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The purpose of this paper is to demonstrate how the Triple-V model of experiential learning, when applied to Higher Education (HE), can transform the student-learning experience by integrating the model’s three elements of Vision, Value and Vocation into delivery, assessment and beyond.

The paper sets out how the Triple-V model was introduced in three case studies. Feedback data were gathered through formal and informal surveys with students and other respondents.

The Triple-V model expounds the virtues of integrating Vision, Value and Vocation into HE to engage students in deep learning and to provide an external employability reference framework, which is particularly vital for students leaving HE with concerns about securing suitable employment to service soaring levels of student debt. The implementation of the model, based on measured outcomes, met with positive feedback from respondents.

The Triple-V model was tested across three scenarios, using different respondents, within a School of Management. A Twitter account has been established (#triple_v_model) to invite wider participation and feedback to hone the model further, in particular its suitability for more esoteric, and less exoteric, subjects.

The Triple-V model is entirely original, devised by the present author, and is intended to enhance the HE student learning experience, contextualising for students their studies within a wider employability framework.

The purpose of this paper is to investigate the use of innovative teaching methods and share a four-step model, to promote the use of co-production in mental health practice.

The case study approach highlights three real-life examples of day to day experiences in mental health nurse education with innovative approaches to sharing and developing co-production skills and attitudes in mental health student nurses.

The case studies highlight three settings where undergraduate mental health nurses experience co-production through a world café event and dialogical community development. Common themes include setting the environment, developing a common aim and relationship building.

A limitation of this paper is that only three case studies are provided, further examples would provide a greater pool of exemplars for others to draw on. However, by focusing upon student nurse education in learning environment, these examples are transferable to other settings.

The practical applications are summarised in a four-step model that can help develop co-productive teaching methods; enable educators to set the climate and generate an understanding of co-production that empowers students and service users.

The emphasis and relevance of promoting co-productive working habits early on in nurses’ mental health nursing careers will enable them to raise awareness of future social implications for a range of client groups.

This paper focuses upon mental health student nurses whilst providing an innovative model to facilitate co-production experiences applicable in a range of settings.

Experiments are the backbone of the development process of data-driven predictive models for scientific applications. The quality of the experiments directly impacts the model performance. Uncertainty inherently affects experiment measurements and is often missing in the available data sets due to its estimation cost. For similar reasons, experiments are very few compared to other data sources. Discarding experiments based on the missing uncertainty values would preclude the development of predictive models. Data profiling techniques are fundamental to assess data quality, but some data quality dimensions are challenging to evaluate without knowing the uncertainty. In this context, this paper aims to predict the missing uncertainty of the experiments.

This work presents a methodology to forecast the experiments’ missing uncertainty, given a data set and its ontological description. The approach is based on knowledge graph embeddings and leverages the task of link prediction over a knowledge graph representation of the experiments database. The validity of the methodology is first tested in multiple conditions using synthetic data and then applied to a large data set of experiments in the chemical kinetic domain as a case study.

The analysis results of different test case scenarios suggest that knowledge graph embedding can be used to predict the missing uncertainty of the experiments when there is a hidden relationship between the experiment metadata and the uncertainty values. The link prediction task is also resilient to random noise in the relationship. The knowledge graph embedding outperforms the baseline results if the uncertainty depends upon multiple metadata.

The employment of knowledge graph embedding to predict the missing experimental uncertainty is a novel alternative to the current and more costly techniques in the literature. Such contribution permits a better data quality profiling of scientific repositories and improves the development process of data-driven models based on scientific experiments.

The purpose of this paper is to present a generic pipeline for Resource Description Framework (RDF) graph mining to provide a comprehensive review of each step in the knowledge discovery from data process. The authors also investigate different approaches and combinations to extract feature vectors from RDF graphs to apply the clustering and theme identification tasks.

The proposed methodology comprises four steps. First, the authors generate several graph substructures (Walks, Set of Walks, Walks with backward and Set of Walks with backward). Second, the authors build neural language models to extract numerical vectors of the generated sequences by using word embedding techniques (Word2Vec and Doc2Vec) combined with term frequency-inverse document frequency (TF-IDF). Third, the authors use the well-known K-means algorithm to cluster the RDF graph. Finally, the authors extract the most relevant rdf:type from the grouped vertices to describe the semantics of each theme by generating the labels.

