Object tracking using multiple motion modalities

This paper presents an object tracking system that utilises a hybrid multi-layer motion segmentation and optical flow algorithm. While many tracking systems seek to combine multiple modalities such as motion and depth or multiple inputs within a fusion system to improve tracking robustness, current systems have avoided the combination of motion and optical flow. This combination allows the use of multiple modes within the object detection stage. Consequently, different categories of objects, within motion or stationary, can be effectively detected utilising either optical flow, static foreground or active foreground information. The proposed system is evaluated using the ETISEO database and evaluation metrics and compared to a baseline system utilising a single mode foreground segmentation technique. Results demonstrate a significant improvement in tracking results can be made through the incorporation of the additional motion information.

Changes in environmentally sensitive productivity and technological modernization in China's iron and steel industry in the 1990s

Technological modernization is widely believed to contribute positively both to economic development and to environmental and resource conservation, through improvements in productivity and strengthening of business competitiveness. However, this may not always be true, particularly in the short term, as it requires substantial investments and may impose financial burdens on firms undertaking such investments. This study empirically examines the effects of technological modernization in China's iron and steel industry in the 1990s on conventional economic productivity (CEP) and environmentally sensitive productivities (ESPs). We employ a directional distance function that can handle multiple inputs and outputs to compute relative production efficiencies. We apply these models to the data covering 27 iron and steel firms in China between 1990 and 1999-a period when the Chinese iron and steel industry modernized rapidly. We find that ESPs have continuously improved, even in the period when the CEP declined.

Development of an education program to improve care of patients with dementia in an acute care setting

As the population ages, the number of people with dementia in acute care environments is projected to increase rapidly. However, many acute care nurses have undertaken little or no dementia training, potentially leading to reduced quality of care for these patients. This article details the development and delivery of a tailored education program to improve the quality of care of people with dementia in a large, urban hospital in Australia. Designed specifically for the existing context, environment and knowledge levels, the program was developed from multiple inputs, including: expert opinion, literature on workplace and dementia care training, and feedback from participants. The program was delivered to acute care nurses and allied health staff within an outcome based, microteaching model. The broader applicability of the development and delivery techniques used in this program is also discussed.

Regulation of Osteoblast Differentiation and Gene Expression by Phosphodiesterases and Bioactive Peptides

Bone is a mineralized tissue that enables multiple mechanical and metabolic functions to be carried out in the skeleton. Bone contains distinct cell types: osteoblasts (bone-forming cells), osteocytes (mature osteoblast that embedded in mineralized bone matrix) and the osteoclasts (bone-resorbing cells). Remodelling of bone begins early in foetal life, and once the skeleton is fully formed in young adults, almost all of the metabolic activity is in this form. Bone is constantly destroyed or resorbed by osteoclasts and then replaced by osteoblasts. Many bone diseases, i.e. osteoporosis, also known as bone loss, typically reflect an imbalance in skeletal turnover. The cyclic adenosine monophosphate (cAMP) and the cyclic guanosine monophosphate (cGMP) are second messengers involved in a variety of cellular responses to such extracellular agents as hormones and neurotransmitters. In the hormonal regulation of bone metabolism, i.e. via parathyroid hormone (PTH), parathyroid hormone-related peptide (PTHrp) and prostaglandin E2 signal via cAMP. cAMP and cGMP are formed by adenylate and guanylate cyclases and are degraded by phosphodiesterases (PDEs). PDEs determine the amplitudes of cyclic nucleotide-mediated hormonal responses and modulate the duration of the signal. The activities of the PDEs are regulated by multiple inputs from other signalling systems and are crucial points of cross-talk between the pathways. Food-derived bioactive peptides are reported to express a variety of functions in vivo. The angiotensin-converting enzymes (ACEs) are involved in the regulation of the specific maturation or degradation of a number of mammalian bioactive peptides. The bioactive peptides offer also a nutriceutical and a nutrigenomic aspect to bone cell biology. The aim of this study was to investigate the influence of PDEs and bioactive peptides on the activation and the differentiation of human osteoblast cells. The profile of PDEs in human osteoblast-like cells and the effect of glucocorticoids on the function of cAMP PDEs, were investigated at the mRNA and enzyme levels. The effects of PDEs on bone formation and osteoblast gene expression were determined with chemical inhibitors and siRNAs (short interfering RNAs). The influence of bioactive peptides on osteoblast gene expression and proliferation was studied at the mRNA and cellular levels. This work provides information on how PDEs are involved in the function and the differentiation of osteoblasts. The findings illustrate that gene-specific silencing with an RNA interference (RNAi) method is useful in inhibiting, the gene expression of specific PDEs and further, PDE7 inhibition upregulates several osteogenic genes and increases bALP activity and mineralization in human mesenchymal stem cells-derived osteoblasts. PDEs appear to be involved in a mechanism by which glucocorticoids affect cAMP signaling. This may provide a potential route in the formation of glucocorticoid-induced bone loss, involving the down-regulation of cAMP-PDE. PDEs may play an important role in the regulation of osteoblastic differentiation. Isoleucine-proline-proline (IPP), a bioactive peptide, possesses the potential to increase osteoblast proliferation, differentiation and signalling.

