This study investigated TYLES, an iPad app adapting a tile-matrix task to support facial expression recognition in autistic children. The main aims of the study were to create an engaging app, assess user engagement and gauge its appeal with this population. Fifteen autistic children and their caregivers participated. Children used the iPad app daily for two weeks. While usability of the app was high, caregivers reported it as monotonous despite being easy for the child to navigate. Interestingly, children used the app for 12.6 minutes per day and achieved high accuracy (>90%) in recognizing emotions regardless of the expression type. These findings suggest children were already skilled at recognizing basic emotions, rendering the task unchallenging. Overall, the study results suggested that the TYLES app was usable but not accepted in its current form. This highlights the need for co-produced interventions and reconsidering the population on which the app is tested.

Accounting theory and accounting researchers stress the importance of clean surplus accounting and comprehensive income to corporate valuation. However, casual observation suggests that sell-side equity analysts routinely ignore other comprehensive income (OCI) in their forecasts and instead focus on forecasting earnings (before OCI). Using a sample of analyst reports, I first confirm that analysts normally omit forecasts of OCI or comprehensive income from their reports, consistent with analysts forecasting OCI as zero. I then predict and find that a zero forecast for OCI generally produces lower forecasting errors than alternative time-series models, such as a random walk or AR(1) model, suggesting a rational reason why analysts take this approach. Finally, I predict and find that although analysts' point forecasts of future OCI are usually zero, their implied cost of equity estimates are consistent with analysts forecasting a positive variance for OCI.

The concept of a smart factory, under Industry 4.0 relies heavily on cyber physical systems (CPS) and intra-enterprise-wide-networks (IWN). Cloud-based implementation is incumbent to accomplish the promises of enterprise integration, automation, seamless information exchange and intelligent self-organisation. Extensive research has been conducted in this domain, however, there is still much research to be done from the perspective of such frameworks in small to medium size enterprises (SMEs). In this context, the agent-oriented smart factory (AOSF) framework provides a generic end-to-end supply chain (SC) model, compliant with CPS and Industry 4.0 standards. In order to support the crucial side of warehouse management, this paper presents AOSF's recommended agent-oriented storage and retrieval (AOSR) warehouse planner with hybrid logic-based strategy, which yields a smart time-stamped plan to manage product placement and retrieval efficiently. The AOSF-associated AOSR-planner uses the hierarchical task network (HTN) AI planning to ensure different warehouse operations in a timely manner.

This paper investigates the consequences of removing the requirement from Australian corporate law that dividends can only be paid out of retained earnings. Using a difference-in-difference design, I find that firms with negative retained earnings and more volatile earnings increased dividends after the law change, consistent with the arguments of proponents of the law change. However, I further find that the law change is associated with a significant increase in cost of debt, decrease in debt maturity and increased reliance on bank debt. These results are consistent with increased agency costs of debt after the relaxation of dividend restrictions.

Autism is a neurodevelopmental condition with associated difficulties that present differently across individuals. One such difficulty is recognizing basic and complex facial expressions. Research has previously found that there are many evidence-based support programs available for building non-verbal communication skills. These programs are frequently administered with a therapist or in a group setting, making them inflexible in nature. Programs hosted on e-technology are becoming increasingly popular, with many parents supportive of them. Applications (apps) that are hosted on technology such as iPads or mobile phones allow users to engage in building skills in real-time social settings and own what they are learning. These technologies are frequently used by autistic children, with apps typically focusing on identifying facial features. Yet at this current time, there are mixed reviews of how to design such programs and what their theoretical backing is, with many studies using a mix of observation and psychological assessments as outcome measures. Eye-tracking and electroencephalography are established methodologies that measure neural processing and gaze behaviors while viewing faces. To better support the field moving forward, objective measures such as these are a way to measure outcomes of apps that are designed for helping children on the spectrum build skills in understanding facial expressions.

