Scotland as an Optimal Currency Area

Since the Scottish independence movement has reached the point that there will be a referendum on Scottish independence this September, the issue of whether the Scotland is optimal currency areas is very topical.In this paper we review the microeconomic foundations of an optimal currency area. We test these microeconomic foundations. We find that the UK, Scotland and the UK without Scotland meet the microeconomic criteria for a common currency area. While adopting a common currency is ultimately a political decision, these results imply that the broadest of these areas, the UK, is the optimal currency area in the sense of minimizing transactions costs.We do find differences in the UK less Scotland and Scotland economies in loan data. We further find that neither the euro bloc nor the euro bloc including Scotland meet the microeconomic criteria for a common currency area. In the event of a “yes” vote for Scottish independence, the immediate problem facing the Scottish government is to decide on an exchange rate regime that is seen as credible by the financial markets to avoid a flight of capital. How policymakers chooses between alternative exchange rate regimes is currently a topic for hot debate in central banking circles and the process of a monetary union breaking up is a fascinating area worthy of future research

Quality of care in University Hospitals in Saudi Arabia: systematic review

Objectives: To identify the key issues, problems, barriers and challenges particularly in relation to the quality of care in university hospitals in the Kingdom of Saudi Arabia (KSA), and to provide recommendations for improvement. Methods: A systematic search was carried out using five electronic databases, for articles published between January 2004 and January 2015. We included studies conducted in university hospitals in KSA that focused on the quality of healthcare. Three independent reviewers verified that the studies met the inclusion criteria, assessed the quality of the studies and extracted their relevant characteristics. All studies were assessed using the Institute of Medicine indicators of quality of care. Results: Of the 1430 references identified in the initial search, eight studies were identified that met the inclusion criteria. The included studies clearly highlight a need to improve the quality of healthcare delivery, specifically in areas of patient safety, clinical effectiveness and patient-centredness, at university hospitals in KSA. Problems with quality of care could be due to failures of leadership, a requirement for better management and a need to establish a culture of safety alongside leadership reform in university hospitals. Lack of instructions given to patients and language communication were key factors impeding optimum delivery of patient-centred care. Decisionmakers in KSA university hospitals should consider programmes and assessment tools to reveal problems and issues related to language as a barrier to quality of care. Conclusions: This review exemplifies the need for further improvement in the quality of healthcare in university hospitals in KSA. Many of the problems identified in this review could be addressed by establishing an independent body in KSA, which could monitor healthcare services and push for improvements in efficiency and quality of care

Design and Implementation of a Hybrid Face Recognition Technique

A thesis presented to the faculty of the Elmer R. Smith College of Business and Technology at Morehead State University in Partial Fulfillment of the requirements for the Degree Master of Science by Asim S. Chaudhry on October 16, 2018

Discovering Class-Specific Pixels for Weakly-Supervised Semantic Segmentation

We propose an approach to discover class-specific pixels for the weakly-supervised semantic segmentation task. We show that properly combining saliency and attention maps allows us to obtain reliable cues capable of significantly boosting the performance. First, we propose a simple yet powerful hierarchical approach to discover the class-agnostic salient regions, obtained using a salient object detector, which otherwise would be ignored. Second, we use fully convolutional attention maps to reliably localize the class-specific regions in a given image. We combine these two cues to discover class-specific pixels which are then used as an approximate ground truth for training a CNN. While solving the weakly supervised semantic segmentation task, we ensure that the image-level classification task is also solved in order to enforce the CNN to assign at least one pixel to each object present in the image. Experimentally, on the PASCAL VOC12 val and test sets, we obtain the mIoU of 60.8% and 61.9%, achieving the performance gains of 5.1% and 5.2% compared to the published state-of-the-art results. The code is made publicly available

A Novel 6G Conversational Orchestration Framework for Enhancing Performance and Resource Utilization in Autonomous Vehicle Networks

A vision of 6G aims to automate versatile services by eliminating the complexity of human effort for Industry 5.0 applications. This results in an intelligent environment with cognitive and collaborative capabilities of AI conversational orchestration that enable a variety of applications across smart Autonomous Vehicle (AV) networks. In this article, an innovative framework for AI conversational orchestration is proposed by enabling on-the-fly virtual infrastructure service orchestration for Anything-as-a-Service (XaaS) to automate a network service paradigm. The proposed framework will potentially contribute to the growth of 6G conversational orchestration by enabling on-the-fly automation of cloud and network services. The orchestration aspect of the 6G vision is not limited to cognitive collaborative communications, but also extends to context-aware personalized infrastructure for 6G automation. The experimental results of the implemented proof-of-concept framework are presented. These experiments not only affirm the technical capabilities of this framework, but also push into several Industry 5.0 applications

