A modified semi-oriented radial measure for target setting with negative data

Over the last few years Data Envelopment Analysis (DEA) has been gaining increasing popularity as a tool for measuring efficiency and productivity of Decision Making Units (DMUs). Conventional DEA models assume non-negative inputs and outputs. However, in many real applications, some inputs and/or outputs can take negative values. Recently, Emrouznejad et al. [6] introduced a Semi-Oriented Radial Measure (SORM) for modelling DEA with negative data. This paper points out some issues in target setting with SORM models and introduces a modified SORM approach. An empirical study in bank sector demonstrates the applicability of the proposed model. © 2014 Elsevier Ltd. All rights reserved.

Handling varying amounts of missing data when classifying mental-health risk levels

One of the main challenges of classifying clinical data is determining how to handle missing features. Most research favours imputing of missing values or neglecting records that include missing data, both of which can degrade accuracy when missing values exceed a certain level. In this research we propose a methodology to handle data sets with a large percentage of missing values and with high variability in which particular data are missing. Feature selection is effected by picking variables sequentially in order of maximum correlation with the dependent variable and minimum correlation with variables already selected. Classification models are generated individually for each test case based on its particular feature set and the matching data values available in the training population. The method was applied to real patients' anonymous mental-health data where the task was to predict the suicide risk judgement clinicians would give for each patient's data, with eleven possible outcome classes: zero to ten, representing no risk to maximum risk. The results compare favourably with alternative methods and have the advantage of ensuring explanations of risk are based only on the data given, not imputed data. This is important for clinical decision support systems using human expertise for modelling and explaining predictions.

Conceptual Building Information Modelling Framework for Whole‐house Refurbishment based on LCC and LCA

The UK government aims at achieving 80% CO2 emission reduction by 2050 which requires collective efforts across all the UK industry sectors. In particular, the housing sector has a large potential to contribute to achieving the aim because the housing sector alone accounts for 27% of the total UK CO2 emission, and furthermore, 87% of the housing which is responsible for current 27% CO2 emission will still stand in 2050. Therefore, it is essential to improve energy efficiency of existing housing stock built with low energy efficiency standard. In order for this, a whole‐house needs to be refurbished in a sustainable way by considering the life time financial and environmental impacts of a refurbished house. However, the current refurbishment process seems to be challenging to generate a financially and environmentally affordable refurbishment solution due to the highly fragmented nature of refurbishment practice and a lack of knowledge and skills about whole‐house refurbishment in the construction industry. In order to generate an affordable refurbishment solution, diverse information regarding costs and environmental impacts of refurbishment measures and materials should be collected and integrated in right sequences throughout the refurbishment project life cycle among key project stakeholders. Consequently, various researchers increasingly study a way of utilizing Building Information Modelling (BIM) to tackle current problems in the construction industry because BIM can support construction professionals to manage construction projects in a collaborative manner by integrating diverse information, and to determine the best refurbishment solution among various alternatives by calculating the life cycle costs and lifetime CO2 performance of a refurbishment solution. Despite the capability of BIM, the BIM adoption rate is low with 25% in the housing sector and it has been rarely studied about a way of using BIM for housing refurbishment projects. Therefore, this research aims to develop a BIM framework to formulate a financially and environmentally affordable whole‐house refurbishment solution based on the Life Cycle Costing (LCC) and Life Cycle Assessment (LCA) methods simultaneously. In order to achieve the aim, a BIM feasibility study was conducted as a pilot study to examine whether BIM is suitable for housing refurbishment, and a BIM framework was developed based on the grounded theory because there was no precedent research. After the development of a BIM framework, this framework was examined by a hypothetical case study using BIM input data collected from questionnaire survey regarding homeowners’ preferences for housing refurbishment. Finally, validation of the BIM framework was conducted among academics and professionals by providing the BIM framework and a formulated refurbishment solution based on the LCC and LCA studies through the framework. As a result, BIM was identified as suitable for housing refurbishment as a management tool, and it is timely for developing the BIM framework. The BIM framework with seven project stages was developed to formulate an affordable refurbishment solution. Through the case study, the Building Regulation is identified as the most affordable energy efficiency standard which renders the best LCC and LCA results when it is applied for whole‐house refurbishment solution. In addition, the Fabric Energy Efficiency Standard (FEES) is recommended when customers are willing to adopt high energy standard, and the maximum 60% of CO2 emissions can be reduced through whole‐house fabric refurbishment with the FEES. Furthermore, limitations and challenges to fully utilize BIM framework for housing refurbishment were revealed such as a lack of BIM objects with proper cost and environmental information, limited interoperability between different BIM software and limited information of LCC and LCA datasets in BIM system. Finally, the BIM framework was validated as suitable for housing refurbishment projects, and reviewers commented that the framework can be more practical if a specific BIM library for housing refurbishment with proper LCC and LCA datasets is developed. This research is expected to provide a systematic way of formulating a refurbishment solution using BIM, and to become a basis for further research on BIM for the housing sector to resolve the current limitations and challenges. Future research should enhance the BIM framework by developing more detailed process map and develop BIM objects with proper LCC and LCA Information.

