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This paper describes the investigations undertaken at Balgray Reservoir after concerns were raised regarding the condition of the heavily encrusted original cast-iron pipework, which dates from 1853. Testing of the thickness and hardness of a section of the pipe as well as testing of the encrustation was used to determine the materials present. The results of these investigations were used to determine the implications for the condition of the pipe and the resulting risks posed to the dam. This informed the potential options for the required remediation works. Details are provided for the case study, the investigations and findings, and potential options for encrustation removal.

Diathesis alternation describes the property of language that individual verbs can be used in different subcategorization frames. However, seemingly similar verbs such as drizzle and spray can behave differently in terms of the alternations they can participate in (drizzle/spray water on the plant; *drizzle/spray the plant with water). By hypothesis, primary linguistic data is not sufficient to learn which verbs alternate and which do not. We tested two state-of-the-art machine learning models trained by self supervision, and found little evidence that they could learn the correct pattern of acceptability judgement in the locative alternation. This is consistent with a poverty of stimulus argument that primary linguistic data does not provide sufficient information to learn aspects of linguistic knowledge. The finding has important consequences for machine learning models trained by self supervision, since they depend on the evidence present in the raw training input.

To ensure safe and efficient operations, all offshore operations follow a plan devised to take into account current operation conditions and identify the optimum workflow with the minimum risk potential. Previously, planners had to manually consult eight data sources, each with a separate UI, and summarise the plan in a.pdf document. Equinor's Operation Planning Tool (OPT) has been developed to easily present the planners with the technical conditions of a platform, identify potentially dangerous combinations of concurrent activities, and propose learnings from eight years’ worth of incident recordings – all relevant to the current list of planned activities. The tool aims to answer questions such as ‘are other activities planned for the same time which would make this activity unsafe?’ or ‘have incidents previously occurred whilst performing similar tasks on this equipment type?’. This paper details the development of the OPT with a particular focus on the application of Natural Language Understanding for extracting equipment types and tasks involved in previous incidents and relating these to planned activities. Utilising natural language processing techniques, a system has been developed that mines the content of Equinor's incident database, and assigns context to incidents, by identifying the systems, activities and equipment involved and the conditions on the asset at the time of the incident. The same context is also discovered from the content of planned activities. These key concepts are organised into a knowledge graph synthesising Equinor's institutional safety and operational experience. The OPT has reduced time spent planning by providing a single interface detailing a plant's technical conditions, all planned work orders and relevant lessons learned from previous incidents. By reducing the reliance on personal experience, the tool has provided subjectively improved risk identification and handling, plus faster knowledge transfer to new employees as well as focussed cross-platform knowledge sharing. The success of the tool highlights the strength of combining data and leveraging the vast quantities of historic data available both in unstructured and structured forms to create a safe, offshore work environment.

While research on project failure tends to supply lists of risk and failure factors, and negative business effects, the objective of this research is to identify specific factors behind failure and who is to blame. We analyze practitioners' perspectives on 57 development and management factors for projects they considered failures across 70 failed projects. Results show that all projects reviewed suffered 6 to 48 failure factors. While no single set of factors is identified, nearly all projects suffered from organizational factors outside the project manager's control. We conclude with suggestions to minimize the most common ones.

PurposeBy providing interoperability users can be supported in sharing and reusing vocabularies and knowledge. Ontology alignment plays an important role in the context of semantic interoperability. Usually ontology alignment tools generate results that are difficult to understand or assess. In order to enable users to check and improve alignment results and to understand their consequences information visualization techniques are used. The purpose of this paper is to discuss the relevant quality aspects in ontology alignment as well as current activities and available tools.Design/methodology/approachBased on a literature study quality measures for ontology alignment identified and requirements for visual ontology alignment are defined. As a proof of concepts a prototype called AlViz was developed.FindingsInformation visualization offers appropriate methods for the assessment of ontology alignment results. Different levels of detail and overview help users to navigate and understand the alignments. The assessment of semi‐structured resources by users involves learning activities. The neighborhood of the entity under investigation bears relevant semantic information. Therefore, assessment may include crisscrossing acquisition of knowledge representations and their semantics.Originality/valueAlong a comprehensive framework alignment assessment tasks are identified and visualization tool is introduced and applied which aims at making ontology alignment results manageable and comprehensible.

