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Applied computational ontologies (ACOs) are increasingly used in data science domains to produce semantic enhancement and interoperability among divergent data. The purpose of this paper is to propose and implement a methodology for researching the sociotechnical dimensions of data-driven ontology work, and to show how applied ontologies are communicatively constituted with ethical implications.

The underlying idea is to use a data assemblage approach for studying ACOs and the methods they use to add semantic complexity to digital data. The author uses a mixed methods approach, providing an analysis of the widely used Basic Formal Ontology (BFO) through digital methods and visualizations, and presents historical research alongside unstructured interview data with leading experts in BFO development.

The author found that ACOs are products of communal deliberation and decision making across institutions. While ACOs are beneficial for facilitating semantic data interoperability, ACOs may produce unintended effects when semantically enhancing data about social entities and relations. ACOs can have potentially negative consequences for data subjects. Further critical work is needed for understanding how ACOs are applied in contexts like the semantic web, digital platforms, and topic domains. ACOs do not merely reflect social reality through data but are active actors in the social shaping of data.

The paper presents a new approach for studying ACOs, the social impact of ACO work, and describes methods that may be used to produce further applied ontology studies.

The purpose of this paper is to explore the adoption and implementation of computer-assisted therapy (CAT) using Breaking Free Online (BFO) in a social care and health charity working with people affected by drugs and alcohol dependence, Crime Reduction Initiatives (CRI).

Semi-structured interviews were conducted with service managers, practitioners, peer mentors and service users. Data were thematically analysed and themes conceptualised using Roger's Diffusion of Innovation Theory (Rogers, 1995, 2002, 2004).

A number of perceived barriers to adoption of BFO throughout CRI were identified within the social system, including a lack of IT resources and skills. However, there were numerous perceived benefits of adoption of BFO throughout CRI, including broadening access to effective interventions to support recovery from substance dependence, and promoting digital inclusion. Along with the solutions that were found to the identified barriers to implementation, intentions around longer-term continuation of adoption of the programme were reported, with this process being supported through changes to both the social system and the individuals within it.

The introduction of innovations such as BFO within large organisations like CRI can be perceived as being disruptive, even when individuals within the organisation recognise its benefits. For successful adoption and implementation of such innovations, changes in the social system are required, at organisational and individual levels.

The learning points from this study may be relevant to the substance misuse sector, and more widely to criminal justice, health and social care organisations.

This study is the first of its kind to use a qualitative approach to examine processes of implementation of CAT for substance misuse within a large treatment and recovery organisation.

Multiple challenges remain in achieving sustainability of digital health innovations, with many failing to realise their potential due to barriers to research, development and implementation. Finding an approach that overcomes these challenges is important if society is to derive benefit from these new approaches to healthcare. Having been commissioned by local authorities, NHS Trusts, prisons, charities, and third sector providers across the UK, Breaking Free Group, who in 2010 launched Breaking Free Online (BFO), a computer-assisted therapy programme for substance misuse, have overcome many of these challenges. This has been possible through close collaborative working with partner organisations, to overcome barriers to implementation and sustainability. The paper aims to discuss these issues.

This paper synthesises findings from a series of qualitative studies conducted by Breaking Free Group in collaboration with health and social care charity, Change, Grow, Live (CGL), which explore barriers and facilitators of implementation and sustainability of BFO at CGL. Data are analysed using thematic analyses with findings conceptualised using behavioural science theory.

This partnership has resulted in UK wide implementation of BFO at CGL, enhanced focus on digital technologies in substance misuse recovery, and a growing body of published collaborative research.

Valuable lessons have been learnt through the partnership between Breaking Free Group and CGL, which will be of interest to the wider digital health community. This paper outlines those lessons, in the hope that they will provide guidance to other digital health developers and their partners, to contribute to the continued evolution of a sustainable digital health sector.

The purpose of this paper is to optimize the performance for complex repairable system of paint manufacturing unit using a new hybrid bacterial foraging and particle swarm optimization (BFO-PSO) evolutionary algorithm. For this, a performance model is developed with an objective to analyze the system availability.

In this paper, a Markov process-based performance model is put forward for system availability estimation. The differential equations associated with the performance model are developed assuming that the failure and repair rate parameters of each sub-system are constant and follow the exponential distribution. The long-run availability expression for the system has been derived using normalizing condition. This mathematical framework is utilized for developing an optimization model in MATLAB 15 and solved through BFO-PSO and basic particle swarm optimization (PSO) evolutionary algorithms coded in the light of applicability. In this analysis, the optimal input parameters are determined for better system performance.

In the present study, the sensitivity analysis for various sub-systems is carried out in a more consistent manner in terms of the effect on system availability. The optimal failure and repair rate parameters are obtained by solving the performance optimization model through the proposed hybrid BFO-PSO algorithm and hence improved system availability. Further, the results obtained through the proposed evolutionary algorithm are compared with the PSO findings in order to verify the solution. It can be clearly observed from the obtained results that the hybrid BFO-PSO algorithm modifies the solution more precisely and consistently.

There is no limitation for implementation of proposed methodology in complex systems, and it can, therefore, be used to analyze the behavior of the other repairable systems in higher sensitivity zone.

