A cell outage management framework for dense heterogeneous networks

In this paper, we present a novel cell outage management (COM) framework for heterogeneous networks with split control and data planes-a candidate architecture for meeting future capacity, quality-of-service, and energy efficiency demands. In such an architecture, the control and data functionalities are not necessarily handled by the same node. The control base stations (BSs) manage the transmission of control information and user equipment (UE) mobility, whereas the data BSs handle UE data. An implication of this split architecture is that an outage to a BS in one plane has to be compensated by other BSs in the same plane. Our COM framework addresses this challenge by incorporating two distinct cell outage detection (COD) algorithms to cope with the idiosyncrasies of both data and control planes. The COD algorithm for control cells leverages the relatively larger number of UEs in the control cell to gather large-scale minimization-of-drive-test report data and detects an outage by applying machine learning and anomaly detection techniques. To improve outage detection accuracy, we also investigate and compare the performance of two anomaly-detecting algorithms, i.e., k-nearest-neighbor- and local-outlier-factor-based anomaly detectors, within the control COD. On the other hand, for data cell COD, we propose a heuristic Grey-prediction-based approach, which can work with the small number of UE in the data cell, by exploiting the fact that the control BS manages UE-data BS connectivity and by receiving a periodic update of the received signal reference power statistic between the UEs and data BSs in its coverage. The detection accuracy of the heuristic data COD algorithm is further improved by exploiting the Fourier series of the residual error that is inherent to a Grey prediction model. Our COM framework integrates these two COD algorithms with a cell outage compensation (COC) algorithm that can be applied to both planes. Our COC solution utilizes an actor-critic-based reinforcement learning algorithm, which optimizes the capacity and coverage of the identified outage zone in a plane, by adjusting the antenna gain and transmission power of the surrounding BSs in that plane. The simulation results show that the proposed framework can detect both data and control cell outage and compensate for the detected outage in a reliable manner

Quality and TQM at Higher Education Institutions in the UK: Lessons from the University of East London and the Aston University

The objective of this article is to investigate the level of implication of Quality in the University of East London and TQM in the Aston University. The elements of Quality and Accountability are the major driving forces in academic institutions in the UK, and in this respect, the total quality management (TQM) movement has exploded, capturing the attention of educators at all levels. Certainly, higher education embraces the concept of TQM as a set of tools for planning continuous improvement. In wider context, TQM have all sought to achieve fundamental change in organizations. The focuses of these two cases are implication of Quality and TQM programme in the University of East London and Aston University respectively.

A Systematic Review of the Existing Literature for the Evaluation of Sustainable Urban Projects

From the 21st century to the present(2021), a worldwide awareness that cities’ development must be based on projects for socio-economic growth and environmental protection is increasing. World governmental agencies and the European Union have suggested action strategies for the construction of «prototype cities» whose value must be founded on the inclusion and/or preservation of anthropic-natural elements and their effects on territories. In order to minimize the theoretical– practical gap between planning and project design with a view to sustainable development and the evaluation of their performance from economic, social and environmental points of view, the present contribution aims to outline a framework useful for systematizing the main scientific contributions concerning sustainability and the evaluation of urban transformation projects. The objective is pursued by analyzing bibliographic references with specific regard to the use of logical-operative methodologies used to rationalize the processes of interventions’ evaluation and selection. The task of examining the available literature is carried out with an investigation protocol of four sequential steps. From the implementation of the last one, the evidence expressing the heterogeneity of the examples in the literature is described. Accordingly, the theoretical-methodological framework for the project evaluation from an urban sustainability perspective is illustrated

Exploring systems that support good clinical care in Indigenous primary health-care services: a retrospective analysis of longitudinal systems assessment tool data from high-improving services

Background: Continuous quality improvement is a process for raising the quality of primary health care across Indigenous PHC services. In addition to clinical auditing using plan, do, study, act cycles, engaging staff in a process of reflecting on systems to support quality care is vital. The One21seventy Systems Assessment Tool (SAT) supports staff to assess systems performance in terms of five key components. This study examines quantitative and qualitative SAT data from five high-improving Indigenous primary health care services in northern Australia to understand the systems used to support quality care. Methods: High improving services selected for the study were determined by calculating quality of care indices for Indigenous health services participating in the ABCD National Research Partnership. Services that reported continuing high improvement in quality of care delivered across two or more audit tools in three or more audits were selected for the study. Pre-collected SAT data (from annual team SAT meetings) is presented longitudinally using radar plots for quantitative scores for each component and content analysis is used to describe strengths and weaknesses of performance in each systems component. Results: High improving services were able to demonstrate strong processes for assessing system performance and consistent improvement in systems to support quality care across components. Key strengths in the quality support systems included adequate and orientated workforce, appropriate health system supports and engagement with other organisations and community while the weaknesses included lack of service infrastructure, recruitment, retention and support for staff and additional costs. Qualitative data revealed clear voices from health service staff expressing concerns with performance and subsequent SAT data provided evidence of changes made to address concerns. Conclusion: Learning from the processes and strengths of high-improving services may be useful as we work with services striving to improve the quality of care provided in other areas

Reinforcement machine learning for predictive analytics in smart cities

The digitization of our lives cause a shift in the data production as well as in the required data management. Numerous nodes are capable of producing huge volumes of data in our everyday activities. Sensors, personal smart devices as well as the Internet of Things (IoT) paradigm lead to a vast infrastructure that covers all the aspects of activities in modern societies. In the most of the cases, the critical issue for public authorities (usually, local, like municipalities) is the efficient management of data towards the support of novel services. The reason is that analytics provided on top of the collected data could help in the delivery of new applications that will facilitate citizens’ lives. However, the provision of analytics demands intelligent techniques for the underlying data management. The most known technique is the separation of huge volumes of data into a number of parts and their parallel management to limit the required time for the delivery of analytics. Afterwards, analytics requests in the form of queries could be realized and derive the necessary knowledge for supporting intelligent applications. In this paper, we define the concept of a Query Controller ( QC ) that receives queries for analytics and assigns each of them to a processor placed in front of each data partition. We discuss an intelligent process for query assignments that adopts Machine Learning (ML). We adopt two learning schemes, i.e., Reinforcement Learning (RL) and clustering. We report on the comparison of the two schemes and elaborate on their combination. Our aim is to provide an efficient framework to support the decision making of the QC that should swiftly select the appropriate processor for each query. We provide mathematical formulations for the discussed problem and present simulation results. Through a comprehensive experimental evaluation, we reveal the advantages of the proposed models and describe the outcomes results while comparing them with a deterministic framework