The data type of spatial objects

A spatial object consists of data assigned to points in a space. Spatial objects, such as memory states and three dimensional graphical scenes, are diverse and ubiquitous in computing. We develop a general theory of spatial objects by modelling abstract data types of spatial objects as topological algebras of functions. One useful algebra is that of continuous functions, with operations derived from operations on space and data, and equipped with the compact-open topology. Terms are used as abstract syntax for defining spatial objects and conditional equational specifications are used for reasoning. We pose a completeness problem: Given a selection of operations on spatial objects, do the terms approximate all the spatial objects to arbitrary accuracy? We give some general methods for solving the problem and consider their application to spatial objects with real number attributes. © 2011 British Computer Society.

Numerical modelling of complex femtosecond laser inscribed fiber gratings - comparison wih experiment

We present experimental studies and numerical modeling based on a combination of the Bidirectional Beam Propagation Method and Finite Element Modeling that completely describes the wavelength spectra of point by point femtosecond laser inscribed fiber Bragg gratings, showing excellent agreement with experiment. We have investigated the dependence of different spectral parameters such as insertion loss, all dominant cladding and ghost modes and their shape relative to the position of the fiber Bragg grating in the core of the fiber. Our model is validated by comparing model predictions with experimental data and allows for predictive modeling of the gratings. We expand our analysis to more complicated structures, where we introduce symmetry breaking; this highlights the importance of centered gratings and how maintaining symmetry contributes to the overall spectral quality of the inscribed Bragg gratings. Finally, the numerical modeling is applied to superstructure gratings and a comparison with experimental results reveals a capability for dealing with complex grating structures that can be designed with particular wavelength characteristics.

Modelling stock volatilities during financial crises:a time varying coefficient approach

We examine how the most prevalent stochastic properties of key financial time series have been affected during the recent financial crises. In particular we focus on changes associated with the remarkable economic events of the last two decades in the volatility dynamics, including the underlying volatility persistence and volatility spillover structure. Using daily data from several key stock market indices, the results of our bivariate GARCH models show the existence of time varying correlations as well as time varying shock and volatility spillovers between the returns of FTSE and DAX, and those of NIKKEI and Hang Seng, which became more prominent during the recent financial crisis. Our theoretical considerations on the time varying model which provides the platform upon which we integrate our multifaceted empirical approaches are also of independent interest. In particular, we provide the general solution for time varying asymmetric GARCH specifications, which is a long standing research topic. This enables us to characterize these models by deriving, first, their multistep ahead predictors, second, the first two time varying unconditional moments, and third, their covariance structure.

Assessing the queuing process using data envelopment analysis:an application in health centres

Queuing is one of the very important criteria for assessing the performance and efficiency of any service industry, including healthcare. Data Envelopment Analysis (DEA) is one of the most widely-used techniques for performance measurement in healthcare. However, no queue management application has been reported in the health-related DEA literature. Most of the studies regarding patient flow systems had the objective of improving an already existing Appointment System. The current study presents a novel application of DEA for assessing the queuing process at an Outpatients’ department of a large public hospital in a developing country where appointment systems do not exist. The main aim of the current study is to demonstrate the usefulness of DEA modelling in the evaluation of a queue system. The patient flow pathway considered for this study consists of two stages; consultation with a doctor and pharmacy. The DEA results indicated that waiting times and other related queuing variables included need considerable minimisation at both stages.

Numerical modelling of multimode fibre-optic communication lines

The results of numerical modelling of nonlinear propagation of an optical signal in multimode fibres with a small differential group delay are presented. It is found that the dependence of the error vector magnitude (EVM) on the differential group delay can be reduced by increasing the number of ADC samples per symbol in the numerical implementation of the differential group delay compensation algorithm in the receiver. The possibility of using multimode fibres with a small differential group delay for data transmission in modern digital communication systems is demonstrated. It is shown that with increasing number of modes the strong coupling regime provides a lower EVM level than the weak coupling one.

