Continuous Prediction Model of Carbon Content in 120 t Converter Blowing Process

A continuous prediction model of carbon content of 120 t BOF is established in this paper. Based on the three-stage decarburization theory and combined with the production process of 120 t converter, the effects of oxygen lance height and top blowing oxygen flow rate are also considered in the model. The explicit finite difference method is used to realize continuous prediction of carbon content in the converter blowing process. The model parameters such as ultimate carbon content in molten pool are calculated according to the actual data of 120 t BOF, which improves the hit rate of the model. Process verification and end-point verification for the continuous prediction model have been carried out, and the results of process verification indicate that the continuous prediction model established in the paper basically accords with the actual behavior of decarburization. Moreover, the hit ratio of the continuous prediction model reached 85% for the prediction of end-point carbon content within a tolerance of ±0.02%.

Understanding the Dynamics of Staff Reallocation to Clinical Practice During the First Wave of COVID-19 – a Qualitative Study

BackgroundDuring the COVID-19 pandemic, one responsive strategy to ensure hospital staff capacity was reallocation of staff between departments. Unpredicted factors may influence how the strategy is executed. Knowledge of potential moderating factors is essential to improve future staff contingency plans. To understand barriers and promoters of staff realloctation, this study explored the dynamics of reallocating staff from departments with low activity to clinical practice during the first wave of the COVID-19 pandemic at a 530-bed university hospital in the Capital Region of Denmark. MethodsWe used a mixed-methods explanatory design with sequential data collection and analysis. This paper primarily describes the qualitative part of the study, which consisted of six interviews with staff reallocated to clinical practice as part of the staff contingency plan, and seven interviews with leaders of departments that contributed with staff for reallocation. Data was analyzed using inductive content analysis.ResultsThe results showed that the execution of a staff contingency plan during a pandemic is influenced by a complex set of structural, perceptional, social, individual, and psychological moderating factors. Although staff felt obligated and motivated to cover shifts, their actual behavior and experience was influenced by factors such as uncertainty about tasks, family obligations, other work-related tasks, the contingency plan set-up, how the contingency plan, roles, and sense of urgency were interpreted by staff and leaders, and how the leaders prioritized tasks and staff time. Introduction to the unit and tasks, the feeling of being needed, voluntary participation, transparency, collegial sparring, and familiarity with the workplace were factors that promoted a positive experience.ConclusionsThis study identified a variety of complex moderating factors, which should be considered when hospital contingency plans are developed. The study highlights the importance of understanding how reallocated staff and leaders experience and make interpretations and adjustments to a given plan, as this may have great significance for how the contingency plan is put into practice. Future staff contingency plans should take these factors into consideration to make better use of human resources in times of a crisis and to improve staff’s experience with reallocation.

Theoretical and Empirical Verification of Electrical Impedance Matching Method for High-Power Transducers

In our prior study, a systematic approach was used to devise Langevin transducers for high-power applications where the energy efficiency was not considered in the design criteria. In this paper, the impedance matching methods are thus proposed to evaluate what matching topology is appropriate for their use. Both the series inductor scheme and low pass filter composed of a series inductor and shunt capacitor are examined as matching circuits. According to MATLAB simulation, the resonance frequency is seen at 36.79 kHz due to a series L circuit, and its associated impedance is reduced by 70.45% from that of its non-matching condition. The measured resonance frequency is 36.77 kHz and the corresponding impedance is decreased by 59.52%. Furthermore, the acoustic pressure is measured to determine the effect of the matching circuit on the transducer’s actual behavior. The transducer with a series L circuit shows more efficient matching results, 2.28 kPa of positive acoustic pressure is emitted without matching and 3.35 kPa is emitted with a series L element, respectively. As a result, this study demonstrates how to evaluate the influence of matching circuits by using our customized approach rather than commercial SPICE programs, as well as how to experimentally verify the acoustic behavior of high-power Langevin transducers.

