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Successful mitigation of emerging infectious disease requires that the public adopt recommended behaviours, which is directly influenced by effective crisis communication. Social media has become an important communication channel during COVID-19 where official actors, influencers, and the public are co-creating crisis messages. Our research examined COVID-19-related crisis messages across Canadian influencer accounts within news media, politicians, public health and government, science communicators, and brand influencer and celebrities, posted on Instagram between December 2019 and March 2021 for Health Belief Model and Extended Parallel Processing Model constructs and the corresponding public comment sentiment and engagement. Thirty-three influencer accounts resulted in a total of 2,642 Instagram posts collected, along with 461,436 comments, which showed overall low use of constructs in both captions and images. Further, most posts used no combinations ( n  = 0 or 1 construct per post) of constructs in captions and images and very infrequently used captions that combined threat (severity and susceptibility) with cues to action and efficacy. Brand influencers and celebrities, politicians, and science communicators had above average post engagement while public health and government and news media had lower. Finally, most influencers saw the largest proportion of neutral sentiment comments. Crisis messages must be designed to include combinations of constructs that increase message acceptance and influence risk perception and efficacy to increase the adoption of recommended and mandated behaviours.

Fear appeals are a common tactic used in work safety interventions to motivate farmers to adopt safer behaviours. They begin by introducing a threat, followed by information on how to remove the threat. However, fear appeals tend to be ineffective when developed without a firm grasp of the cognitive processes underlying behavioural change. Although previous research on farm safety interventions have investigated fear appeals, they have focused on very narrow threats and behaviours, such as tractor or cow safety, while others have studied the threats but not the cognitive processing. Consequently, not enough is known about the range of threats that evoke fear, how farmers behave when under threat, or their general cognitive beliefs regarding self-efficacy, response cost and response efficacy. In In this study, 23 Swedish Farmers were interviewed and participated in a work safety intervention to identify the range of threats farmers perceive, and actions taken to remove those threats. The extended parallel processing model was used to gain insights into how farmers cognitively processed threats and their subsequent behaviour. Interestingly, it was found that farmers were more fearful of work safety threats related to family members and employees-yet the actions they took to reduce threats were mostly personal in nature. To help explain this finding, a typology of threat complexity was developed by the authors. It was found that simple, common, and direct threats to safety tended to lead to adaptive, threat-reducing behaviours, whereas complex, general, or indirect threats promoted more maladaptive behaviours that reduced fear, but not the threats.

Purpose - The purpose of this paper is to examine why mainstream information security awareness techniques have failed to evolve at the same rate as automated technical security controls and to suggest improvements based on psychology and safety science. Design/methodology/approach - The concepts of bounded rationality, mental models and the extended parallel processing model are examined in an information security context. Findings - There is a lack of formal methodologies in information security awareness for systematically identifying audience communication requirements. Problems with human behaviour in an information security context are assumed to be caused by a lack of facts available to the audience. Awareness, therefore, is largely treated as the broadcast of facts to an audience in the hope that behaviour improves. There is a tendency for technical experts in the field of information security to tell people what they think they ought to know (and may in fact already know). This \"technocratic\" view of risk communication is fundamentally flawed and has been strongly criticised by experts in safety risk communications as ineffective and inefficient. Practical implications - The paper shows how the approach to information security awareness can be improved using knowledge from the safety field. Originality/value - The paper demonstrates how advanced concepts from safety science can be used to improve information security risk communications.

This study adopted the extended parallel process model (EPPM) and dual process models to examine how recipients’ reactance proneness affected the appraisal of threat and efficacy, which, in turn, influenced their use of information-processing modes, attitudes, and behavioral intentions regarding the mitigation of microplastic pollutions in Taiwan. An experiment was conducted using 362 college students as the subjects. The results yielded three conclusions: (1) Fear-induced communication was an effective persuasive approach because this approach was more likely to guide the recipients to adopt a systematic mode to process messages. (2) Recipients’ reactance proneness was discovered to first affect their perceived threat and perceived efficacy, which, in turn, influenced their attitudes and behavioral intention regarding the prevention of microplastic pollution, demonstrating that individual differences mediate fear-appeal messages to affect persuasive outcomes. (3) Perceived threat was important for fear-appeal messages to obtain persuasive outcomes.

Health impact of exposure to air pollution is a public health concern. The aim of this study was to investigate an extended parallel process model (EPPM)-based mobile phone text message intervention for improving protective behaviors against air pollution among pregnant women. In this randomized controlled trial (IRCT2016102810804N8), 130 pregnant women were randomly assigned into either experimental or control groups. A valid and reliable questionnaire was used to collect data. Experimental group received mobile phone intervention on a daily basis for 2 months. Control group received usual care, only. Data were analyzed using SPSS 15 applying t test, chi-square, and Wilcoxon and Mann-Whitney U test. Although before intervention, there were no significant differences between different structures of EPPM (P > 0.05), after intervention, there were statistically significant differences between perceived severity, response efficacy, self-efficacy, and protective behaviors between two groups (P < 0.05). Implementing EPPM based-mobile phone intervention could promote protective behaviors against air pollution among pregnant women. The present study might be used as a framework for evidence-based health promotion regarding air pollution risk communication and self-care behaviors. Trial registration: IRCT2016102810804N8

This paper presents a parallel generalized finite element method ( GFEM ) that uses customized enrichment functions for applications where limited a priori knowledge about the solution is available. The procedure involves the parallel solution of local boundary value problems using boundary conditions from a coarse global problem. The local solutions are in turn used to enrich the global solution space using the partition of unity methodology. The parallel computation of local solutions can be implemented using a single pair of scatter–gather communications. Several numerical experiments demonstrate the high parallel efficiency of these computations. For problems requiring non-uniform mesh refinement and enrichment, load unbalance is addressed by defining a larger number of small local problems than the number of parallel processors and by sorting and solving the local problems based on estimates of their workload. A simple and effective estimate of the largest number of processors where load balance among processors is maintained is also proposed. Several three-dimensional fracture mechanics problems aiming at investigating the accuracy and parallel performance of the proposed GFEM are analyzed.

