Business continuity of business models: Evaluating the resilience of business models for contingencies

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.     

Score Reporting for Examinees with Incomplete Data on Large‐Scale Educational Assessments

Technical difficulties occasionally lead to missing item scores and hence to incomplete data on computerized tests. It is not straightforward to report scores to the examinees whose data are incomplete due to technical difficulties. Such reporting essentially involves imputation of missing scores. In this paper, a simulation study based on data from three educational tests is used to compare the performances of six approaches for imputation of missing scores. One of the approaches, based on data mining, is the first application of its kind to the problem of imputation of missing data. The approach based on data mining and a multiple imputation approach based on chained equations led to the most accurate imputation of missing scores, and hence to most accurate score reporting. A simple approach based on linear regression performed the next best overall. Several recommendations are made regarding the reporting of scores to examinees with incomplete data.     

Machine learning based system for managing energy efficiency of public sector as an approach towards smart cities

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.     

An analysis on document length retrieval trends in language modeling smoothing

Document length is widely recognized as an important factor for adjusting retrieval systems. Many models tend to favor the retrieval of either short or long documents and, thus, a length-based correction needs to be applied for avoiding any length bias. In Language Modeling for Information Retrieval, smoothing methods are applied to move probability mass from document terms to unseen words, which is often dependant upon document length. In this article, we perform an in-depth study of this behavior, characterized by the document length retrieval trends, of three popular smoothing methods across a number of factors, and its impact on the length of documents retrieved and retrieval performance. First, we theoretically analyze the Jelinek–Mercer, Dirichlet prior and two-stage smoothing strategies and, then, conduct an empirical analysis. In our analysis we show how Dirichlet prior smoothing caters for document length more appropriately than Jelinek–Mercer smoothing which leads to its superior retrieval performance. In a follow up analysis, we posit that length-based priors can be used to offset any bias in the length retrieval trends stemming from the retrieval formula derived by the smoothing technique. We show that the performance of Jelinek–Mercer smoothing can be significantly improved by using such a prior, which provides a natural and simple alternative to decouple the query and document modeling roles of smoothing. With the analysis of retrieval behavior conducted in this article, it is possible to understand why the Dirichlet Prior smoothing performs better than the Jelinek–Mercer, and why the performance of the Jelinek–Mercer method is improved by including a length-based prior.

DVD在线租赁的数学模型

首先运用概率统计方法,对问卷调查结果和会员每月租赁DVD次数进行分析,合理地解决了网站既要尽可能满足消费者的意愿,同时又使成本低廉的矛盾；其次运用整数规划,根据100名会员的在线订单和网站手上20种DVD的现有张数,进行了定性地分配,并用分类规划,进行了定量地分配；再用定性反过来约束定量,井进行误差分析,根据会员要求和网站的宏观调控,得出了最优的分配方案,最后依据最优化原理导出递推关系,将整数规划转化为动态规划,进而求出每种DVD合理的购买量,并对这些DVD进行合理的分配；然后在计算机上用Lingo软件对模型进行灵敏度检验,模型还对网站DVD在线租赁提出几点建议供网站参考。     

构建教师网站 转变师生关系

信息技术的发展冲击着整个现代教育,更促使传统的教育思想、教育行为、教育体制、教学模式等发生一系列的变革。本文从建立良好师生关系的角度出发,指出建立教师网站的必要性及此类网站应具备的特性。最后还就构建教师网站应注意的问题作了一些探讨。     

几何中的比例中项问题

以“问题解决”理论为依据,对一类比例中项问题进行提炼,得到一种具有一般意义的模型,它含有某种模式,只要搞清题设、图形和结论之间的联系,或许会使解题思路更加清晰．     

高等职业教育课程模式设置的探讨

通过教学实践提出几种适合高职教育的课程模式:以问题为中心的综合基础课,以任务为中心的技术课程,以技术开发能力培养为中心的设计课程,以及用于提高学生职业适应性的微型课程等。