11,253 research outputs found
Recommended from our members
Ensuring Access to Safe and Nutritious Food for All Through the Transformation of Food Systems
Bayesian networks for disease diagnosis: What are they, who has used them and how?
A Bayesian network (BN) is a probabilistic graph based on Bayes' theorem,
used to show dependencies or cause-and-effect relationships between variables.
They are widely applied in diagnostic processes since they allow the
incorporation of medical knowledge to the model while expressing uncertainty in
terms of probability. This systematic review presents the state of the art in
the applications of BNs in medicine in general and in the diagnosis and
prognosis of diseases in particular. Indexed articles from the last 40 years
were included. The studies generally used the typical measures of diagnostic
and prognostic accuracy: sensitivity, specificity, accuracy, precision, and the
area under the ROC curve. Overall, we found that disease diagnosis and
prognosis based on BNs can be successfully used to model complex medical
problems that require reasoning under conditions of uncertainty.Comment: 22 pages, 5 figures, 1 table, Student PhD first pape
Quantifying and Explaining Machine Learning Uncertainty in Predictive Process Monitoring: An Operations Research Perspective
This paper introduces a comprehensive, multi-stage machine learning
methodology that effectively integrates information systems and artificial
intelligence to enhance decision-making processes within the domain of
operations research. The proposed framework adeptly addresses common
limitations of existing solutions, such as the neglect of data-driven
estimation for vital production parameters, exclusive generation of point
forecasts without considering model uncertainty, and lacking explanations
regarding the sources of such uncertainty. Our approach employs Quantile
Regression Forests for generating interval predictions, alongside both local
and global variants of SHapley Additive Explanations for the examined
predictive process monitoring problem. The practical applicability of the
proposed methodology is substantiated through a real-world production planning
case study, emphasizing the potential of prescriptive analytics in refining
decision-making procedures. This paper accentuates the imperative of addressing
these challenges to fully harness the extensive and rich data resources
accessible for well-informed decision-making
Efficiency measurement based on novel performance measures in total productive maintenance (TPM) using a fuzzy integrated COPRAS and DEA method
Total Productive Maintenance (TPM) has been widely recognized as a strategic tool and lean manufacturing practice for improving manufacturing performance and sustainability, and therefore it has been successfully implemented in many organizations. The evaluation of TPM efficiency can assist companies in improving their operations across a variety of dimensions. This paper aims to propose a comprehensive and systematic framework for the evaluation of TPM performance. The proposed total productive maintenance performance measurement system (TPM PMS) is divided into four phases (e.g., design, evaluate, implement, and review): i) the design of new performance measures, ii) the evaluation of the new performance measures, iii) the implementation of the new performance measures to evaluate TPM performance, and iv) the reviewing of the TPM PMS. In the design phase, different types of performance measures impacting TPM are defined and analyzed by decision-makers. In the evaluation phase, novel performance measures are evaluated using the Fuzzy COmplex Proportional Assessment (FCOPRAS) method. In the implementation phase, a modified fuzzy data envelopment analysis (FDEA) is used to determine efficient and inefficient TPM performance with novel performance measures. In the review phase, TPM performance is periodically monitored, and the proposed TPM PMS is reviewed for successful implementation of TPM. A real-world case study from an international manufacturing company operating in the automotive industry is presented to demonstrate the applicability of the proposed TPM PMS. The main findings from the real-world case study showed that the proposed TPM PMS allows measuring TPM performance with different indicators especially soft ones, e.g., human-related, and supports decision makers by comparing the TPM performances of production lines and so prioritizing the most important preventive/predictive decisions and actions according to production lines, especially the ineffective ones in TPM program implementation. Therefore, this system can be considered a powerful monitoring tool and reliable evidence to make the implementation process of TPM more efficient in the real-world production environment
A Design Science Research Approach to Smart and Collaborative Urban Supply Networks
Urban supply networks are facing increasing demands and challenges and thus constitute a relevant field for research and practical development. Supply chain management holds enormous potential and relevance for society and everyday life as the flow of goods and information are important economic functions. Being a heterogeneous field, the literature base of supply chain management research is difficult to manage and navigate. Disruptive digital technologies and the implementation of cross-network information analysis and sharing drive the need for new organisational and technological approaches. Practical issues are manifold and include mega trends such as digital transformation, urbanisation, and environmental awareness.
A promising approach to solving these problems is the realisation of smart and collaborative supply networks. The growth of artificial intelligence applications in recent years has led to a wide range of applications in a variety of domains. However, the potential of artificial intelligence utilisation in supply chain management has not yet been fully exploited. Similarly, value creation increasingly takes place in networked value creation cycles that have become continuously more collaborative, complex, and dynamic as interactions in business processes involving information technologies have become more intense.