A spreadsheet-based tool for whole-life carbon dioxide accounting of soil remediation projects has been created. The tool carries out whole-life analysis of projects, including supply chain emissions. It was applied to the Glasgow 2014 Commonwealth Games Athletes' Village remediation project, for which a calculated total ‘carbon footprint’ of 2328 t of carbon dioxide equivalent emission (tCO2e) was obtained. This is 71 tCO2e/ha of the site or 13·3 kgCO2e/t whole life of soil treated. These figures are not comparable with those reported for other projects, which have typically not included supply chain emissions. Fuel use was the main contributor to emissions, but the contribution made by staff transport and carbon dioxide embodied in construction plant was also found to be significant. A comparison was made with an excavate and disposal (E&D) approach, which required considerable use of estimation for the hypothetical E&D. However, it was determined that the carbon footprint of E&D may have been 14% higher than the soil washing actually used. It was concluded that fuel efficiency would be key to future reduction of the carbon footprint of remediation projects, that the accounting tool would be useful for ongoing project management, and its application over time could lead to a database of values for optioneering at the process design stage.

Recently semantic web technologies, such as ontologies, have been proposed as key enablers for integrating heterogeneous data schemas in business and governmental systems. Algorithms designed to align different but related ontologies have become necessary as differing ontologies proliferate. The process of ontology alignment seeks to find corresponding entities in a second ontology with the same or the closest meaning for each entity in a single ontology. This research is motivated by the need to provide tools and techniques to support the task of validating ontology alignment statements, since it cannot be guaranteed that the results from automated tools are accurate. The authors present a framework for understanding ontology alignment quality and describe how AlViz, a tool for visual ontology alignment, may be used to improve the quality of alignment results. An experiment was undertaken to test the claim that AlViz supports the task of validating ontology alignments. A promising result found that the tool has potential for identifying missing alignments and for rejecting false alignments.

Inpatient rehabilitation is an integral part of the continuum of care for people with severe acquired brain injury (ABI), and may extend for weeks or months. However, there is a lack of research describing the experience of inpatient brain injury rehabilitation. The purpose of this study was to describe and interpret the inpatient brain injury rehabilitation experience from the perspective of patients and their caregivers. Qualitative content analysis of semi-structured interviews from a purposeful sample of 20 patients and 18 of their family caregivers generated in-depth descriptions of the inpatient experience. Three themes were identified; "rehabilitation context/environment", "activity/occupation", and "support and adjustment". The findings provide a greater understanding of rehabilitation from an insider perspective and can assist rehabilitation teams to better meet the needs of people with ABI. Specifically, the need for a therapeutic environment, meaningful occupations and family-centredness in inpatient settings were highlighted by this study.

B-SCP (Business Strategy, Context, and Process) is a promising framework addressing alignment of IT with business strategy from a requirements engineering perspective. The B-SCP approach combines goal and context modeling, and business processes, into a generic modeling framework that deconstructs these to IT requirements and context. However, a problem with the B-SCP framework is that it is difficult to track dependencies between requirements in a project of realistic complexity. To address this we discuss how the RDF (Resource Description Framework) data model with OWL (Web Ontology Language) semantics will greatly benefit an implementation using B-SCP. Our contribution is to extend B-SCP by describing an ontology data structure for representing the requirements and the complex rules which map them together. The benefit in our approach is that it provides a comprehensive way to validate the decomposition of the requirements. Seven–Eleven Japan is used as an exemplar to demonstrate improved productivity and consistency of B-SCP.

Without careful methodological guidance, case studies in software engineering are difficult to plan, design and execute. While there are a number of broad guidelines for case study research, there are none that specifically address the needs of a software engineer undertaking multiple case studies in an industrial setting. Through a synthesis of existing best practices in case study research, we provide a set of comprehensive guidelines for conducting multiple case studies in software engineering research. Our guidelines can assist software engineering researchers with all stages of multiple case study research, although in this paper we concentrate on the early phases, such as focusing the case study and detailed plan design. To date, three exploratory research projects found our guidelines very useful. We illustrate our guidelines with examples from one of these projects.

