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2022 ◽  
Vol 6 ◽  
Author(s):  
Fredrik Breien ◽  
Barbara Wasson

STEAM education enables the cross-curricular study of subjects based on their naturally occurring relationships through holistic and integrated methods. Narratives are enablers of STEAM learning environments, something that is evident in the exploration of narrative learning from pre-recorded history until present. Narrative Digital Game-Based Learning (DGBL) use narratives to drive the game. The extended Ludo Narrative Variable Model (the Variable Model) is a narratological model for categorization of narrative DGBL. Empirical evidence from categorizing narrative DGBL on the Variable Model shows that there is a particular set of categories that incur positive effects on engagement, motivation, and learning. This article introduces the eLuna co-design framework that builds on these categories and empowers educators to participate alongside game developers in multidisciplinary design and development of narrative DGBL. eLuna comprises 1) a four-phase co-design method, and 2) a visual language to support the co-design and co-specification of the game to a blueprint that can be implement by game developers. Idun’s Apples, a narrative DGBL co-designed, co-specified, and implemented into a prototype using eLuna, is presented to illustrate the use of the method and visual language. Arguing that narrative DGBL are vessels for STEAM learning, seven eLuna co-designed games are examined to illustrate that they support STEAM. The article concludes that narrative DGBL co-designed using the eLuna framework provide high opportunity and potential for supporting STEAM, providing educators and game developers with a STEAM co-design framework that enforces positive effects on engagement, motivation, and learning.


Author(s):  
Allon van Uitert ◽  
Elle C. J. van de Wiel ◽  
Jordache Ramjith ◽  
Jaap Deinum ◽  
Henri J. L. M. Timmers ◽  
...  

Abstract Background Posterior retroperitoneoscopic adrenalectomy (PRA) has several advantages over transperitoneal laparoscopic adrenalectomy (TLA) regarding operative time, blood loss, postoperative pain, and recovery. However, it can be a technically challenging procedure. To improve patient selection for PRA, we developed a preoperative nomogram to predict operative time. Methods All consecutive patients with tumors of ≤ 7 cm and a body mass index (BMI) of < 35 kg/m2 undergoing unilateral PRA between February 2011 and March 2020 were included in the study. The primary outcome was operative time as surrogate endpoint for surgical complexity. Using ten patient variables, an optimal prediction model was created, with a best subsets regression analysis to find the best one-variable up to the best seven-variable model. Results In total 215 patients were included, with a mean age of 52 years and mean tumor size of 2.4 cm. After best subsets regression analysis, a four-variable nomogram was selected and calibrated. This model included sex, pheochromocytoma, BMI, and perinephric fat, which were all individually significant predictors. This model showed an ideal balance between predictive power and applicability, with an R2 of 38.6. Conclusions A four-variable nomogram was developed to predict operative time in PRA, which can aid the surgeon to preoperatively identify suitable patients for PRA. If the nomogram predicts longer operative time and therefore a more complex operation, TLA should be considered as an alternative approach since it provides a larger working space. Also, the nomogram can be used for training purposes to select patients with favorable characteristics when learning this surgical approach.


Actuators ◽  
2022 ◽  
Vol 11 (1) ◽  
pp. 21
Author(s):  
Alejandro Piñón ◽  
Antonio Favela-Contreras ◽  
Francisco Beltran-Carbajal ◽  
Camilo Lozoya ◽  
Graciano Dieck-Assad

Many industrial processes include MIMO (multiple-input, multiple-output) systems that are difficult to control by standard commercial controllers. This paper describes a MIMO case of a class of SISO-APC (single-input, single-output adaptive predictive controller) based upon an ARX (autoregressive with exogenous variable) model. This class of SISO-APC based on ARX models has been successfully and extensively used in many industrial applications. This approach aims to minimize the barriers between the theory of predictive adaptive control and its application in the industrial environment. The proposed MIMO-APC (MIMO adaptive predictive controller) performance is validated with two simulated processes: a quadrotor drone and the quadruple tank process. In the first experiment the proposed MIMO APC shows ISE-IAE-ITAE performance indices improvements of up to 25%, 25.4% and 38.9%, respectively. For the quadruple tank process the water levels in the lower tanks follow closely the set points, with the exception of a 13% overshoot in tank 1 for the minimum phase behavior response. The controller responses show significant performance improvements when compared with previously published MIMO control strategies.


2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Soumendu Biswas

PurposeDespite organizational socialization and support, contemporary managers often perceive employees to be less engaged and attached to their workplace, multiplying their workload with unsolicited vexations and worries. In this connection, the purpose of this paper is to explore and possibly confirm the ameliorative role of organizational identification as a mediator between employees' perceptions of organizational support and justice and their favorable association to their levels of engagement and attenuation of their intentions to quit.Design/methodology/approachSuitable theories such as the social exchange and fairness heuristics theories were examined to select and support the study constructs. Accordingly, the literature was reviewed to formulate the study hypotheses and connect them through a conceptual latent variable model (LVM). Data were collected from 402 full-time managerial executives all over India. The data thus collected were subjected to structural equation modeling (SEM) procedures.FindingsAll the measures used in this study had acceptable reliabilities as indicated by their Cronbach's Alpha values. Based on the SEM procedures all the study hypotheses and one of the competing LVMs labeled as LVM5 was finally accepted.Originality/valueThe distinctive feature of this study is the theoretical compilation of all the study constructs in one LVM and subsequent empirical verification of the same. This study is, perhaps, the first of its kind to examine the implications of such justice-based perceptions of social exchange relations between employees and their organizations in India more so, since it considers support and justice to complement each other as an interactive whole.