The characteristics of large-scale parallel applications are multi-paradigm and multi-grain parallel in essence. The key factor in improving the performance of parallel application systems is to determine suitable parallel paradigms and grains according to the nature of the practical problem. Therefore, it is necessary to provide multi-paradigm and multi-grain parallel programming interface for development of large-scale parallel application systems. This paper proposes a multi-paradigm and multi-grain parallel execution model integrated coarse-grain parallelism (paralleled by macro tasks), mid-grain parallelism (paralleled by basic program blocks), and fine-grain parallelism (paralleled in repetition blocks). This model also supports the task parallel, data parallel, and sequential executing. In this paper we also discuss the programming mechanism of this model by extended OpenMP specification. The extensions include computing resource partition, defining different grain task groups, mapping from task groups to the respective processor groups, out-of-core computing, asynchronous parallel I/O , and definition of sequential relationship of tasks. We compare the performance of different implementations of benchmark, using the same numerical algorithm but employing different programming approaches, including MPI, MPI+OpenMP, and our extended OpenMP. We also discuss a case based on SMP-Cluster and network storage architecture.

The accuracy of least squares calibration using option premiums and particle filtering of price data to find model parameters is determined. Derivative models using exponential Lévy processes are calibrated using regularized weighted least squares with respect to the minimal entropy martingale measure. Sequential importance resampling is used for the Bayesian inference problem of time series parameter estimation with proposal distribution determined using extended Kalman filter. The algorithms converge to their respective global optima using a highly parallelizable statistical optimization approach using a grid of initial positions. Each of these methods should produce the same parameters. We investigate this assertion.

When dealing with imperfect data and general models of dynamic systems, the best estimate is always sought in the presence of uncertainty or unknown parameters. In many cases, as the first attempt, the Extended Kalman filter (EKF) provides sufficient solutions to handling issues arising from nonlinear and non-Gaussian estimation problems. But these issues may lead unacceptable performance and even divergence. In order to accurately capture the nonlinearities of most real-world dynamic systems, advanced filtering methods have been created to reduce filter divergence while enhancing performance. Approaches, such as Gaussian sum filtering, grid based Bayesian methods and particle filters are well-known examples of advanced methods used to represent and recursively reproduce an approximation to the state probability density function (pdf). Some of these filtering methods were conceptually developed years before their widespread uses were realized. Advanced nonlinear filtering methods currently benefit from the computing advancements in computational speeds, memory, and parallel processing. Grid based methods, multiple-model approaches and Gaussian sum filtering are numerical solutions that take advantage of different state coordinates or multiple-model methods that reduced the amount of approximations used. Choosing an efficient grid is very difficult for multi-dimensional state spaces, and oftentimes expensive computations must be done at each point. For the original Gaussian sum filter, a weighted sum of Gaussian density functions approximates the pdf but suffers at the update step for the individual component weight selections. In order to improve upon the original Gaussian sum filter, Ref. [2] introduces a weight update approach at the filter propagation stage instead of the measurement update stage. This weight update is performed by minimizing the integral square difference between the true forecast pdf and its Gaussian sum approximation. By adaptively updating each component weight during the nonlinear propagation stage an approximation of the true pdf can be successfully reconstructed. Particle filtering (PF) methods have gained popularity recently for solving nonlinear estimation problems due to their straightforward approach and the processing capabilities mentioned above. The basic concept behind PF is to represent any pdf as a set of random samples. As the number of samples increases, they will theoretically converge to the exact, equivalent representation of the desired pdf. When the estimated qth moment is needed, the samples are used for its construction allowing further analysis of the pdf characteristics. However, filter performance deteriorates as the dimension of the state vector increases. To overcome this problem Ref. [5] applies a marginalization technique for PF methods, decreasing complexity of the system to one linear and another nonlinear state estimation problem. The marginalization theory was originally developed by Rao and Blackwell independently. According to Ref. [6] it improves any given estimator under every convex loss function. The improvement comes from calculating a conditional expected value, often involving integrating out a supportive statistic. In other words, Rao-Blackwellization allows for smaller but separate computations to be carried out while reaching the main objective of the estimator. In the case of improving an estimator's variance, any supporting statistic can be removed and its variance determined. Next, any other information that dependents on the supporting statistic is found along with its respective variance. A new approach is developed here by utilizing the strengths of the adaptive Gaussian sum propagation in Ref. [2] and a marginalization approach used for PF methods found in Ref. [7]. In the following sections a modified filtering approach is presented based on a special state-space model within nonlinear systems to reduce the dimensionality of the optimization problem in Ref. [2]. First, the adaptive Gaussian sum propagation is explained and then the new marginalized adaptive Gaussian sum propagation is derived. Finally, an example simulation is presented.