A model for examining middle school students’ STEM integration behavior in a national technology competition

Background Research on teaching and learning for science, technology, engineering, and mathematics (STEM) subjects has increased, and has demonstrated the importance of integrating interdisciplinary knowledge and skills. Our research model was based on the theory of planned behavior (TPB) and the data were analyzed by partial least squares-structural equation modeling. The present study aims to identify factors that play an important role in students’ ability to integrate STEM knowledge and skills. Results Data were collected from participants who had won awards in local contests and represented their regions in a national technology competition. The reliability and validity of our instrument, the Students’ STEM Integration Scale, were verified. The findings demonstrated that students’ intentions to integrate STEM knowledge and skills to solve complex problems can be predicted by their attitude and perceived behavioral control. Conclusions This work highlights factors which are associated with students’ intentions to integrate interdisciplinary knowledge and skills, and serves as a reference for research on the gap between intentions and actual behavior. The findings could help teachers and instructors design STEM-based activities to enhance students’ attitudes, perceived behavioral control, and intentions, to improve their ability to integrate STEM knowledge and skills.

Once Good, Always Good? Testing Nudge’s Spillovers on Pro Environmental Behavior

It is a common assumption to believe that encouraging pro environmental behavior (PEB) in one domain would lead to increased PEB in other domains (best-case scenario) or just be restricted to the initial targeted domain (worst-case scenario). Evidence from a rapidly growing literature on moral licensing suggests that interventions targeting behavioral change could lead to an even worse scenario, with individuals starting to underperform in one domain, as a compensation for their good performance in other domains. We propose to study the dynamic of PEBs when individuals are exposed to a specific nudge (priming) via an original experiment designed to capture actual behavior. We found that priming could increase PEB, but does not thwart moral licensing. Primed individuals end up doing worse than non-primed individual under a moral licensing condition. A more comprehensive view of the mechanisms underlying behavioral change is essential to support sustainable policies.

Evaluation of seismic forces under modified structural schemes in the process of vibrations

The aim of the work - development of one of the possible methods for seismic analysis that considers the inelastic behavior of structures under seismic loads. This requires the development of seismic analysis methods that take into account the change (decrease) in the bearing capacity or the destruction of individual elements until the final loss of the bearing capacity of the structure. Methods. The dependences and algorithms include determining seismic forces using the method of normal forms, which until now is the main one in solving problems of the seismic resistance theory in seismic regions, calculation formulas to calculate seismic forces at each time step are presented in the form of expansions into natural vibration modes, which regard the changes in the design scheme. The calculation is repeated at each time step as a static calculation for the action of seismic forces determined at the previous stage, before the building collapses. Results. The developed dependencies and algorithms allow to consider changes in the design scheme during vibrations at each time step, changes in the dynamic properties of the building and, as a result, the values of seismic forces. The value of the coefficient of inelastic work of structures K 1, which are given in regulatory documents, do not give fully correspond to the actual behavior of the structure under seismic influences. The proposed calculation method allows to determine the estimated values of seismic forces and their distribution taking into account the influence of damage of elements and the appearance of inelastic zones in the design process of fluctuations at each time step and to assess the dynamic behavior of the building.

The effect of network structure on the purchase of virtual goods on social networking services

PurposeThis paper aims to identify the effect of social structure variables on the purchase of virtual goods. Using field data, it also tests whether their effects on a social networking service are dynamic.Design/methodology/approachTo achieve the research objectives, the authors have applied the random effects panel Tobit model with actual time-series corporate data to explain a link between network structure factors and actual behavior on social networking services.FindingsThe authors have found that various network structure variables such as in-degree, in-closeness centrality, out-closeness centrality and clustering coefficients are significant predictors of virtual item sales; while the constraint is marginally significant, out-degree is not significant. Furthermore, these variables are time-varying, and the dynamic model performs better in a model fit than the static one.Practical implicationsThe findings will help social networking service (SNS) operators realize the importance of understanding network structure variables and personal motivations or the behavior of consumers.Originality/valueThis study provides implications in that it uses various and dynamic network structure variables with panel data.

