Adequacy of ecosystem services assessment tools and approaches to current policy needs and gaps in the European Union Overseas entities

The paper presents the current policy needs and gaps identified in the European Union (EU) Outermost Regions and Overseas Countries and Territories to implement Mapping and Assessment of Ecosystem and their Services (MAES) methodology. Then, a selection of the most appropriate tools and methods for mapping and assessing ecosystem services (biophysical, economic, socio-cultural – and decision-support) is provided to address local needs. Using a performance matrix to assess the effectiveness, efficiency and sustainability of these tools, key factors required to facilitate the implementation of an ecosystem services framework are identified by considering local needs and possibilities in terms of data availability, mapping support, ecosystem services assessment and decision-support. Our results show how effective and accurate various methods (e.g. process-based models, integrated modelling and most Decision-Supporting Tools) can be, or how efficient other methods are (e.g. value transfer, spatial proxy methods and replacement cost) in data-scarce regions. Participatory approaches score well in terms of sustainability as they allow the assessment of multiple ecosystem services (covering the biophysical, economic and social-cultural components of the assessment) with local stakeholders' contribution, therefore contributing to the awareness-raising dimension. There is no one-size-fits-all approach. Instead, there is a need for flexible, guidance-based ecosystem services mapping and assessment approaches in the EU Overseas entities to facilitate MAES implementation and to adapt and integrate those methods into scenario analysis and decision-supporting tools for better uptake of MAES outputs at the decision-making and policy levels in the EU Overseas entities.

Exploring Solutions and the Role of GDS Technology in Crossing the Current Pandemic Context in Tourism

In the current pandemic context, tourists increasingly need to receive assurances from the tourism and hospitality industry that their safety and health will not be endangered during their trip or their stay. Tourists increasingly prefer to plan their own trips and information technology is playing a very important role in the hotel and tourism industry. This study uses strategic management methods and comparative analysis to formulate strategies, measures, and solutions to maintain the safety and the health of tourists at the level of the tourism and hospitality industry. The aim of the paper is to provide solutions for the tourism industry in the current context of the COVID-19 pandemic, through a strategic managerial approach and by involving information technology and global distribution systems (GDSs) as a decision supporting tool. The results of the study are useful for small and medium enterprises to act efficiently in this pandemic context, but are also useful for GDS providers to reflect on possible improvements in system functionality to implement solutions and to support the decisions of tourists.

Current Applications of Machine Learning in Spine: From Clinical View

Study Design: Narrative review. Objectives: This review aims to present current applications of machine learning (ML) in spine domain to clinicians. Methods: We conducted a comprehensive PubMed search of peer-reviewed articles that were published between 2006 and 2020 using terms (spine, spinal, lumbar, cervical, thoracic, machine learning) to examine ML in spine. Then exclude research of other domain, case report, review or meta-analysis, and which without available abstract or full text. Results: Total 1738 articles were retrieved from database, and 292 studies were finally included. Key findings of current applications were compiled and summarized in this review. Main clinical applications of those techniques including image processing, diagnosis, decision supporting, operative assistance, rehabilitation, surgery outcomes, complications, hospitalization and cost. Conclusions: ML had achieved excellent performance and hold immense potential in spine. ML could help clinical staff to improve medical level, enhance work efficiency, and reduce adverse events. However more randomized controlled trials and improvement of interpretability are essential to clinicians accepting models’ assistance in real work.

Development of a Margin Determination Framework for Tumor-Tracking Radiation Therapy With Intraoperatively Implanted Fiducial Markers

PurposeTo develop an internal target volume (ITV) margin determination framework (or decision-supporting framework) for treating multiple lung metastases using CyberKnife Synchrony with intraoperatively implanted fiducial markers (IIFMs). The feasibility of using non-ideally implanted fiducial markers (a limited number and/or far from a target) for tracking-based lung stereotactic ablative radiotherapy (SABR) was investigated.MethodsIn the developed margin determination framework, an optimal set of IIFMs was determined to minimize a tracking uncertainty-specific ITV (ITVtracking) margin (margin required to cover target-to-marker motion discrepancy), i.e., minimize the motion discrepancies between gross tumor volume (GTV) and the selected set of fiducial markers (FMs). The developed margin determination framework was evaluated in 17 patients with lung metastases. To automatically calculate the respiratory motions of the FMs, a template matching-based FM tracking algorithm was developed, and GTV motion was manually measured. Furthermore, during-treatment motions of the selected FMs were analyzed using log files and compared with those calculated using 4D CTs.ResultsFor 41 of 42 lesions in 17 patients (97.6%), an optimal set of the IIFMs was successfully determined, requiring an ITVtracking margin less than 5 mm. The template matching-based FM tracking algorithm calculated the FM motions with a sub-millimeter accuracy compared with the manual measurements. The patient respiratory motions during treatment were, on average, significantly smaller than those measured at simulation for the patient cohort considered.ConclusionUse of the developed margin determination framework employing CyberKnife Synchrony with a limited number of IIFMs is feasible for lung SABR.