The Environment Web (EW) is the official database for emissions and discharges from the offshore oil and gas industry in Norway. Currently we are working on a project to describe and align terms in the EW application according to the ISO 15926 Reference Data Library (RDL). One of our goals is to ensure that the EW terms in the RDL can be used as a reference point for all systems using EW terms. Therefore the definitions of the EW constructs in the RDL need to be unambiguous. Moreover, another goal is to enrich the ISO 15926 ontology by adding terms from the EW domain.We briefly describe our experiences and challenges faced so far when mapping terms from the EW database to the ISO15926 Ontology.

With a literature study we found an enormous number of ontology visualization tools. Many of them apply graph visualization but there are other approaches as well. We have identified interesting solutions for dealing with the complexity of large ontologies. Ontology engineering, ontology mapping and alignment can benefit from Information Visualization. Our collection is a starting point to demonstrate the usefulness of Information Visualization techniques, however, a detailed evaluation would be the next step to consolidate this research area and help to boost the adoption of ontologies in common Web applications.

It has been suggested that there is more than one reason for a software development project to fail. However, most of the literature that discusses project failure tends to be rather general, supplying us with lists of risk and failure factors, and focusing on the negative business effects of the failure. Very little research has attempted an in-depth investigation of a number of failed projects to identify exactly what are the factors behind the failure. In this research we analyze data from 70 failed projects. This data provides us with practitionerspsila perspectives on 57 development and management factors for projects they considered were failures. Our results show that all projects we investigated suffered from numerous failure factors. For a single project the number of such factors ranges from 5 to 47. While there does not appear to be any overarching set of failure factors we discovered that all of the projects suffered from poor project management. Most projects additionally suffered from organizational factors outside the project managerpsilas control. We conclude with suggestions for minimizing the four most common failure factors.

Semantic Web applications often rely on the reuse of underlying data from legacy information systems. The motivation for our research is how to make this data accessible for use in such applications. We propose the use of ontologies and conceptual modelling in an overall approach for data interoperability. The approach incorporates a process for ontology alignment using our tool for visual ontology alignment. First results from our tool evaluation were promising and show that visualization is useful for locating additional candidate alignments by an end user.

Conservation of water quality is inherently tied to watershed management. Efforts to proect Lake Baikal have increasingly focused on the Selenge River, a major tributary, with more than half its watershed area in Mongolia. Placer gold mining in Mongolia has the potential to load total suspended sediment (TSS), and total phosphorus (TP) into Lake Baikal and destroy spawning areas for the endangered Taimen salmon (Hucho taimen taimen). This work describes water quality assessments performed from 2001 to 2003 on Mongolian tributaries to the Selenge River. Of 7 rivers sampled, rivers with proximal mining had the worst water quality. Elevated loading of TSS and TP was observed below mining regions on the Tuul River. Flooding could breach thin strips of land separating dredge pits from river channels, resulting in massive sediment loading. Extensive disturbance of the river terrace was apparent for many square kilometers. In the mountainous headwaters of the Yeroo River, tributary drainages undergoing mining had TP concentrations 8 to 15 times higher than the main stem. TSS was 7 to 12 times higher, and turbidity was 8 times higher. Alternative mining technologies exist that could minimize impact and improve the possibility for reclamation.

Explores the importance of semantic integrity during data warehouse design and its impact on the successful use of the implemented warehouse. This was achieved through a detailed case study. Consequently, a conceptual framework for describing inter-subjective meaning in data modelling has been developed. The purpose of the framework is to provide a theoretical basis for explaining how a data model is interpreted through the meaning levels of understanding, connotation and generation, and also how a data model is created from an existing meaning structure by intention, generation and action. This paper also describes inhibiting factors for understanding the physical data model based on the case study findings. Furthermore strategies are suggested to address the factors inhibiting the generation of meaning from a data model, in particular for improving understanding. The result of the exploration is the recognition that the implementation of a data warehouse may not assist with providing a detailed understanding of the semantic content of the data warehouse.