The Fourth Industrial Revolution (Industry 4.0), with the help of cyber-physical systems (CPS), the Internet of Things (IoT), and Artificial Intelligence (AI), is transforming the way industrial setups are designed. Recent literature has provided insight about large firms gaining benefits from Industry 4.0, but many of these benefits do not translate to SMEs. The agent-oriented smart factory (AOSF) framework provides a solution to help bridge the gap between Industry 4.0 frameworks and SME-oriented setups by providing a general and high-level supply chain (SC) framework and an associated agent-oriented storage and retrieval (AOSR)-based warehouse management strategy. This paper presents the extended heuristics of the AOSR algorithm and details how it improves the performance efficiency in an SME-oriented warehouse. A detailed discussion on the thorough validation via scenario-based experimentation and test cases explain how AOSR yielded 60–148% improved performance metrics in certain key areas of a warehouse.

The emergence of the fourth industrial revolution (Industry 4.0) has sparked proliferation in thedomain of Cyber-Physical Systems (CPS), and extensive research has been conductedin this area since its beginning. However, recent literature claims that Smallto Medium Size Enterprises (SMEs) are not getting the benefits of Industry 4.0 (I4.0) in a full potential because of unresolved compatibility-mismatch issues and involvement of high infrastructuralcost. In order to help bridge this gap, the Extended Agent-Oriented Smart Factory (xAOSF) framework provides a high-level guideline solution, integrating the whole supply chain (SC), from supplier-end to customer-end with an objective to expose SMEs towards the benefits of I4.0. This paper, as part of a publication series, provides a conceptualised visualisation of the xAOSF framework as a customised CPS,which presents an elegant mediation mechanism between multiple xAOSF agents to uptake negotiation and coordination schemes at different enterprise levels. This paper also includes detail on howthe I4.0 based xAOSF framework caters to three-dimensional enterprise integration, in order to provide seamless connectivity and robustness in enterprise-wide operations. Furthermore, for the purpose of validation and to justify the claim, the experimentation is performed by applying a comprehensive test scenario onxAOSF's recommended AOSR WMS strategy in comparison with linear SC-based standard WMS system, which yields a substantial performance improvement in certain key-performance areas.

Nowadays Model-Driven Engineering (MDE) is gaining more popularity due to high-level development leading to a faster generation of executable code, which reduces manual intervention. Verification is crucial at different levels of model-based development Model-based development, along with formal verification process, assures the developed model satisfies software requirements described in formal specifications. Owing the inadequate knowledge of formal methods (complex mathematical theory), software developers are not adopting formal methods during software development. There are several approaches in the literature available to transform MDE models into formal models directly for formal verification, and these approaches require an additional input of formal specifications to verification tools for formal verification. But these methods have not addressed the problem of formal specifications at the model level. In this paper, we design a modelling framework using modelling techniques, which allows specifying formal properties at the model level, automatically extracting formal specifications and formal models from developed application models, which are used for formal verification. The proposed method allows full automation and reduces the time for formal verification process during the development life-cycle. Furthermore, the method reduces the complexity of learning formal specification notations (specifications specified at the model level are automatically converted into formal specifications), which are required to input verification tools for formal verification.

Nowadays, automation can be assisted by using programmable logic controllers (PLCs). PLCs are typically programmed with IEC 61131-3 languages to automate and implement the applications. PLC program classification plays an important role in the identification of similar functionality, which can be considered as software clones. In this paper, we present work to identify clones in IEC 61131-3 languages, using an approach based on four different perspectives: (a) clone prediction: filtering based on heuristics; (b) structural analysis: detect syntactic code clones; (c) semantic analysis: analysis of output variable dependency and input variable impact usage to detect semantic clones; (d) variable interval analysis: analysis of each program variable intervals to examine and detect clones. Our approach is a combination of structural, semantic and data interval based analysis. As a result, our approach is feasible and yields good results in detecting clones on our test data.

Recent literature claims that Small to Medium Size Enterprises (SMEs), as compared to larger setups, may not be able to experience all the benefits of the fourth industrial revolution (Industry 4.0). In order to bridge this gap, the Agent Oriented Smart Factory (AOSF) framework provides a comprehensive supply chain architecture. AOSF framework does not only provide high-level enterprise integration guidelines but also recommends a thorough implementation in the area of warehousing by providing Agent Oriented Storage and Retrieval (AOSR) WMS system. This paper focuses on scenario-based comparison of the extended AOSF framework with a Linear SC model, to explain substantially improved performance efficiency especially in SME-oriented warehousing. These scenario-based experiments indicate that AOSR can yield 60–148% improvement in certain Key Performance Indicators (KPIs), i.e. number of products stored in racks, receiving area (RA) and expedition areas (EA), in comparison with standard WMS strategies.