The Dark Side of Interpersonal Touch: Physical Contact Leads to More Non-compliance With Preventive Measures to COVID-19

Following preventive measures is crucial for slowing the rate of COVID-19 spread. To date, most research has focused on the role of individual differences and personality in compliance with preventive measures to COVID-19. Building on findings that interpersonal touch instills a feeling of security, we propose that interpersonal touching behavior, an underexplored factor tied to social interaction, leads to more breaches of coronavirus restrictions by inducing security feelings. In a lab experiment (Experiment 1) and a field study (Experiment 2), we demonstrated that a female experimenter’s fleeting and comforting pat on the shoulder made people less willing to abide by preventive measures in their self-report and actual behavior. Further, we excluded a potential alternative explanation that touch intervention by the experimenter presents the defiance of COVID-19 rules because the effect cannot be observed when the touch consists of a handshake rather than a comforting pat on the shoulder (Experiment 3). Finally, consistent with our theoretical perspective, the results revealed that sense of security mediated the effect of interpersonal touch on violation of instructions to follow coronavirus precautions. Taken together, interpersonal touch not only enhances trust and security, but also can push people away from health guidelines.

Understanding Farmers' Behavior and Their Decision-Making Process in the Context of Cattle Diseases: A Review of Theories and Approaches

Understanding farmers' behavior regarding disease control is essential to successfully implement behavior change interventions that improve uptake of best practices. A literature review was conducted to identify theoretical underpinnings, analytical methodologies, and key behavioral determinants that have been described to understand farmers' behavior in disease control and prevention on cattle farms. Overall, 166 peer-reviewed manuscripts from studies conducted in 27 countries were identified. In the past decade, there were increasing reports on farmers' motivators and barriers, but no indication of application of appropriate social science methods. Furthermore, the majority (58%) of reviewed studies lacked a theoretical framework in their study design. However, when a theoretical underpinning was applied, the Theory of Planned Behavior was most commonly used (14% of total). The complexity of factors impacting farmers' behavior was illustrated when mapping all described key constructs of the reviewed papers in behavior change frameworks, such as the socioecological framework and the Capability, Opportunity and Motivation Behavior (COM-B) model. Constructs related to personal influences and relationships between farmers and veterinarians were overrepresented, whereas constructs related to other interpersonal and contextual environments were not extensively studied. There was a general lack of use of validated scales to measure constructs and empirically validated theoretical frameworks to understand and predict farmers' behavior. Furthermore, studies mainly focused on measurements of intention of stakeholder behavior rather than actual behavior, although the former is a poor predictor of the latter. Finally, there is still a lack of robust evidence of behavior change interventions or techniques that result in a successful change in farmers' behavior. We concluded that for a sustainable behavior change, studies should include wider constructs at individual, interpersonal, and contextual levels. Furthermore, the use of empirically validated constructs and theoretical frameworks is encouraged. By using coherent frameworks, researchers could link constructs to design interventions, and thereby take the first step toward theory-driven, evidence-based interventions to influence farmers' behavior for disease control.

Measuring Readiness to Change Substance Use, Alcohol Use, and Cannabis Use: An Experimental Manipulation of Cognitive Effort

The Transtheoretical Model supports that readiness to change should predict actual substance-related behavior change, though this relationship is surprisingly modest. Across several behavioral domains, individuals tend to have unrealistic expectations regarding the amount of effort and time required to successfully change one’s behaviors, dubbed the False Hope Syndrome. Based on the False Hope Syndrome, we expect that the standard method of measuring self-reported readiness to change is inaccurate and overestimated. To test this hypothesis, we experimentally manipulated the level of cognitive effort or focus on the practical implications of changing substance use prior to completing readiness to change measures. College students from a large southwestern university who reported using substances in the past 30 days (n = 345) were recruited from a psychology department participant pool to complete this experimental study online. Participants were randomized to one of three conditions: 1) standard low effort condition, 2) medium effort condition (had to select likes/dislikes of substance use, and negative consequences of changing one’s use), and 3) high effort condition (also provided text responses to how they would handle difficult situations related to changing their substance use). We conducted one-way ANOVAs with Tukey post-hoc comparisons to examine differences on three measures of readiness to change: the University of Rhode Island Change Assessment (URICA) scale for overall substance use as well as the readiness and motivation rulers for overall substance use, alcohol use, and cannabis use. Contrary to our hypothesis, all significant statistical tests supported higher cognitive effort conditions reporting higher readiness to change. Although effect sizes were modest, higher cognitive effort appeared to increase self-reported readiness to change substance use. To address the present study’s limitations, additional work is needed to test how self-reported readiness to change relates to actual behavior change when assessed under the different effort conditions.