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Background Designing implementation interventions to change the behaviour of healthcare providers and other professionals in the health system requires detailed specification of the behaviour(s) targeted for change to ensure alignment between intervention components and measured outcomes. Detailed behaviour specification can help to clarify evidence-practice gaps, clarify who needs to do what differently, identify modifiable barriers and enablers, design interventions to address these and ultimately provides an indicator of what to measure to evaluate an intervention’s effect on behaviour change. An existing behaviour specification framework proposes four domains (Target, Action, Context, Time; TACT), but insufficiently clarifies who is performing the behaviour (i.e. the Actor). Specifying the Actor is especially important in healthcare settings characterised by multiple behaviours performed by multiple different people. We propose and describe an extension and re-ordering of TACT to enhance its utility to implementation intervention designers, practitioners and trialists: the Action, Actor, Context, Target, Time (AACTT) framework. We aim to demonstrate its application across key steps of implementation research and to provide tools for its use in practice to clarify the behaviours of stakeholders across multiple levels of the healthcare system. Methods and results We used French et al.’s four-step implementation process model to describe the potential applications of the AACTT framework for (a) clarifying who needs to do what differently, (b) identifying barriers and enablers, (c) selecting fit-for-purpose intervention strategies and components and (d) evaluating implementation interventions. Conclusions Describing and detailing behaviour using the AACTT framework may help to enhance measurement of theoretical constructs, inform development of topic guides and questionnaires, enhance the design of implementation interventions and clarify outcome measurement for evaluating implementation interventions.

Light detection and ranging technology allows for the creation of detailed 3D point clouds of physical objects and environments. Therefore, it has the potential to provide valuable information for the operation of various kinds of cyber-physical systems that need to be aware of, and interact with, their surroundings, such as autonomous vehicles and robots. Point clouds can also become the basis for the creation of digital representations of different assets and a system’s operational environment. This article outlines a system architecture that integrates the geo-spatial context information provided by LiDAR scans with behavioral models of the components of a cyber-physical system to create a digital twin. The clear distinction between behavior and data sets the proposed digital twin architecture apart from existing approaches (that primarily focus on the data aspect), and promotes contextual digital twin generation through executable process models. A vaccine logistics automation use case is detailed to illustrate how information regarding the environment can be used for the operation of an autonomous robot carrying out transport preparation tasks. Besides supporting operation, we propose to combine context data retrieved from the system at different points in the logistics process with information regarding instances of executable behavior models as part of the digital twin architecture. The twin can subsequently be used to facilitate system and process monitoring through relevant stakeholders and structure context data in a user-centric fashion.

Programming robots by human demonstration is an intuitive approach, especially by gestures. Because robot pick-and-place tasks are widely used in industrial factories, this paper proposes a framework to learn robot pick-and-place tasks by understanding human hand gestures. The proposed framework is composed of the module of gesture recognition and the module of robot behaviour control. For the module of gesture recognition, transport empty (TE), transport loaded (TL), grasp (G), and release (RL) from Gilbreth's therbligs are the hand gestures to be recognized. A convolution neural network (CNN) is adopted to recognize these gestures from a camera image. To achieve the robust performance, the skin model by a Gaussian mixture model (GMM) is used to filter out non-skin colours of an image, and the calibration of position and orientation is applied to obtain the neutral hand pose before the training and testing of the CNN. For the module of robot behaviour control, the corresponding robot motion primitives to TE, TL, G, and RL, respectively, are implemented in the robot. To manage the primitives in the robot system, a behaviour-based programming platform based on the Extensible Agent Behavior Specification Language (XABSL) is adopted. Because the XABSL provides the flexibility and re-usability of the robot primitives, the hand motion sequence from the module of gesture recognition can be easily used in the XABSL programming platform to implement the robot pick-and-place tasks. The experimental evaluation of seven subjects performing seven hand gestures showed that the average recognition rate was 95.96%. Moreover, by the XABSL programming platform, the experiment showed the cube-stacking task was easily programmed by human demonstration.

Background Precision medicine holds enormous potential to improve outcomes for cancer patients, offering improved rates of cancer control and quality of life. Not all patients who could benefit from targeted cancer therapy receive it, and some who may not benefit do receive targeted therapy. We sought to comprehensively identify determinants of targeted therapy use among community oncology programs, where most cancer patients receive their care. Methods Guided by the Theoretical Domains Framework, we conducted semi-structured interviews with 24 community cancer care providers and mapped targeted therapy delivery across 11 cancer care delivery teams using a Rummler-Brache diagram. Transcripts were coded to the framework using template analysis, and inductive coding was used to identify key behaviors. Coding was revised until a consensus was reached. Results Intention to deliver precision medicine was high across all participants interviewed, who also reported untenable knowledge demands. We identified distinctly different teams, processes, and determinants for (1) genomic test ordering and (2) delivery of targeted therapies. A key determinant of molecular testing was role alignment. The dominant expectation for oncologists to order and interpret genomic tests is at odds with their role as treatment decision-makers’ and pathologists’ typical role to stage tumors. Programs in which pathologists considered genomic test ordering as part of their staging responsibilities reported high and timely testing rates. Determinants of treatment delivery were contingent on resources and ability to offset delivery costs, which low- volume programs could not do. Rural programs faced additional treatment delivery challenges. Conclusions We identified novel determinants of targeted therapy delivery that potentially could be addressed through role re-alignment. Standardized, pathology-initiated genomic testing may prove fruitful in ensuring patients eligible for targeted therapy are identified, even if the care they need cannot be delivered at small and rural sites which may have distinct challenges in treatment delivery. Incorporating behavior specification and Rummler-Brache process mapping with determinant analysis may extend its usefulness beyond the identification of the need for contextual adaptation.