2022 ◽  
Vol 17 (1) ◽  
Author(s):  
Mohammad Hemmat Esfe ◽  
Soheyl Alidoust ◽  
Erfan Mohammadnejad Ardeshiri ◽  
Mohammad Hasan Kamyab ◽  
Davood Toghraie

AbstractIn this study, MWCNT-Al2O3 hybrid nanoparticles with a composition ratio of 50:50 in SAE50 base oil are used. This paper aims to describe the rheological behavior of hybrid nanofluid based on temperature, shear rate ($$\dot{\gamma })$$ γ ˙ ) and volume fraction of nanoparticles ($$\varphi$$ φ ) to present an experimental correlation model. Flowmetric methods confirm the non-Newtonian behavior of the hybrid nanofluid. The highest increase and decrease in viscosity ($${\mu }_{\rm nf}$$ μ nf ) in the studied conditions are measured as 24% and − 17%, respectively. To predict the experimental data, the five-point-three-variable model is used in the response surface methodology with a coefficient of determination of 0.9979. Margin deviation (MOD) of the data is determined to be within the permissible limit of − 4.66% < MOD < 5.25%. Sensitivity analysis shows that with a 10% increase in $$\varphi$$ φ at $$\varphi =$$ φ = 1%, the highest increase in $${\mu }_{\rm nf}$$ μ nf of 34.92% is obtained.


2022 ◽  
pp. 296-323
Author(s):  
Muhammad Arslan

In modern organizations, there is a separation between ownership and control of the firm. On the lenses of agency theory, this study statistically examines the relationship between ownership structure (i.e., ownership concentration and owner identity) and firm performance of non-financial listed firms of Pakistan by taking firm-level control variables of size, age, liquidity, financial leverage, and growth of the firm. Secondary data is collected from annual reports of 65 non-financial listed firms for the year 2008 to 2012. The least-square dummy variable model followed by the random effect model has been employed to statistically determining the impact of ownership structure on firm performance. The results of the least square dummy variable model reveal that the ownership concentration has a significant positive impact on firm performance. The owner identity (such as dispersed, family, institutional, and government ownership) has a significant causal effect on firm performance as indicated from t and p values.


2022 ◽  
Vol 10 (1) ◽  
pp. 15-24
Author(s):  
Bhuwaneshwar Kumar Gupt ◽  
Mankupar Swer ◽  
Md. Irphan Ahamed ◽  
B. K. Singh ◽  
Kh. Herachandra Singh

2021 ◽  
Author(s):  
Jihong Zhang ◽  
Terry Ackerman ◽  
Yurou Wang

Fitting item response theory (IRT) models using the generalized mixed logistic regression model (GLMM) has become more popular in large-scale assessment because GLMM allows combining complicated multilevel structures (i.e., students are nested in classrooms which are nested in schools) with IRT measurement models. However, the estimation accuracy of item parameters between these two models is not well examined. This study aimed to compare the estimation results of the GLMM based 2PL model (using the PLmixed R package) with the traditional IRT model (using flexMIRT software) under different sample sizes (N= 500, 1000, 5000) and test length (J = 15, 21) conditions. The simulation results showed that for both the GLMM-based method and the traditional method, item threshold estimates had lower bias than item discrimination parameters. We also found that according to the simulation study, GLMM estimates via PLmixed had lower accuracy than traditional IRT modeling via flexMIRT for items with high discrimination.


2021 ◽  
pp. 002199832110588
Author(s):  
Miguel Tomás ◽  
Said Jalali ◽  
Alexandre Silva de Vargas

This article investigates the dependency of temperature on electrical resistance (R) change in micro carbon fiber polymer composites (MCFPC), for further development as an Internet of Things sensor from previous research works. Three mixtures were prepared using Dow Corning’s Silastic 145 as base polymer and made vary fiber content weight percentages: fiber diameter to length ratio ∅⁄l 0.13 and carbon fiber content of 13%; ∅⁄l:0.66 and carbon fiber contents of 40% and 50%. Composites tested were submitted to temperature loading, with a constant strain of 0.0%, for assessment of R when a change in the composite’s temperature occurs. The composite response was observed to follow an Arrhenius function, for temperatures ranging from −10°C to 40°C. The apparent activation energy was calculated to evaluate further differences between carbon fiber contents and the sensitivity factor, [Formula: see text] due to temperature is determined. The specimens were also tested with a constant strain of 2.86% to assess creep. It was found that creep and R, over the period of time in the analysis, best fit a discrete latent variable model. The sensitivity factor change is determined in regard to stress relaxation, [Formula: see text]. The properties of MCFPC investigated here can be used to establish relationships between electrical resistance outputs and environmental loading conditions for this type of composites, enabling the possibility of deployment as part of a management system network for structural monitoring with real-time data acquisition.


2021 ◽  
Author(s):  
Christopher J Fariss ◽  
Therese Anders ◽  
Jonathan Markowitz ◽  
Miriam Barnum

Gross Domestic Product (GDP), GDP per capita, and population are central to the study of politics and economics broadly, and conflict processes in particular. Despite the prominence of these variables in empirical research, existing data lack historical coverage and are assumed to be measured without error. We develop a latent variable modeling framework that expands data coverage (1500 A.D--2018 A.D) and, by making use of multiple indicators for each variable, provides a principled framework to estimate uncertainty for values for all country-year variables relative to one another. Expanded temporal coverage of estimates provides new insights about the relationship between development and democracy, conflict, repression, and health. We also demonstrate how to incorporate uncertainty in observational models. Results show that the relationship between repression and development is weaker than models that do not incorporate uncertainty suggest. Future extensions of the latent variable model can address other forms of systematic measurement error with new data, new measurement theory, or both.


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