The performance model of the paint manufacturing system is formulated by utilizing the available uncertain data of the used manufacturing unit. Using these data information, which affects the performance of the system are parameterized in the input failure and repair rate parameters for each sub-system. Further, these parameters are varied to find the sensitivity of a sub-system for system availability among the various sub-systems in order to predict the repair priorities for different sub-systems. The findings of the present study show their correspondence with the system experience and highlight the various availability measures for the system analyst in maintenance planning.

The aim of this research paper is to examine the bio‐inspired optimization algorithms, namely, genetic algorithm (GA), particle swarm optimization (PSO) and bacterial foraging optimization (BFO) algorithm with adaptive chemotactic step for determining the steady‐state equivalent circuit parameters of the three‐phase induction motor using a set of manufacturer data.

The induction motor parameter determination issue is devised as a nonlinear constrained optimization problem. The nonlinear equations of various quantities (torque, current and power factor) are derived in terms of equivalent circuit parameters from a single and a double‐cage model, and then, equates to the corresponding manufacturer data. These equations are solved by the bio‐inspired algorithms. Using the squared error between the determined and the manufacturer data as the objective function, the parameter determination problem is transferred into an optimization process where the model parameters are determined that minimize the defined objective function. The objective function is iteratively minimized using GA, PSO and BFO techniques. In order to balance the exploration and exploitation searches of the BFO algorithm, an adaptive chemotactic step is utilized.

Comparisons of the results of GA, PSO, BFO and IEEE Std. 112‐F (using no‐load, locked‐rotor and stator resistance tests) methods for two sample motors are presented. Results show the superiority of the bio‐inspired optimization algorithms over the classical one. Besides, BFO‐based parameter determination method is observed to obtain better quality solutions quickly than GA and PSO methods.

The parameters obtained by the proposed approaches can be used in analyzing the stalling and/or reacceleration process of a loaded motor following a fault or during voltage sag condition as well as in system‐level studies.

The most significant contribution of the research is the potential to determine the equivalent circuit parameters of induction motor only from its manufacturer data without conducting any lab tests on the motor. The bio‐inspired optimization based parameter determination approaches are faster and less intrusive than the IEEE Std. 112‐F method.

In recent years there has been a proliferation of computer-based psychotherapeutic interventions for common mental health difficulties. Building on this, a small number of such interventions have now been developed to address substance dependence, one of which is Breaking Free Online (BFO). A new “eTherapy” self-help service, which was set up by the UK mental health charity Self-Help Services, has provided access to BFO to service users presenting with comorbid mental health and substance misuse difficulties. The purpose of this paper is to evaluate a range of clinical outcomes in the first cohort of service users accessing this dual diagnosis service.

A number of standardised psychometric assessments were conducted with service users at baseline and post-treatment at discharge from the service. Outcome data were available for 47 service users out of an original cohort of 74.

Statistically significant improvements were found in terms of measures of social functioning, depression, anxiety, alcohol and drug use and social anxiety. Clinically relevant gains were also identified, with fewer service users reaching threshold scores for depression and anxiety at post-treatment compared to baseline. Effect sizes also indicated that the identified improvements across the psychometric measures were robust and significant.

These findings provide further support for the clinical effectiveness of BFO, and also provide evidence that an eTherapy self-help service may be appropriate for some individuals presenting with dual diagnosis. Further research is underway with larger and alternative clinical populations to examine the effectiveness of BFO and also this novel eTherapy self-help approach.

This paper has provided initial data to support effectiveness of a novel eTherapy service for dual diagnosis.

This study aims to explore a way of representing historical collections by examining the features of an event in historical documents and building an event-based ontology model.

To align with a domain-specific and upper ontology, the Basic Formal Ontology (BFO) model is adopted. Based on BFO, an event-based ontology for historical description (EOHD) is designed. To define events, event-related vocabularies are taken from the Library of Congress’ event types (2012). The three types of history and six kinds of changes are defined.

The EOHD model demonstrates how to apply the event ontology to biographical sketches of a creator history to link event types.

The EOHD model has great potential to be further expanded to specific events and entities through different types of history in a full set of historical documents.

The EOHD provides a framework for modeling and semantically reforming the relationships of historical documents, which can make historical collections more explicitly connected in Web environments.

The links between substance use and offending are well evidenced in the literature, and increasingly, substance misuse recovery is being seen as a central component of the process of rehabilitation from offending, with substance use identified as a key criminogenic risk factor. In recent years, research has demonstrated the commonalities between recovery and rehabilitation, and the possible merits of providing interventions to substance-involved offenders that address both problematic sets of behaviours. The purpose of this paper is to provide an overview of the links between substance use and offending, and the burgeoning literature around the parallel processes of recovery and rehabilitation.

This is provided as a rationale for a new treatment approach for substance-involved offenders, Breaking Free Online (BFO), which has recently been provided as part of the “Gateways” throughcare pathfinder in a number of prisons in North-West England. The BFO programme contains specific behaviour change techniques that are generic enough to be applied to change a wide range of behaviours, and so is able to support substance-involved offenders to address their substance use and offending simultaneously.