Use case diagrams are primary artifacts used for modeling functional requirements. Use case diagrams are part of the Unified Modeling Language (UML) suite of models that has become a de facto standard for modeling object oriented languages. Each model in this suite is described by a metamodel that dictates its syntax and semantics. The use case diagram is considered the most controversial diagram in UML. Practitioners claim that the use case diagram cannot be used as a valuable artifact for requirement analysis. The main reason behind this concern is the lack of behavioral description of a use case depicted within the model. Quite a few extensions to the use case metamodel have been proposed in literature to incorporate behavioral aspect of a use case within the metamodel. All these extensions omit a few important features like generalization and most of them can only be used for model representation and cannot be used for model analysis and evaluation. In this paper, we propose an extension to the UML use case metamodel with use case behavior specification elements. The main objective of the proposed extension is to provide a complete metamodel for use case diagrams which includes representation for all its elements and relationships in a conflict-free manner and one that includes information for model analysis, evaluation, and interchange among modeling tools. In order to include all valuable information related to a use case, a number of use case representation templates were considered for the proposed extension. Simultaneously, to enable the use case models generated based on the proposed metamodel to be used for analysis, pertinent information related to model usage in analysis such as effort estimation, use case scheduling, and use case metrics evaluation were considered from published studies, tools, and paradigms and included within the proposed metamodel.

This paper considers compositional specifications of services using UML 2 collaborations, activity and interaction diagrams, and addresses the realizability problem for such specifications: given a global specification, can we construct a set of communicating system components whose joint behavior is precisely the specified global behavior? We approach the problem by looking at how the sequencing of collaborations and local actions may be described using UML activity diagrams. We identify the realizability problems for each of the sequencing operators, such as strong and weak sequence, choice of alternatives, loops, and concurrency. The nature of these realizability problems and possible solutions are discussed. This brings a new look at already known problems: we show that given some conditions, certain problems can already be detected at an abstract level, without looking at the detailed interactions of the collaborations, provided that we know the components that initiate and terminate the different collaborations.

A modeling scheme is presented which provides a medium for the rigorous, formal, and abstract specification of large-scale software system components. The scheme allows the description of component behavior without revealing or requiring the description of a component's internal operation. Both collections of sequential processes and the data objects which they share may be described. The scheme is of particular value during the early stages of software system design, when the system's modules are being delineated and their interactions designed, and when rigorous, well-defined specification of undesigned components allows formal and informal arguments concerning the design's correctness to be formulated.

The effective implementation of government policies and measures for controlling the coronavirus disease 2019 (COVID-19) pandemic requires compliance from the public. This study aimed to examine cross-sectional and longitudinal associations of trust in government regarding COVID-19 control with the adoption of recommended health behaviours and prosocial behaviours, and potential determinants of trust in government during the pandemic. This study analysed data from the PsyCorona Survey, an international project on COVID-19 that included 23 733 participants from 23 countries (representative in age and gender distributions by country) at baseline survey and 7785 participants who also completed follow-up surveys. Specification curve analysis was used to examine concurrent associations between trust in government and self-reported behaviours. We further used structural equation model to explore potential determinants of trust in government. Multilevel linear regressions were used to examine associations between baseline trust and longitudinal behavioural changes. Higher trust in government regarding COVID-19 control was significantly associated with higher adoption of health behaviours (handwashing, avoiding crowded space, self-quarantine) and prosocial behaviours in specification curve analyses (median standardised = 0.173 and 0.229, < 0.001). Government perceived as well organised, disseminating clear messages and knowledge on COVID-19, and perceived fairness were positively associated with trust in government (standardised = 0.358, 0.230, 0.056, and 0.249, < 0.01). Higher trust at baseline survey was significantly associated with lower rate of decline in health behaviours over time ( for interaction = 0.001). These results highlighted the importance of trust in government in the control of COVID-19.

The availability of executable specification languages allows testing to be carried out soon after or concurrently with the requirements specification phase. In addition, it becomes possible to use these languages for rapid prototyping, making it possible to gather information on properties of the specified target system including its behavior in response to external events. The inspection of software behavior is viewed as the querying of executable specifications. A language RSQ is defined for the purpose of constructing queries against executable specifications expressed in RSF, a language for the description of systems with time constraints. A query is able to single out a subclass of possible behaviors based on properties supplied by the query. The integration of RSQ with RSF enhances the analytical abilities of the software designer and developer.< >

Difference-in-differences (DID) is commonly used for causal inference in time-series cross-sectional data. It requires the assumption that the average outcomes of treated and control units would have followed parallel paths in the absence of treatment. In this paper, we propose a method that not only relaxes this often-violated assumption, but also unifies the synthetic control method (Abadie, Diamond, and Hainmueller 2010) with linear fixed effects models under a simple framework, of which DID is a special case. It imputes counterfactuals for each treated unit using control group information based on a linear interactive fixed effects model that incorporates unit-specific intercepts interacted with time-varying coefficients. This method has several advantages. First, it allows the treatment to be correlated with unobserved unit and time heterogeneities under reasonable modeling assumptions. Second, it generalizes the synthetic control method to the case of multiple treated units and variable treatment periods, and improves efficiency and interpretability. Third, with a built-in cross-validation procedure, it avoids specification searches and thus is easy to implement. An empirical example of Election Day Registration and voter turnout in the United States is provided.