NON-DIMENSIONAL KINETOELASTOSTATIC MAPS FOR COMPLIANT MECHANISMS

How do we assess the capability of a compliant mechanism of given topology and shape? The kinetoelastostatic maps proposed in this paper help answer this question. These maps are drawn in 2D using two non-dimensional quantities, one capturing the nonlinear static response and the other the geometry, material, and applied forces. Geometrically nonlinear finite element analysis is used to create the maps for compliant mechanisms consisting of slender beams. In addition to the topology and shape, the overall proportions and the proportions of the cross-sections of the beam segments are kept fixed for a map. The finite region of the map is parameterized using a non-dimensional quantity defined as the slenderness ratio. The shape and size of the map and the parameterized curves inside it indicate the complete kinetoelastostatic capability of the corresponding compliant mechanism of given topology, shape, and fixed proportions. Static responses considered in this paper include input/output displacement, geometric amplification, mechanical advantage, maximum stress, etc. The maps can be used to compare mechanisms, to choose a suitable mechanism for an application, or re-design as may be needed. The usefulness of the non-dimensional maps is presented with multiple applications of different variety. Non-dimensional portrayal of snap-through mechanisms is one such example. The effect of the shape of the cross-section of the beam segments and the role of different segments in the mechanism as well as extension to 3D compliant mechanisms, the cases of multiple inputs and outputs, and moment loads are also explained. The effects of disproportionate changes on the maps are also analyzed.

Identification et étude de mécanismes régulant l’expression de MAPK

Les fichiers accompagnant le document sont en format Microsoft Excel 2010.

LMI-based digital redesign of linear time-invariant systems with state-derivative feedback

A simple method for designing a digital state-derivative feedback gain and a feedforward gain such that the control law is equivalent to a known and adequate state feedback and feedforward control law of a digital redesigned system is presented. It is assumed that the plant is a linear controllable, time-invariant, Single-Input (SI) or Multiple-Input (MI) system. This procedure allows the use of well-known continuous-time state feedback design methods to directly design discrete-time state-derivative feedback control systems. The state-derivative feedback can be useful, for instance, in the vibration control of mechanical systems, where the main sensors are accelerometers. One example considering the digital redesign with state-derivative feedback of a helicopter illustrates the proposed method. © 2009 IEEE.

Estudo e implementação de sinais de excitação aplicados em identificação de sistemas multivariáveis.

Devido à crescente implementação do Controle Preditivo baseado em Modelo (MPC) em outros processos além de refino e plantas petroquímicas, que geralmente possuem múltiplas entradas e saídas, tem-se um aumento na demanda de modelos gerados por identificação de sistemas. Identificar modelos que representem fielmente a dinâmica do processo depende em grande medida das características dos sinais de excitação dos processos. Assim, o foco deste trabalho é realizar um estudo dos sinais típicos usados em identificação de sistemas, PRBS e GBN, em uma abordagem multivariável. O estudo feito neste trabalho parte das características da geração dos sinais individualmente, depois é feita uma análise de correlação cruzada dos sinais de entrada, observando a influência desta sobre os resultados de identificação. Evitar uma alta correlação entre os sinais de entrada permite determinar o efeito de cada entrada sobre a saída no processo de identificação. Um ponto importante no projeto de sinais de identificação de sistemas multivariáveis é a frequência dos mesmos para conseguir excitar os processos nas regiões de frequência de operação normal e assim extrair a maior informação dinâmica possível do processo. As características estudadas são avaliadas por meio de testes em três plantas simuladas diferentes, categorizadas como mal, medianamente e bem condicionadas. Estas implementações foram feitas usando sinais GBN e PRBS de diferentes frequências. Expressões para a caracterização dos sinais de excitação foram avaliadas identificando os processos em malha aberta e malha fechada. Para as plantas mal condicionadas foram implementados sinais compostos por uma parte completamente correlacionada e uma parte não-correlacionada, conhecido como método de dois passos. Finalmente são realizados experimentos de identificação em uma aplicação em tempo real de uma planta piloto de neutralização de pH. Os testes realizados na planta foram feitos visando avaliar os estudos de frequência e correlação em uma aplicaficção real. Os resultados mostram que a condição de sinais completamente descorrelacionados n~ao deve ser cumprida para ter bons resultados nos modelos identificados. Isto permite ter mais exibilidade na geração do conjunto de sinais de excitação.