This dual and multi-target intervention approach has the potential to address multiple, associated areas of need simultaneously, streamlining services and providing more holistic support for individuals, such as substance-involved offenders, who may have multiple and complex needs.

Given the links between substance use and offending, it may be beneficial to provide multi-focussed interventions that address both these behaviours simultaneously, in addition to other areas of multiple and complex needs. Specifically, digital technologies may provide an opportunity to widen access to such multi-focussed interventions, through computer-assisted therapy delivery modalities. Additionally, using digital technologies to deliver such interventions can provide opportunities for joined-up care by making interventions available across both prison and community settings, following offenders on their journey through the criminal justice system.

Recommendations are provided to other intervention developers who may wish to further contribute to widening access to such dual- and multi-focus programmes for substance-involved offenders, based on the experiences developing and evidencing the BFO programme.

The innovation in big data is increasing day by day in such a way that the conventional software tools face several problems in managing the big data. Moreover, the occurrence of the imbalance data in the massive data sets is a major constraint to the research industry.

The purpose of the paper is to introduce a big data classification technique using the MapReduce framework based on an optimization algorithm. The big data classification is enabled using the MapReduce framework, which utilizes the proposed optimization algorithm, named chicken-based bacterial foraging (CBF) algorithm. The proposed algorithm is generated by integrating the bacterial foraging optimization (BFO) algorithm with the cat swarm optimization (CSO) algorithm. The proposed model executes the process in two stages, namely, training and testing phases. In the training phase, the big data that is produced from different distributed sources is subjected to parallel processing using the mappers in the mapper phase, which perform the preprocessing and feature selection based on the proposed CBF algorithm. The preprocessing step eliminates the redundant and inconsistent data, whereas the feature section step is done on the preprocessed data for extracting the significant features from the data, to provide improved classification accuracy. The selected features are fed into the reducer for data classification using the deep belief network (DBN) classifier, which is trained using the proposed CBF algorithm such that the data are classified into various classes, and finally, at the end of the training process, the individual reducers present the trained models. Thus, the incremental data are handled effectively based on the training model in the training phase. In the testing phase, the incremental data are taken and split into different subsets and fed into the different mappers for the classification. Each mapper contains a trained model which is obtained from the training phase. The trained model is utilized for classifying the incremental data. After classification, the output obtained from each mapper is fused and fed into the reducer for the classification.

The maximum accuracy and Jaccard coefficient are obtained using the epileptic seizure recognition database. The proposed CBF-DBN produces a maximal accuracy value of 91.129%, whereas the accuracy values of the existing neural network (NN), DBN, naive Bayes classifier-term frequency–inverse document frequency (NBC-TFIDF) are 82.894%, 86.184% and 86.512%, respectively. The Jaccard coefficient of the proposed CBF-DBN produces a maximal Jaccard coefficient value of 88.928%, whereas the Jaccard coefficient values of the existing NN, DBN, NBC-TFIDF are 75.891%, 79.850% and 81.103%, respectively.

In this paper, a big data classification method is proposed for categorizing massive data sets for meeting the constraints of huge data. The big data classification is performed on the MapReduce framework based on training and testing phases in such a way that the data are handled in parallel at the same time. In the training phase, the big data is obtained and partitioned into different subsets of data and fed into the mapper. In the mapper, the features extraction step is performed for extracting the significant features. The obtained features are subjected to the reducers for classifying the data using the obtained features. The DBN classifier is utilized for the classification wherein the DBN is trained using the proposed CBF algorithm. The trained model is obtained as an output after the classification. In the testing phase, the incremental data are considered for the classification. New data are first split into subsets and fed into the mapper for classification. The trained models obtained from the training phase are used for the classification. The classified results from each mapper are fused and fed into the reducer for the classification of big data.

The purpose of this paper is to develop a proportional-integral-derivative-based fuzzy neural network (PIDFNN) with elitist bacterial foraging optimization (EBFO)-based optimal membership functions (PIDFNN-EBFO) position controller to control the voice coil motor (VCM) for tracking reference trajectory accurately.

Because the control characteristics of the VCM are highly nonlinear and time varying, a PIDFNN, which integrates adaptive PID control with fuzzy rules, is proposed to control the mover position of the VCM. Moreover, an EBFO algorithm is further proposed to find the initial optimal fuzzy membership functions for the PIDFNN controller.

Due to the gradient descent method used in back propagation (BP) to derive the on-line learning algorithm for the PIDFNN, it may reach the local optimal solution due to the inappropriate initial values. Hence, a hybrid learning method, which includes BP and EBFO algorithms, is proposed to improve the learning performance of the PIDFNN controller.

Future work will consider reducing the computational burden of bacterial foraging optimization algorithm for on-line parameters optimization.

The real-time control system is implemented on a 32-bit floating-point digital signal processor (DSP). The experimental results demonstrate the favorable effectiveness of the proposed PIDFNN-EBFO controlled VCM system.

A new PIDFNN-EBFO control scheme is proposed and implemented via DSP for real-time VCM position control. The experimental results show the superior control performance of the proposed PIDFNN-EBFO compared with the other control systems.