Probability elicitation using geostatistics in hydrocarbon exploration

The exploratory phase of a hydrocarbon field is a period when decision-supporting information is scarce while the drilling stakes are high. Each new prospect drilled brings more knowledge about the area and might reveal reserves, hence choosing such prospect is essential for value creation. Drilling decisions must be made under uncertainty as the available geological information is limited and probability elicitation from geoscience experts is key in this process. This work proposes a novel use of geostatistics to help experts elicit geological probabilities more objectively, especially useful during the exploratory phase. The approach is simpler, more consistent with geologic knowledge, more comfortable for geoscientists to use and, more comprehensive for decision-makers to follow when compared to traditional methods. It is also flexible by working with any amount and type of information available. The workflow takes as input conceptual models describing the geology and uses geostatistics to generate spatial variability of geological properties in the vicinity of potential drilling prospects. The output is stochastic realizations which are processed into a joint probability distribution (JPD) containing all conditional probabilities of the process. Input models are interactively changed until the JPD satisfactory represents the expert’s beliefs. A 2D, yet realistic, implementation of the workflow is used as a proof of concept, demonstrating that even simple modeling might suffice for decision-making support. Derivative versions of the JPD are created and their effect on the decision process of selecting the drilling sequence is assessed. The findings from the method application suggest ways to define the input parameters by observing how they affect the JPD and the decision process.

Conceptual Framework for Insomnia: A Cognitive Model in Practice

Insomnia is a widespread neuropsychological sleep-related disorder known to result in various predicaments including cognitive impairments, emotional distress, negative thoughts, and perceived sleep insufficiency besides affecting the incidence and aggravation of other medical disorders. Despite the available insomnia-related theoretical cognitive models, clinical studies, and related guidelines, an evidence-based conceptual framework for a personalized approach to insomnia seems to be lacking. This study proposes a conceptual cognitive framework (CCF) providing insight into cognitive mechanisms involved in the predisposition, precipitation, and perpetuation of insomnia and consequent cognitive deficits. The current CCF for insomnia relies on evaluative conditional learning and appraisal which generates negative valence (emotional value) and arousal (cognitive value). Even with the limitations of this study, the suggested methodology is well-defined, reproducible, and accessible can help foster future high-quality clinical databases. During clinical insomnia but not the neutral one, negative mood (trait-anxiety) causes cognitive impairments only if mediating with a distorted perception of insomnia (Ind-1 = 0.161, 95% CI 0.040–0.311). Further real-life testing of the CCF is intended to formulate a meticulous, decision-supporting platform for clinical interventions. Furthermore, the suggested methodology is expected to offer a reliable platform for CCF-development in other cognitive impairments and support the causal clinical data models. It may also improve our knowledge of psychological disturbances and complex comorbidities to help design rehabilitation interventions and comprehensive frameworks in line with the “preventive medicine” policies.

Knowledge visualisation for construction procurement decision-making: a process innovation

PurposeIn the construction sector, the knowledge-based process outgrows its emphasis on technological aspects. Yet, there is a lack of applied studies showing how a procurement system (PS) could be selected in the digital age. In particular, there is a radical need to establish an innovative process to visualise novel PS decision. Therefore, this paper aims to present a knowledge visualised framework for aiding construction PS decision-making.Design/methodology/approachThis paper describes the construction of process innovation. The framework (process) is supported by four influential decision supporting methods (mean utility values, analytic hierarchy process, fuzzy set theory and Delphi method) and computer programming (Matlab).FindingsThere are four stages of this framework: (1) uniform rating for decision alternatives; (2) group decision for determining the decision attribute; (3) determining the final choice; (4) reporting the cognitive computing process. Supported by individual and groups decision dynamics, this framework emphasises how the dashboard aided innovative approach enables the induction of understanding, cognitive computing for decision-making and how the information would precisely be represented, which are vital requirements of modern construction.Originality/valueThe contribution of this paper presents two leverage points that support the modern PS decision. Firstly, this paper provides a holistic view of the decision supporting methods on the basis of how a suitable PS would be systematically sought. Based on the existing studies, this paper upgrades into a visualised knowledge decision supporting process. It helps the participants understand and improve their cognitive learning. Secondly, this framework allows the participants to have a view of the individual and group decisions. It sheds light on the development of the collaborative decision-making process.

Automatic information retrievement for exporting services: First project findings from the development of an AI based export decision supporting instrument

On the servitization journey, manufacturing companies complement their offerings with new industrial and knowledge-based services, which causes challenges of uncertainty and risk. In addition to the required adjustment of internal factors, the international selling of services is a major challenge. This paper presents the initial results of an international research project aimed at assisting advanced manufacturers in making decisions about exporting their service offerings to foreign markets. In the frame of this project, a tool is developed to support managers in their service export decisions through the automated generation of market information based on Natural Language Processing and Machine Learning. The paper presents a roadmap for progressing towards an Artificial Intelligence-based market information solution. It describes the research process steps of analyzing problem statements of relevant industry partners, selecting target countries and markets, defining parameters for the scope of the tool, classifying different service offerings and their components into categories and developing annotation scheme for generating reliable and focused training data for the Artificial Intelligence solution. This paper demonstrates good practices in essential steps and highlights common pitfalls to avoid for researcher and managers working on future research projects supported by Artificial Intelligence. In the end, the paper aims at contributing to support and motivate researcher and manager to discover AI application and research opportunities within the servitization field.

Decision-Making Method Employed at Construction Planning Phase of Housing Development

A correct decision made by decision-makers, at the planning phase, determines the success of a housing development project. Detailed decision supporting data is needed to provide specific guidance to private housing developers. The purpose of this paper is to define a decision-making method to be employed at the construction planning phase of a housing development project in Malaysia. Survey method is implemented using a questionnaire that was distributed to 67 private housing developers. The collected data was then analysed via Statistical Package for Social Sciences (SPSS) software. The outcomes of the analysis show that for Malaysian private housing developers, discussion and market/economic report are the most important methods that are applied while making a decision for a housing development project. Thus, this paper is supposed to guide private housing developers as well as governments in making decisions at the construction planning phase of housing development projects in Malaysia.