The experimental evaluation on the state of the art data sets (AIFB, BGS and Conference) shows that the combination of Set of Walks-with-backward with TF-IDF and Doc2vec techniques give excellent results. In fact, the clustering results reach more than 97% and 90% in terms of purity and F-measure, respectively. Concerning the theme identification, the results show that by using the same combination, the purity and F-measure criteria reach more than 90% for all the considered data sets.

The originality of this paper lies in two aspects: first, a new machine learning pipeline for RDF data is presented; second, an efficient process to identify and extract relevant graph substructures from an RDF graph is proposed. The proposed techniques were combined with different neural language models to improve the accuracy and relevance of the obtained feature vectors that will be fed to the clustering mechanism.

This study aims to design a multivariate control chart that improves the applicability of the traditional Hotelling T² chart. This new type of multivariate control chart displays sufficient information about the states and relationships of the variables in the production process. It is used to make better quality control decisions during the production process.

Multivariate data are collected at an equal time interval and are represented by nodes of the graph. The edges connecting the nodes represent the sequence of operation. Each node is plotted on the control chart based on their Hotelling T² statistical distance. The changing behavior of each pair of input and output nodes is studied by the neural network. A case study from the cement industry is conducted to validate the control chart.

The finding of this paper is that the points and lines in the classic Hotelling T² chart are effectively substituted by nodes and edges of the graph respectively. Nodes and edges have dimension and color and represent several attributes. As a result, this control chart displays much more information than the traditional Hotelling T² control chart. The pattern of the plot represents whether the process is normal or not. The effect of the sequence of operation is visible in the control chart. The frequency of the happening of nodes is recognized by the size of nodes. The decision to change the product feature is assisted by finding the shortest path between nodes. Moreover, consecutive nodes have different behaviors, and that behavior change is recognized by neural network.

Modifying the classical Hotelling T² control chart by integrating with the concept of graph theory and neural network is new of its kind.

There is growing public interest in alternatives to intellectual property including, but not limited to, prizes and government grants. We collect various historical and contemporary examples of alternative incentives, and show when they are superior to intellectual property. We also give an explanation for why federally funded R&D has moved from an intramural activity to largely a grant process. Finally, we observe that much research is supported by a hybrid system of public and private sponsorship, and explain why this makes sense in some circumstances.

The purpose of this paper is to implement a Web interface for hybrid intelligent systems. Using the implemented Web interface, this paper evaluates two hybrid intelligent systems based on particle swarm optimization, hill climbing and distributed genetic algorithm to solve the node placement problem in wireless mesh networks (WMNs).

The node placement problem in WMNs is well-known to be a computationally hard problem. Therefore, the authors use intelligent algorithms to solve this problem. The implemented systems are intelligent systems based on meta-heuristics algorithms: Particle Swarm Optimization (PSO), Hill Climbing (HC) and Distributed Genetic Algorithm (DGA). The authors implement two hybrid intelligent systems: WMN-PSODGA and WMN-PSOHC-DGA.

The authors carried out simulations using the implemented Web interface. From the simulations results, it was found that the WMN-PSOHC-DGA system has a better performance compared with the WMN-PSODGA system.

For simulations, the authors considered Normal distribution of mesh clients. In the future, the authors need to consider different client distributions, patterns, number of mesh nodes and communication distance.

In this research work, the authors implemented a Web interface for hybrid intelligent systems. The implemented interface can be extended for other metaheuristic algorithms.

The purpose of this paper is to investigate the implications for supply chain risk management (SCRM) by applying internet of things (IoT). Therefore, the impact and effects on the SCRM process, as well as the internal and external pathway and the outcome of SCRM are examined.

This study adopts a multiple case study methodology with twelve companies from the manufacturing industry. This study is guided by the information processing theory (IPT) and a theory-grounded research framework to provide insights into information requirements and information processing capabilities for IoT-supported SCRM.

The studied cases demonstrate an increase in data availability in the companies that contribute to improved process transparency and process management. Furthermore, the process steps, risk transparency, risk knowledge and risk strategies have been enhanced, which enabled improved SCRM performance by fitting information requirements and information processing capabilities, thus allowing for competitive advantage.

This study offers in-depth insights for SCRM managers into the structure of IoT systems, primary use cases and changes for the process itself. Furthermore, implications for employees, incentives and barriers are identified, which could be used to redesign SCRM.

This study addresses the requirement for additional empirical research on technology-enhanced SCRM, supported by IPT as a theoretical foundation. The radical change of SCRM by IoT is demonstrated while discussing the human role, implications for SCRM strategies and identifying relevant topics for future development.