Following a design science research approach this cumulative thesis comprises the development and discussion of four artefacts for the analysis and advancement of smart and collaborative urban supply networks. This thesis aims to highlight the potential of artificial intelligence-based supply networks, to advance data-driven inter-organisational collaboration, and to improve last mile supply network sustainability. Based on thorough machine learning and systematic literature reviews, reference and system dynamics modelling, simulation, and qualitative empirical research, the artefacts provide a valuable contribution to research and practice
Corporate Social Responsibility: the institutionalization of ESG
Understanding the impact of Corporate Social Responsibility (CSR) on firm performance as it relates to industries reliant on technological innovation is a complex and perpetually evolving challenge. To thoroughly investigate this topic, this dissertation will adopt an economics-based structure to address three primary hypotheses. This structure allows for each hypothesis to essentially be a standalone empirical paper, unified by an overall analysis of the nature of impact that ESG has on firm performance. The first hypothesis explores the evolution of CSR to the modern quantified iteration of ESG has led to the institutionalization and standardization of the CSR concept. The second hypothesis fills gaps in existing literature testing the relationship between firm performance and ESG by finding that the relationship is significantly positive in long-term, strategic metrics (ROA and ROIC) and that there is no correlation in short-term metrics (ROE and ROS). Finally, the third hypothesis states that if a firm has a long-term strategic ESG plan, as proxied by the publication of CSR reports, then it is more resilience to damage from controversies. This is supported by the finding that pro-ESG firms consistently fared better than their counterparts in both financial and ESG performance, even in the event of a controversy. However, firms with consistent reporting are also held to a higher standard than their nonreporting peers, suggesting a higher risk and higher reward dynamic. These findings support the theory of good management, in that long-term strategic planning is both immediately economically beneficial and serves as a means of risk management and social impact mitigation. Overall, this contributes to the literature by fillings gaps in the nature of impact that ESG has on firm performance, particularly from a management perspective
What do new performance metrics, VeDBA and Dynamic yaw, tell us about energy-intensive activities in whale sharks?
During oscillatory dives, whale sharks (Rhincodon typus) expend varying levels of energy in active ascent and passive descent. They are expected to minimise movement costs by travelling at optimum speed unless having reason to move faster, for example during feeding or evasion of danger. A proxy for power, dynamic body acceleration (DBA) has previously been used to identify whale shark movement patterns but has yet been used to identify occasions where power is elevated above minimum requirements. 59 hours of biologging data from 13 juvenile whale sharks (Ningaloo Reef, Western Australia) including depth, body pitch angle, magnetometry and DBA, was analysed to investigate minimum power requirements for dives and identify events of elevated power. Dynamic yaw (the rate of change of heading), a new proxy for power, was introduced to determine its effectiveness compared to the already-established DBA. The relationship between pitch angle and these two proxies was investigated to determine which had the stronger relationship. Dynamic yaw produced a poor relationship with pitch angle compared to DBA, and thus DBA was selected as the focus proxy for the remainder of the study. DBA was utilised to produce a minimum power trend versus body pitch angle using a convex hull analysis which allowed for the identification of proxy for power utilisation above the minimum (PAM). 16 instances of PAM were identified in 59 hours of data, which could all be considered instances where energy minimisation is not prioritised, such as feeding or avoidance. The PAM method was capable of identifying instances where energy minimisation is not prioritised, and therefore has future implications in investigations of location-specific behaviours in relation to feeding and anthropogenic disturbance
Visualisation of Fundamental Movement Skills (FMS): An Iterative Process Using an Overarm Throw
Fundamental Movement Skills (FMS) are precursor gross motor skills to more complex or specialised skills and are recognised as important indicators of physical competence, a key component of physical literacy. FMS are predominantly assessed using pre-defined manual methodologies, most commonly the various iterations of the Test of Gross Motor Development. However, such assessments are time-consuming and often require a minimum basic level of training to conduct. Therefore, the overall aim of this thesis was to utilise accelerometry to develop a visualisation concept as part of a feasibility study to support the learning and assessment of FMS, by reducing subjectivity and the overall time taken to conduct a gross motor skill assessment. The overarm throw, an important fundamental movement skill, was specifically selected for the visualisation development as it is an acyclic movement with a distinct initiation and conclusion. Thirteen children (14.8 ± 0.3 years; 9 boys) wore an ActiGraph GT9X Link Inertial Measurement Unit device on the dominant wrist whilst performing a series of overarm throws. This thesis illustrates how the visualisation concept was developed using raw accelerometer data, which was processed and manipulated using MATLAB 2019b software to obtain and depict key throw performance data, including the trajectory and velocity of the wrist during the throw. Overall, this thesis found that the developed visualisation concept can provide strong indicators of throw competency based on the shape of the throw trajectory. Future research should seek to utilise a larger, more diverse, population, and incorporate machine learning. Finally, further work is required to translate this concept to other gross motor skills
TOWARDS AN UNDERSTANDING OF EFFORTFUL FUNDRAISING EXPERIENCES: USING INTERPRETATIVE PHENOMENOLOGICAL ANALYSIS IN FUNDRAISING RESEARCH
Physical-activity oriented community fundraising has experienced an exponential growth in popularity over the past 15 years. The aim of this study was to explore the value of effortful fundraising experiences, from the point of view of participants, and explore the impact that these experiences have on people’s lives. This study used an IPA approach to interview 23 individuals, recognising the role of participants as proxy (nonprofessional) fundraisers for charitable organisations, and the unique organisation donor dynamic that this creates. It also bought together relevant psychological theory related to physical activity fundraising experiences (through a narrative literature review) and used primary interview data to substantiate these. Effortful fundraising experiences are examined in detail to understand their significance to participants, and how such experiences influence their connection with a charity or cause. This was done with an idiographic focus at first, before examining convergences and divergences across the sample. This study found that effortful fundraising experiences can have a profound positive impact upon community fundraisers in both the short and the long term. Additionally, it found that these experiences can be opportunities for charitable organisations to create lasting meaningful relationships with participants, and foster mutually beneficial lifetime relationships with them. Further research is needed to test specific psychological theory in this context, including self-esteem theory, self determination theory, and the martyrdom effect (among others)
- …