Moving forward in GME reform: a 4 + 1 model of resident ambulatory training

Traditional ambulatory training models have limitations in important domains, including opportunities for residents to learn, fragmentation of care delivery experience, and satisfaction with ambulatory experiences. New models of ambulatory training are needed. To compare the impact of a traditional ambulatory training model with a templated 4 + 1 model. A large university-based internal medicine residency using three different training sites: a patient-centered medical home, a hospital-based ambulatory clinic, and community private practices. Residents, faculty, and administrative staff. Development of a templated 4 + 1 model of residency where trainees do not attend to inpatient and outpatient responsibilities simultaneously. A mixed-methods analysis of survey and nominal group data measuring three primary outcomes: 1) Perception of learning opportunities and quality of faculty teaching; 2) Reported fragmentation of care delivery experience; 3) Satisfaction with ambulatory experiences. Self-reported empanelment was a secondary outcome. Residents\u27 learning opportunities increased (p = 0.007) but quality of faculty teaching was unchanged. Participants reported less fragmentation in the care residents provide patients in the inpatient and outpatient setting (p \u3c 0.0001). Satisfaction with ambulatory training improved (p \u3c 0.0001). Self-reported empanelment also increased (p \u3c 0.0001). Results held true for residents, faculty, and staff at all three ambulatory training sites (p \u3c 0.0001). A 4 + 1 model increased resident time in ambulatory continuity clinic, enhanced learning opportunities, reduced fragmentation of care residents provide, and improved satisfaction with ambulatory experiences. More studies of similar models are needed to evaluate effects on additional trainee and patient outcomes. (C) Society of General Internal Medicine 201

6G Vision: Towards Future Collaborative Cognitive Communication (3C) Systems

6G networks are expected to have a breakthrough by enabling the emergence of collaborative cognitive communication services over heterogeneous environments for industry 5.0 applications. These applications are required to adapt human-centric approach to make the most of human intuition and intelligence in Industry 4.0 automation.It calls for a transdisciplinarity research domain to investigate innovative systems with overlapping realms of Psychology, Sociology, Communication networks, Artificial Intelligence , Natural Language Processing and Collaborative Computing. The author at the Cognitive Systems Research Centre, London South Bank University has coined the expression “3C Systems" to refer to such artifacts which stands for "Collaborative Cognitive Communication Systems”. In this paper, an innovative framework for 3C Systems is proposed that is able to analyze and predict both the human as well as machine behaviors. It proactively diagnoses issues and recommends solutions without requiring any human intervention. The proposed concept of 3C Systems would potentially contribute towards 6G standardization. The automation and orchestration aspects of this research have variety of applications stretched across city infrastructures, retail, business, tourism, health, law, education and travel. A thorough insight to a broad view of 6G vision has been presented towards envisioned 3C Systems, while covering its enabling technologies. The experimental results for the proof of concept implementation has been presented. Results affirm the technical capabilities of the concept, to contribute to several industry 5.0 applications including, but not limited to holographic communication, self-driving vehicles, context-aware infrastructure and personalized interfaces

An Ontology for Submarine Feature Representation on Charts

A landform is a subjective individuation of a part of a terrain. Landform recognition is a difficult task because its definition usually relies on a qualitative and fuzzy description. Achieving automatic recognition of landforms requires a formal definition of the landforms properties and their modelling. In the maritime domain, the International Hydrographic Organisation published a standard terminology of undersea feature names which formalises a set of definition mainly for naming and communication purpose. This terminology is here used as a starting point for the definition of an ontology of undersea features and their automatic classification from a terrain model. First, an ontology of undersea features is built. The ontology is composed of an application domain ontology describing the main properties and relationships between features and a representation ontology deals with representation on a chart where features are portrayed by soundings and isobaths. A database model was generated from the ontology. Geometrical properties describing the feature shape are computed from soundings and isobaths and are used for feature classification. An example of automatic classification on a nautical chart is presented and results and on-going research are discussed