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The purpose of this paper is to present the application of logical framework analysis (LFA) for implementing continuous quality improvement (CQI) across multiple settings in a tertiary care hospital.

This study adopts a multiple case study approach. LFA is implemented within three diverse settings, namely, intensive care unit, surgical ward, and acute in-patient psychiatric ward. First, problem trees are developed in order to determine the root causes of quality issues, specific to the three settings. Second, objective trees are formed suggesting solutions to the quality issues. Third, project plan template using logical framework (LOGFRAME) is created for each setting.

This study shows substantial improvement in quality across the three settings. LFA proved to be effective to analyse quality issues and suggest improvement measures objectively.

This paper applies LFA in specific, albeit, diverse settings in one hospital. For validation purposes, it would be ideal to analyse in other settings within the same hospital, as well as in several hospitals. It also adopts a bottom-up approach when this can be triangulated with other sources of data.

LFA enables top management to obtain an integrated view of performance. It also provides a basis for further quantitative research on quality management through the identification of key performance indicators and facilitates the development of a business case for improvement.

LFA is a novel approach for the implementation of CQI programs. Although LFA has been used extensively for project development to source funds from development banks, its application in quality improvement within healthcare projects is scant.

The purpose of this study is to automatically generate a construction schedule by extracting data from the BIM (Building Information Modeling) model and combining an ontology constraint rule and a genetic algorithm (GA).

This study developed a feasible multi-phase framework to generate the construction schedule automatically through extracting information from the BIM, utilizing the ontology constraint rule to demonstrate the relationships between all the components and finally using the GA to generate the construction schedule.

To present the functionality of the framework, a prototype case is adopted to show the whole procedure, and the results show that the scheme designed in this study can quickly generate the schedule and ensure that it can satisfy the requirements of logical constraints and time parameter constraints.

A proper utilization of conceptual framework can contribute to the automatic generation of construction schedules and significantly reduce manual errors in the Architectural, Engineering, and Construction (AEC) industry. Moreover, a scheme of BIM-based ontology and GA for construction schedule generation may reduce additional manual work and improve schedule management performance.

The hybrid approach combines the ontology constraint rule and GA proposed in this study, and it is an effective attempt to generate the construction schedule, which provides a direct indicator for the schedule control of the project.

In this study, the data application process of the BIM model is divided into four modules: extraction, processing, optimization, and output. The key technologies including secondary development, ontology theory, and GA are introduced to develop a multi-phase framework for the automatic generation of the construction schedule and to realize the schedule prediction under logical constraints and duration interference.

The purpose of this paper is to design a unified operational design domain (ODD) monitoring framework for mitigating Safety of the Intended Functionality (SOTIF) risks triggered by vehicles exceeding ODD boundaries in complex traffic scenarios.

A unified model of ODD monitoring is constructed, which consists of three modules: weather condition monitoring for unusual weather conditions, such as rain, snow and fog; vehicle behavior monitoring for abnormal vehicle behavior, such as traffic rule violations; and road condition monitoring for abnormal road conditions, such as road defects, unexpected obstacles and slippery roads. Additionally, the applications of the proposed unified ODD monitoring framework are demonstrated. The practicability and effectiveness of the proposed unified ODD monitoring framework for mitigating SOTIF risk are verified in the applications.

First, the application of weather condition monitoring demonstrates that the autonomous vehicle can make a safe decision based on the performance degradation of Lidar on rainy days using the proposed monitoring framework. Second, the application of vehicle behavior monitoring demonstrates that the autonomous vehicle can properly adhere to traffic rules using the proposed monitoring framework. Third, the application of road condition monitoring demonstrates that the proposed unified ODD monitoring framework enables the ego vehicle to successfully monitor and avoid road defects.

The value of this paper is that the proposed unified ODD monitoring framework establishes a new foundation for monitoring and mitigating SOTIF risks in complex traffic environments.

This paper provides an overview of a form of factor analysis, Q Methodology, and suggests how it might be applied in an institutional analysis setting. Q Methodology provides for a middle ground between positivist and phenomenological methods, and that its usage will not necessarily result in overly contextualized findings that render generalization impossible. The paperʼs primary focus is to suggest several uses of Q Methodology within different established policy studies frameworks, namely the Institutional Analysis and Development (IAD) Framework, the Advocacy Coalition Framework (ACF), and the policy process as conceptualized by Lasswellʼs Policy Sciences approach.

The purpose of this paper is to describe a generic software framework for development of agricultural and forestry robots. The primary goal is to provide generic high‐level functionality and to encourage distributed and structured programming, thus leading to faster and simplified development of robots. A secondary goal is to investigate the value of several architecture views when describing different software aspects of a robotics system.

The framework is constructed with a hybrid robot architecture, with a static state machine that implements a flow diagram describing each specific robot. Furthermore, generic modules for GUI, resource management, performance monitoring, and error handling are included. The framework is described with logical, development, process, and physical architecture views.