Modelling knowledge production performance of research centres with a focus on triple bottom line benchmarking

We demonstrate a portable process for developing a triple bottom line model to measure the knowledge production performance of individual research centres. For the first time, this study also empirically illustrates how a fully units-invariant model of Data Envelopment Analysis (DEA) can be used to measure the relative efficiency of research centres by capturing the interaction amongst a common set of multiple inputs and outputs. This study is particularly timely given the increasing transparency required by governments and industries that fund research activities. The process highlights the links between organisational objectives, desired outcomes and outputs while the emerging performance model represents an executive managerial view. This study brings consistency to current measures that often rely on ratios and univariate analyses that are not otherwise conducive to relative performance analysis.

Capital market equilibrium with moral hazard and flexible technology

(Magill, M., Quinzii, M., 2002. Capital market equilibrium with moral hazard. Journal of Mathematical Economics 38, 149-190) showed that, in a stockmarket economy with private information, the moral hazard problem may be resolved provided that a spanning overlap condition is satisfed. This result depends on the assumption that the technology is given by a stochastic production function with a single scalar input. The object of the present paper is to extend the analysis of Magill and Quinzii to the case of multiple inputs. We show that their main result extends to this general case if and only if, for each firm, the number of linearly independent combinations of securities having payoffs correlated with, but not dependent on, the firms output is equal to the number of degrees of freedom in the firm's production technology.

General and multiplicative non-parametric corporate performance models with interval ratio data

The increasing intensity of global competition has led organizations to utilize various types of performance measurement tools for improving the quality of their products and services. Data envelopment analysis (DEA) is a methodology for evaluating and measuring the relative efficiencies of a set of decision making units (DMUs) that use multiple inputs to produce multiple outputs. All the data in the conventional DEA with input and/or output ratios assumes the form of crisp numbers. However, the observed values of data in real-world problems are sometimes expressed as interval ratios. In this paper, we propose two new models: general and multiplicative non-parametric ratio models for DEA problems with interval data. The contributions of this paper are fourfold: (1) we consider input and output data expressed as interval ratios in DEA; (2) we address the gap in DEA literature for problems not suitable or difficult to model with crisp values; (3) we propose two new DEA models for evaluating the relative efficiencies of DMUs with interval ratios, and (4) we present a case study involving 20 banks with three interval ratios to demonstrate the applicability and efficacy of the proposed models where the traditional indicators are mostly financial ratios. © 2011 Elsevier Inc.

A taxonomy and review of the fuzzy data envelopment analysis literature:two decades in the making

Data envelopment analysis (DEA) is a methodology for measuring the relative efficiencies of a set of decision making units (DMUs) that use multiple inputs to produce multiple outputs. Crisp input and output data are fundamentally indispensable in conventional DEA. However, the observed values of the input and output data in real-world problems are sometimes imprecise or vague. Many researchers have proposed various fuzzy methods for dealing with the imprecise and ambiguous data in DEA. In this study, we provide a taxonomy and review of the fuzzy DEA methods. We present a classification scheme with four primary categories, namely, the tolerance approach, the a-level based approach, the fuzzy ranking approach and the possibility approach. We discuss each classification scheme and group the fuzzy DEA papers published in the literature over the past 20 years. To the best of our knowledge, this paper appears to be the only review and complete source of references on fuzzy DEA. © 2011 Elsevier B.V. All rights reserved.

Flexible measures in production process:a DEA-based approach

Data envelopment analysis (DEA) has been proven as an excellent data-oriented efficiency analysis method for comparing decision making units (DMUs) with multiple inputs and multiple outputs. In conventional DEA, it is assumed that the status of each measure is clearly known as either input or output. However, in some situations, a performance measure can play input role for some DMUs and output role for others. Cook and Zhu [Eur. J. Oper. Res. 180 (2007) 692–699] referred to these variables as flexible measures. The paper proposes an alternative model in which each flexible measure is treated as either input or output variable to maximize the technical efficiency of the DMU under evaluation. The main focus of this paper is on the impact that the flexible measures has on the definition of the PPS and the assessment of technical efficiency. An example in UK higher education intuitions shows applicability of the proposed approach.

The state of the art in fuzzy data envelopment analysis

Data envelopment analysis (DEA) is a methodology for measuring the relative efficiencies of a set of decision making units (DMUs) that use multiple inputs to produce multiple outputs. Crisp input and output data are fundamentally indispensable in conventional DEA. However, the observed values of the input and output data in real-world problems are sometimes imprecise or vague. Many researchers have proposed various fuzzy methods for dealing with the imprecise and ambiguous data in DEA. This chapter provides a taxonomy and review of the fuzzy DEA (FDEA) methods. We present a classification scheme with six categories, namely, the tolerance approach, the α-level based approach, the fuzzy ranking approach, the possibility approach, the fuzzy arithmetic, and the fuzzy random/type-2 fuzzy set. We discuss each classification scheme and group the FDEA papers published in the literature over the past 30 years. © 2014 Springer-Verlag Berlin Heidelberg.