ABSTRACT Management accounting researchers have been slow to explore the empirical implications of the “manager effect” on management control choices. We critique the “manager effect” literature and identify research opportunities for management accounting researchers. Since the publication of Bertrand and Schoar's (2003) seminal paper, which shows that individual managers have an effect on firm behavior, a large and growing body of accounting and finance research has used publicly available data to identify individual manager effects on a variety of firm outcomes. Management accounting researchers can add significant value to this research; for example, by exploring the control choices that a firm makes to mitigate the adverse consequences associated with some managerial characteristics. In this critique we first identify some of the theoretical and methodological challenges associated with the “manager effects” research and second identify opportunities for management accounting researchers to explore these effects while overcoming some of the limitations.

Background Stroke events often result in long-term negative health outcomes. People who experience a first stroke event are 30%–40% more likely to experience a second stroke event within 5 years. An online secondary prevention programme for stroke survivors may help stroke survivors improve their health risk behaviours and lower their risk of a second stroke. Objectives This paper describes the development and early iteration testing of the usability and acceptability of an online secondary prevention programme for stroke survivors (Prevent 2nd Stroke, P2S). P2S aims to address six modifiable health risk behaviours of stroke: blood pressure, physical activity, nutrition, depression and anxiety, smoking, and alcohol consumption. Methods P2S was developed as an eight-module online secondary prevention programme for stroke survivors. Modelled on the DoTTI (Design and development, Testing early iterations, Testing for effectiveness, Integration and implementation) framework for the development of online programmes, the following stages were followed during programme development: (1) content development and design; and (2) testing early iteration. The programme was pilot-tested with 15 stroke survivors who assessed P2S on usability and acceptability. Results In stage 1, experts provided input for the content development of P2S. In stage 2, 15 stroke survivors were recruited for usability testing of P2S. They reported high ratings of usability and acceptability of P2S. P2S was generally regarded as ‘easy to use’ and ‘relevant to stroke survivors’. Participants also largely agreed that it was appropriate to offer lifestyle advice to stroke survivors through the internet. Conclusions The study found that an online secondary prevention programme was acceptable and easily usable by stroke survivors. The next step is to conduct a randomised controlled trial to assess the effectiveness of the programme regarding behaviour change and determine the cost-effectiveness of the intervention.

The detection of software clones is gaining more attention due to the advantages it can bring to software maintenance. Clone detection helps in code optimization (code present in multiple locations can be updated and optimized once), bug detection (discovering bugs that are copied to various locations in the code), and analysis of re-used code in software systems. There are several approaches to detect clones at the code level, but existing methods do not address the issue of clone detection in the PLC-based IEC 61131-3 languages. In this paper, we present a novel approach to detect clones in PLC-based IEC 61131-3 software using semantic-based analysis. For the semantic analysis, we use I/O based dependency analysis to detect PLC program clones. Our approach is a semantic-based technique to identify clones, making it feasible even for large code bases. Further, experiments indicate that the proposed method is successful in identifying software clones.

For the concept of Industry 4.0 to come true, a mature amalgamation of allied technologies is obligatory, i.e. Internet of Things (IoT), Big Data analytics, Mobile Computing, Multi-Agent Systems (MAS) and Cloud Computing. With the emergence of the fourth industrial revolution, proliferation in the field of Cyber-Physical Systems (CPS) and Smart Factory gave a boost to recent research in this dimension. Despite many autonomous frameworks contributed in this area, there are very few widely acceptable implementation frameworks, particularly for Small to Medium Size Enterprises (SMEs) under the umbrella of Industry 4.0. This paper presents an Agent-Oriented Smart Factory (AOSF) framework, integrating the whole supply chain (SC), from supplier-end to customer-end. The AOSF framework presents an elegant mediating mechanism between multiple agents to increase robustness in decision making at the base level. Classification of agents, negotiation mechanism and few results from a test case are presented.

