement of the service in the resource pool of the cloud data centres. An increasing number of research approaches is focusing on using machine learning algorithms to deal with dynamic cloud workloads by allocating resources to services in an adaptive way. Many of such solutions are intended for cloud infrastructure providers and deal only with specific types of cloud services. In this paper, we present a model-based approach aimed at the providers of applications hosted in the cloud, which is applicable in early phases of the service lifecycle and can be used for any cloud application service. Using several machine learning methods, we create models to predict cloud service cost and response times of two cloud applications. We also explore how to extract knowledge about the effect that the cloud application context has on both service cost and quality of service so that the gained knowledge can be used in the service placement decision process. The experimental results demonstrate the ability of providing relevant information about the impact of cloud application context parameters on service cost and quality of service. The results also indicate the relevance of our approach for applications in preproduction phase since application providers can gain useful insights regarding service placement decision without acquiring extensive training datasets.