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Company business models are vulnerable to various contingencies in the business environment that may unexpectedly render their business logic ineffective. In particular, technological advancements, such as the Internet of things, big data, sharing economy and crowdsourcing, have enabled new forms of business models that can effectively and abruptly make traditional business models obsolete. By disrupting or even diminishing companies’ revenue streams, environmental contingencies may present a significant threat to business continuity (BC). Evaluating the resilience of business models against these contingencies should therefore be a core area of BC. However, existing BC approaches tend to focus on the continuity of the resources and processes through which a particular business model is accomplished in practice but omit the business model itself. We argue that in order for BC approaches to become holistic and strategic, business models need to become a part of the BC considerations, entailing an expansion of the scope of BC from value preservation to value creation. We propose an approach of Strategic Business Continuity Management, which consists of two parts: (1) sustaining the continuity of the company business model (value preservation) and (2) evaluating and modifying the business model (value creation). We illustrate conceptually the value creation part with an example drawn from the sharing economy.  相似文献   
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ABSTRACT

This study identified key somatic and demographic characteristics that benefit all swimmers and, at the same time, identified further characteristics that benefit only specific swimming strokes. Three hundred sixty-three competitive-level swimmers (male [n = 202]; female [n = 161]) participated in the study. We adopted a multiplicative, allometric regression model to identify the key characteristics associated with 100 m swimming speeds (controlling for age). The model was refined using backward elimination. Characteristics that benefited some but not all strokes were identified by introducing stroke-by-predictor variable interactions. The regression analysis revealed 7 “common” characteristics that benefited all swimmers suggesting that all swimmers benefit from having less body fat, broad shoulders and hips, a greater arm span (but shorter lower arms) and greater forearm girths with smaller relaxed arm girths. The 4 stroke-specific characteristics reveal that backstroke swimmers benefit from longer backs, a finding that can be likened to boats with longer hulls also travel faster through the water. Other stroke-by-predictor variable interactions (taken together) identified that butterfly swimmers are characterized by greater muscularity in the lower legs. These results highlight the importance of considering somatic and demographic characteristics of young swimmers for talent identification purposes (i.e., to ensure that swimmers realize their most appropriate strokes).  相似文献   
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Energy efficiency of public sector is an important issue in the context of smart cities due to the fact that buildings are the largest energy consumers, especially public buildings such as educational, health, government and other public institutions that have a large usage frequency. However, recent developments of machine learning within Big Data environment have not been exploited enough in this domain. This paper aims to answer the question of how to incorporate Big Data platform and machine learning into an intelligent system for managing energy efficiency of public sector as a substantial part of the smart city concept. Deep neural networks, Rpart regression tree and Random forest with variable reduction procedures were used to create prediction models of specific energy consumption of Croatian public sector buildings. The most accurate model was produced by Random forest method, and a comparison of important predictors extracted by all three methods has been conducted. The models could be implemented in the suggested intelligent system named MERIDA which integrates Big Data collection and predictive models of energy consumption for each energy source in public buildings, and enables their synergy into a managing platform for improving energy efficiency of the public sector within Big Data environment. The paper also discusses technological requirements for developing such a platform that could be used by public administration to plan reconstruction measures of public buildings, to reduce energy consumption and cost, as well as to connect such smart public buildings as part of smart cities. Such digital transformation of energy management can increase energy efficiency of public administration, its higher quality of service and healthier environment.  相似文献   
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The purpose of this study was to estimate the optimal body size, limb-segment length, girth or breadth ratios for 100-m backstroke mean speed performance in young swimmers. Sixty-three young swimmers (boys [n = 30; age: 13.98 ± 0.58 years]; girls [n = 33; age: 13.02 ± 1.20 years]) participated in this study. To identify the optimal body size and body composition components associated with 100-m backstroke speed performance, we adopted a multiplicative allometric log-linear regression model, which was refined using backward elimination. The multiplicative allometric model exploring the association between 100-m backstroke mean speed performance and the different somatic measurements estimated that biological age, sitting height, leg length for the lower-limbs, and two girths (forearm and arm relaxed girth) are the key predictors. Stature and body mass did not contribute to the model, suggesting that the advantage of longer levers was limb-specific rather than a general whole-body advantage. In fact, it is only by adopting multiplicative allometric models that the abovementioned ratios could have been derived. These findings highlighted the importance of considering somatic characteristics of young backstroke swimmers and can help swimming coaches to classify their swimmers and enable them to suggest what might be the swimmers’ most appropriate stroke (talent identification).  相似文献   
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In this ITEMS module, we introduce the generalized deterministic inputs, noisy “and” gate (G‐DINA) model, which is a general framework for specifying, estimating, and evaluating a wide variety of cognitive diagnosis models. The module contains a nontechnical introduction to diagnostic measurement, an introductory overview of the G‐DINA model, as well as common special cases, and a review of model‐data fit evaluation practices within this framework. We use the flexible GDINA R package, which is available for free within the R environment and provides a user‐friendly graphical interface in addition to the code‐driven layer. The digital module also contains videos of worked examples, solutions to data activity questions, curated resources, a glossary, and quizzes with diagnostic feedback.  相似文献   
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Abstract

The number of ebooks continues to grow in academic libraries. Ebooks have many advantages that make them attractive as a library resource. However, ebooks also have restrictions. These include use and sharing restrictions, license restrictions, and technological barriers. Some students also prefer print books for specific tasks. The following articles examine many aspects of ebooks in academic libraries, including student preference, overall ebook use, and the advantages and disadvantages of various ebook acquisition models.  相似文献   
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In this digital ITEMS module, Dr. Jeffrey Harring and Ms. Tessa Johnson introduce the linear mixed effects (LME) model as a flexible general framework for simultaneously modeling continuous repeated measures data with a scientifically defensible function that adequately summarizes both individual change as well as the average response. The module begins with a nontechnical overview of longitudinal data analyses drawing distinctions with cross-sectional analyses in terms of research questions to be addressed. Nuances of longitudinal designs, timing of measurements, and the real possibility of missing data are then discussed. The three interconnected components of the LME model—(1) a model for individual and mean response profiles, (2) a model to characterize the covariation among the time-specific residuals, and (3) a set of models that summarize the extent that individual coefficients vary—are discussed in the context of the set of activities comprising an analysis. Finally, they demonstrate how to estimate the linear mixed effects model within an open-source environment (R). The digital module contains sample R code, diagnostic quiz questions, hands-on activities in R, curated resources, and a glossary.  相似文献   
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The study evaluated the relationship among teaching, leadership, teacher certification, student and teacher number related factors, and index of economic, social and cultural status (ESCS), and science literacy from an international comparative perspective. 2015 dataset of the Programme for International Student Assessment for Turkey, Singapore, the United States, Korea, Italy, and Brazil was used. Two-level hierarchical linear models were established for analysis. Results revealed that school ESCS was the best predictor of science literacy across six countries and additionally teacher-related factors came to the forefront of science literacy performance. The study underlined policymakers should focus primarily on policies that increase equity in education. Furthermore, the effectiveness of investments made in factors related to leadership, teacher certification, the number of students and teachers, which contribute very little to science literacy performance, need to be more questioned. Inferences about the findings were discussed in detail, and recommendations for further research were made.  相似文献   
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