In previous publications we have introduced the concept of using a component-based software engineering paradigm to build Internet-enabled applications. We have proposed that this design allows for greater flexibility in deployment, better utilisation of resources and a reduction in total application development effort. We have described a system and realised that system as an API that can be used to design, build and execute such components. In this report we provide an overview of the key system components and present an implementation of an application developed using the system. We use this application to perform experimental and functional comparisons to show that the system provides advancements over the status quo.

Industry 4.0 is revolutionising recent industrial setups. Literature has examined the idea of Smart Factory under the umbrella of Industry 4.0 extensively, but further research into the applicability of such frameworks for Small to Medium Size Enterprises (SMEs) is still required. To help address this issue, the Agent-Oriented Smart Factory (AOSF) framework focuses on a multi-agent architecture for end-to-end Supply Chain (SC) in SMEs. This paper presents a Cyber Physical System (CPS) based extension to the general AOSF framework and design heuristics for problem and domain definition of Agent Oriented Storage and Retrieval (AOSR) warehouse system using Multi-Agent Hierarchical Task Networking (MA-HTN) planning formalism.

Clone detection is gaining more attention due to its advantages of software maintenance. Clone detection helps in code optimization (code present in multiple locations can be updated and optimized once), bug detection (discovering bugs that are copied to multiple locations in the code), and analysis of re-used code in software systems. Importantly, model-based software development is gaining more popularity due to its reduced production time and cost. There are several approaches to detect clones in the code level, but few methods to detect clones at the model level. These methods use syntactic based analysis of models to detect clones at the model level. In this paper, we present a novel approach to detect clones at model-based levels using semantic based analysis. Our method is based on model checking which involves mathematical based analysis. Our method is tested with control flow based models and yields good results in detection of model clones.

Background Tobacco smoking can have negative health outcomes on recovery from surgery. Although it is recommended best practice to provide patients with advice to quit and follow-up support, provision of post-discharge support is rare. Developing an online smoking cessation program may help address this gap. Objectives This paper describes the development and pretesting of an online smoking cessation program (smoke-free recovery, SFR) tailored to the orthopaedic trauma population for use while in hospital and post-discharge. Methods Drawing on the DoTTI framework for developing an online program, the following steps were followed for program development: (1) design and development; (2) testing early iteration; (3) testing for effectiveness and (4) integration and implementation. This article describes the first two stages of SFR program development. Results SFR is a 10-module online smoking cessation program tailored for patients with orthopaedic trauma. Of the participants who completed testing early iterations, none reported any difficulties orientating themselves to the program or understanding program content. The main themes were that it was ‘helpful’, provision of ‘help to quit’ was low and SFR increased thoughts of ‘staying quit post-discharge’. Conclusions This study found that a theory and evidence-based approach as the basis for an online smoking cessation program for patients with orthopaedic trauma was acceptable to users. A randomised controlled trial will be conducted to examine whether the online smoking cessation program is effective in increasing smoking cessation and how it can be integrated and implemented into hospital practice (stages three and four of the DoTTI framework).

Anecdotal evidence suggests that corporate boards often use information concerning a CEO’s social capital in the selection decision as it is an important heuristic for her ability. Based on prior evidence it is assumed that CEOs with high social capital will improve firm value. However, a CEO can use her social capital in ways that are detrimental to the firm. We attempt to address this conundrum by firstly examining whether the social capital of a new chief executive officer (CEO) affects firm value and secondly whether the conditions faced by the firm influence the relation between the new CEO’s social capital and firm value. We present evidence that the new CEO’s connections have a significantly positive association with the hiring firm’s market value. We also find that CEO social ties have a stronger impact on firm value when firms face greater strategic uncertainty and have an independent board in place. Our supplemental tests also show that the most valuable connections are ‘advice networks’ and cross-industry connections. Furthermore, we find that well-connected CEOs tend to spend more on R&D projects, make greater SG&A investments and generate higher gross margins. Firms also reward CEOs for their social capital.