Background: Modern modeling techniques may potentially provide more accurate predictions of dichotomous outcomes than classical techniques. Objective: In this study, we aimed to examine the predictive performance of eight modeling techniques to predict mortality by frailty. Methods: We performed a longitudinal study with a 7-year follow-up. The sample consisted of 479 Dutch community-dwelling people, aged 75 years and older. Frailty was assessed with the Tilburg Frailty Indicator (TFI), a self-report questionnaire. This questionnaire consists of eight physical, four psychological, and three social frailty components. The municipality of Roosendaal, a city in the Netherlands, provided the mortality dates. We compared modeling techniques, such as support vector machine (SVM), neural network (NN), random forest, and least absolute shrinkage and selection operator, as well as classical techniques, such as logistic regression, two Bayesian networks, and recursive partitioning (RP). The area under the receiver operating characteristic curve (AUROC) indicated the performance of the models. The models were validated using bootstrapping. Results: We found that the NN model had the best validated performance (AUROC=0.812), followed by the SVM model (AUROC=0.705). The other models had validated AUROC values below 0.700. The RP model had the lowest validated AUROC (0.605). The NN model had the highest optimism (0.156). The predictor variable “difficulty in walking” was important for all models. Conclusions: Because of the high optimism of the NN model, we prefer the SVM model for predicting mortality among community-dwelling older people using the TFI, with the addition of “gender” and “age” variables. External validation is a necessary step before applying the prediction models in a new setting.
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A stream of literature is emerging where network development and business modeling intersect. Various authors emphasize that networks influence business models. This paper extends this stream of literature by studying two cases in which we analyze how business modeling and networking interact over time. We propose the concept ‘value shaping’ to describe this interaction. Value shaping refers to the mutually constitutive process in which on the one hand networking helps to refine and improve the overall business model and on the other hand an improved business model spurs expansion of the network. We identify five micro-level processes through which value shaping occurs. Value shaping is particularly relevant for sustainability-oriented innovations, to help clarify all the types of financial, social and environmental value to which a business model may contribute.
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Full text beschikbaar met HU-account. Since the 2010s, various companies have begun to manufacture wearable smartwatch devices, but the current sales of these products are not impressive. This study investigates how the limitations of the smartwatch are related to perceptual discomforts. Theoretically, this study evaluates the claim that the discomfort that users appear to have with the smartwatch stem from failed remediation. Users perceive the smartwatch more as a set of functional sensors rather than a watch or smartphone. Specifically, from the remediation perspective, the authors asked how users perceive the functions of the smartwatch. This study used dynamic topic modeling for topics on the smartwatch on Reddit. This study reports that the smartwatch has failed to provide a proper way to use the remediated content that it provides. Suggestions for future studies are addressed.
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Agent-based modeling (ABM) is a widely used method for evaluating demand response (DR) strategies. To comprehensively assess the impact of DR strategies on a district cooling system, the integration of building managers’ DR behavior is essential. However, most ABM studies focus on technical optimization while overlooking the behavioral factors that may exist in building managers’ decision-making processes. To address this gap, this paper introduces an agent-based model using the belief-desire-intention (BDI) framework to simulate building managers’ air-conditioning setpoint adjustment behavior under DR, integrating the reasoning capabilities and irrational behavior factors.
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Coupling beams between shear walls are one of the key elements for energy dissipation in tall buildings. A representative mathematical model of coupling beam should represent flexure, shear and interface slip/extension mechanisms simultaneously. This goal can be achieved by using either detailed finite element models or by using macro models. This paper presents a review of various macro model alternatives for diagonally reinforced coupling beams in the literature. Three distinct methods have been reviewed in terms of their modeling techniques, the cyclic response overlap and the amount of cumulative plastic energy dissipated based on the results of previously performed tests. Through an analytical study, adequately accurate results can be captured by using macro models, although they are simpler in practice compared to sophisticated micro models. This study shows that, by modifying ultimate shear capacities where concrete material between diagonal bundles is adequately confined, it is possible to capture a more realistic result and a better approximation to the actual responses. It is also concluded that a simpler numerical model for diagonally reinforced coupling beams can be achieved by introducing linear part of slip/extension behavior into elastic part of the beam. It is observed, as a result of this study, that the ratio of effective stiffness to that of the gross cross-sectional one ranges from 0.04 to 0.14 in diagonally reinforced coupling beams depending on the aspect ratio and the beam strength parameters.