The multiple architecture views provide complementary information that is valuable both during and after the design phase. The framework has been shown to be efficient and time saving when integrating work by several partners in several robotics projects. Although the framework is guided by the specific needs of harvesting agricultural robots, the result is believed to be of general value for development also of other types of robots.

In this paper, the authors present a novel generic framework for development of agricultural and forestry robots. The robot architecture uses a state machine as replacement for the planner commonly found in other hybrid architectures. The framework is described with multiple architecture views.

Context‐awareness is an essential property of any pervasive system perceiving its environment. Such a system captures and processes context, i.e. the features describing the relevant aspects of environment state and user behaviour. However, development of these systems still requires solving a number of research and engineering challenges. The purpose of this paper is to propose perception framework, a RESTful middleware which simplifies and accelerates the development of pervasive systems. Perception framework allows constructing services' application logic using rules and context. Moreover, it collects sensor data and produces the context information that is required for the rules. The authors present the architecture, design, complete implementation, and prototype‐based verification of perception framework.

Development of context‐aware services is achieved with a novel architecture supporting building of the logic of web services using rules which directly manipulate the available elementary context represented with the Web Ontology Language (OWL) ontology. These rules are described using the Rule Interchange Format (RIF) with support for different rule languages. The implementation of this framework is aligned with RESTful principles, providing a lightweight and flexible solution for large‐scale context‐aware systems.

The fully implemented prototype verifies the feasibility of constructing the logic of context‐aware web services with the rules supported by perception framework.

The contributions of this paper include: the requirement specification for a generic context‐aware pervasive middleware; and the design and implementation of the framework (i.e. perception framework) supporting the development of context‐aware web services. The perception framework includes a generic rule‐based reasoner allowing developers to use several RIF‐compliant rule description languages.

In this monograph the author discusses the problems in constructing a logical and ethical‐empirical foundation so that relevant social values may be studied by the scientific method. Part One is concerned with the difficulties posed by the prevailing methodology. Part Two presents a new research programme based on the simultaneous equilibrium versus disequilibrium approach in conjunction with Wittgenstein's logic and the current research in ethics.

The purpose of the chapter is to develop the pragmatic logical framework for safeguarding successful Public–Private Partnership (PPP) implementations in water supply in emerging markets. The case analysis related to the research question of how efficacy of PPP implementations could be improved revealed considerable shortfalls within the current modus operandi. The research was limited to urban water utilities of Kazakhstan, Kyrgyzstan, Tajikistan and Uzbekistan with implications most relevant for emerging markets conditions. The proposed logical framework could improve efficacy and sustainability of PPP undertakings in water supply in developing countries. The framework is centred on the simple question: ‘What would constitute a meaningful quid pro quo within the envisioned PPP arrangement to eligible counterparts?’ The framework would necessitate properly answering many complex and uncomfortable questions of PPP arrangements, especially in terms of performance management, public accountability and underlying benefits to the parties. PPP in water utilities is a popular notion amongst governments and the international financial institutions (IFIs). PPP is commonly considered to be a tool for providing an optimal solution to chronic problems of water utilities in terms of underperformance and underinvestment. In-spite of massive efforts of modernization and institutional upgrade of the water utilities in Central Asia, success rate with PPP modalities is still rather low.

Defects in concrete surfaces are inevitably recurring during construction, which needs to be checked and accepted during construction and completion. Traditional manual inspection of surface defects requires inspectors to judge, evaluate and make decisions, which requires sufficient experience and is time-consuming and labor-intensive, and the expertise cannot be effectively preserved and transferred. In addition, the evaluation standards of different inspectors are not identical, which may lead to cause discrepancies in inspection results. Although computer vision can achieve defect recognition, there is a gap between the low-level semantics acquired by computer vision and the high-level semantics that humans understand from images. Therefore, computer vision and ontology are combined to achieve intelligent evaluation and decision-making and to bridge the above gap.

Combining ontology and computer vision, this paper establishes an evaluation and decision-making framework for concrete surface quality. By establishing concrete surface quality ontology model and defect identification quantification model, ontology reasoning technology is used to realize concrete surface quality evaluation and decision-making.

Computer vision can identify and quantify defects, obtain low-level image semantics, and ontology can structurally express expert knowledge in the field of defects. This proposed framework can automatically identify and quantify defects, and infer the causes, responsibility, severity and repair methods of defects. Through case analysis of various scenarios, the proposed evaluation and decision-making framework is feasible.

This paper establishes an evaluation and decision-making framework for concrete surface quality, so as to improve the standardization and intelligence of surface defect inspection and potentially provide reusable knowledge for inspecting concrete surface quality. The research results in this paper can be used to detect the concrete surface quality, reduce the subjectivity of evaluation and improve the inspection efficiency. In addition, the proposed framework enriches the application scenarios of ontology and computer vision, and to a certain extent bridges the gap between the image features extracted by computer vision and the information that people obtain from images.