Modelling markets

Decision makers in marketing are often faced with rather complicated situations in which decisions have to be made. Let us consider the problem of determining the appropriate advertising budget. A brand manager is asked to determine the optimal budget. He knows that increases in advertising may lead to increased sales, but also lead to increased costs. The advertising expenditures in period t, say 1994, may not only lead to increases in sales in t, but also to increases in t + 1 (1995) and possibly may contribute to the value of the brand for a long time period.2 Increases in sales will result in changes in profit. The decision maker is allowed to spend more advertising money if there is more profit and more sales, thus advertising spending depends on past sales and profit performance. In order to account for these and possibly other relationships it is necessary to formalise these relations. This means that the decision maker has to specify which variables influence which other variables and what the directions of causality between these variables are. To this end a model has to be formalised, data have to be collected and the formalised model has to be calibrated.

A virtual species set for robust and reproducible species distribution modelling tests

Predicting species potential and future distribution has become a relevant tool in biodiversity monitoring and conservation.In this data article we present the suitability map of a virtual species generated based on two bioclimatic variables, and a dataset containing more than 700,000 random observations at the extent of Europe. The dataset includes spatial attributes such as: distance to roads, protected areas, country codes, and the habitat suitability of two spatially clustered species (grassland and forest species) and a wide-spread species.

Modelling and linking transformations in EPCIS governing supply chain business processes

Supply chains comprise of complex processes spanning across multiple trading partners. The various operations involved generate large number of events that need to be integrated in order to enable internal and external traceability. Further, provenance of artifacts and agents involved in the supply chain operations is now a key traceability requirement. In this paper we propose a Semantic web/Linked data powered framework for the event based representation and analysis of supply chain activities governed by the EPCIS specification. We specifically show how a new EPCIS event type called "Transformation Event" can be semantically annotated using EEM - The EPCIS Event Model to generate linked data, that can be exploited for internal event based traceability in supply chains involving transformation of products. For integrating provenance with traceability, we propose a mapping from EEM to PROV-O. We exemplify our approach on an abstraction of the production processes that are part of the wine supply chain.

Sensing information modelling for smart city

Sensing technology is a key enabler of the Internet of Things (IoT) and could produce huge volume data to contribute the Big Data paradigm. Modelling of sensing information is an important and challenging topic, which influences essentially the quality of smart city systems. In this paper, the author discusses the relevant technologies and information modelling in the context of smart city and especially reports the investigation of how to model sensing and location information in order to support smart city development.

Better queue management in a busy public hospital of a developing country without appointment system::an application using data envelopment analysis

Queuing is a key efficiency criterion in any service industry, including Healthcare. Almost all queue management studies are dedicated to improving an existing Appointment System. In developing countries such as Pakistan, there are no Appointment Systems for outpatients, resulting in excessive wait times. Additionally, excessive overloading, limited resources and cumbersome procedures lead to over-whelming queues. Despite numerous Healthcare applications, Data Envelopment Analysis (DEA) has not been applied for queue assessment. The current study aims to extend DEA modelling and demonstrate its usefulness by evaluating the queue system of a busy public hospital in a developing country, Pakistan, where all outpatients are walk-in; along with construction of a dynamic framework dedicated towards the implementation of the model. The inadequate allocation of doctors/personnel was observed as the most critical issue for long queues. Hence, the Queuing-DEA model has been developed such that it determines the ‘required’ number of doctors/personnel. The results indicated that given extensive wait times or length of queue, or both, led to high target values for doctors/personnel. Hence, this crucial information allows the administrators to ensure optimal staff utilization and controlling the queue pre-emptively, minimizing wait times. The dynamic framework constructed, specifically targets practical implementation of the Queuing-DEA model in resource-poor public hospitals of developing countries such as Pakistan; to continuously monitor rapidly changing queue situation and display latest required personnel. Consequently, the wait times of subsequent patients can be minimized, along with dynamic staff scheduling in the absence of appointments. This dynamic framework has been designed in Excel, requiring minimal training and work for users and automatic update features, with complex technical aspects running in the background. The proposed model and the dynamic framework has the potential to be applied in similar public hospitals, even in other developing countries, where appointment systems for outpatients are non-existent.