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Eye movement modeling examples are instructional videos in which a model (this can be an expert, a teacher, or a peer student) demonstrates and usually (though not necessarily) verbally explains how to perform a task. In contrast to regular video examples, however, students do not only see the model’s actions but also a visualization of the model’s eye movements superimposed on the video (i.e., the student sees where the model is looking at any given moment, indicated, for instance, by means of a circle or dot). Seeing where the model is looking at any given moment can serve two functions: 1) it synchronizes the students’ gaze with the model’s gaze, which can aid the comprehension of the model’s demonstration and explanation, and 2) it can give students insight into the perceptual or cognitive strategies the model uses to perform the task, which would otherwise not be observable for them. In this chapter, evidence on the effectiveness of eye movement modeling examples for attaining these two goals is reviewed, followed by a critical discussion and avenues for future research on this topic.
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Accurate modeling of end-users’ decision-making behavior is crucial for validating demand response (DR) policies. However, existing models usually represent the decision-making behavior as an optimization problem, neglecting the impact of human psychology on decisions. In this paper, we propose a Belief-Desire-Intention (BDI) agent model to model end-users’ decision-making under DR. This model has the ability to perceive environmental information, generate different power scheduling plans, and make decisions that align with its own interests. The key modeling capabilities of the proposed model have been validated in a household end-user with flexible loads
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Front-of-pack nutrition labels can help consumers to make healthier choices and stimulate healthier product development. This is the first modeling study to investigate the potential impact on cholesterol levels of consuming a diet consisting of products that comply with the criteria for a ‘healthier choice logo’.
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Abstract Background: COVID-19 was first identified in December 2019 in the city of Wuhan, China. The virus quickly spread and was declared a pandemic on March 11, 2020. After infection, symptoms such as fever, a (dry) cough, nasal congestion, and fatigue can develop. In some cases, the virus causes severe complications such as pneumonia and dyspnea and could result in death. The virus also spread rapidly in the Netherlands, a small and densely populated country with an aging population. Health care in the Netherlands is of a high standard, but there were nevertheless problems with hospital capacity, such as the number of available beds and staff. There were also regions and municipalities that were hit harder than others. In the Netherlands, there are important data sources available for daily COVID-19 numbers and information about municipalities. Objective: We aimed to predict the cumulative number of confirmed COVID-19 infections per 10,000 inhabitants per municipality in the Netherlands, using a data set with the properties of 355 municipalities in the Netherlands and advanced modeling techniques. Methods: We collected relevant static data per municipality from data sources that were available in the Dutch public domain and merged these data with the dynamic daily number of infections from January 1, 2020, to May 9, 2021, resulting in a data set with 355 municipalities in the Netherlands and variables grouped into 20 topics. The modeling techniques random forest and multiple fractional polynomials were used to construct a prediction model for predicting the cumulative number of confirmed COVID-19 infections per 10,000 inhabitants per municipality in the Netherlands. Results: The final prediction model had an R2 of 0.63. Important properties for predicting the cumulative number of confirmed COVID-19 infections per 10,000 inhabitants in a municipality in the Netherlands were exposure to particulate matter with diameters <10 μm (PM10) in the air, the percentage of Labour party voters, and the number of children in a household. Conclusions: Data about municipality properties in relation to the cumulative number of confirmed infections in a municipality in the Netherlands can give insight into the most important properties of a municipality for predicting the cumulative number of confirmed COVID-19 infections per 10,000 inhabitants in a municipality. This insight can provide policy makers with tools to cope with COVID-19 and may also be of value in the event of a future pandemic, so that municipalities are better prepared.
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Although near-peer role modeling (NPRM) has been suggested as an effective pedagogical intervention for boosting confidence, motivation, and self-efficacy, few studies have examined its connection with learner needs and well-being utilizing an established psychological framework. The present study investigates the pedagogical role of NPRM within English classes in Japanese higher education from the perspective of basic psychological need (BPN) satisfaction and frustration. In this two-phase explanatory mixed methods study, two quantitative scales were utilized to assess the significance of the connections between NPRM and six subcategories of BPN satisfaction or frustration. Subsequently, a qualitative investigation with a more limited sample size was conducted to elucidate and expand upon these associations. The quantitative findings revealed NPRM to be a significant predictor of students’ autonomy and relatedness satisfaction and exhibited a negative correlation with students' autonomy and relatedness frustration. However, no discernible association was observed between NPRM and competence satisfaction or frustration. The qualitative data revealed that the students’ mixed feelings of competence may have stemmed from low confidence and L2 self-concept with some students comparing themselves unfavorably to near-peer role models. The study highlights the need for NPRM interventions to be accompanied by instruction related to learner beliefs or growth mindsets.
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