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Empirical research involving projects is an important and common way to advance knowledge in the energy sector, and there are well-established approaches for qualitative analysis of single or few cases (1–10 cases) as well as quantitative analysis of large databases (from 50+ cases). However, the “middle-ground” of analysing 10–50 cases is an unknown territory, and very few approaches exist to deal with numbers of cases that lie in the range of 10–50. This paper shows how this “middle-ground” can be explored through Qualitative Comparative Analysis (QCA). This is a method that can be applied to energy infrastructure projects (such as construction, operations, and decommissioning of power plants) in order to study causal inference (e.g. factors associated with outcomes). This paper demonstrates the potential of QCA by showing its application on an energy infrastructure phenomenon with an intermediate number of cases, that of nuclear decommissioning projects. These projects are becoming increasingly important to society and have multibillion US dollar budgets. Moreover, their characteristics need to urgently be matched with their project performance in order to avoid even further cost overruns. The application of QCA to 24 European nuclear decommissioning projects shows that a combination of characteristics (such as a streamlined governance structure and the presence of a storage facility for radioactive material on site) might be contributing to lower cost overruns. This paper concludes by showing how QCA can be applied to other energy infrastructure phenomena with a similar intermediate number of cases.

Agile software development methods are mostly built as a set of managerial guidelines and development concepts on how to handle a software development but are not bounded to software development paradigms like object or agent orientation. Some methods, like eXtreme Programming and SCRUM are driven by operational requirements representation models called User Stories. These User Stories can be used as an anchoring point to agile methods; this means that we could take a User Stories set to drive a software transformation approach embedded in a particular development paradigm. This paper presents a process fragment for Multi-Agent Systems development with agile methods based on User Stories sets. The process fragment indeed takes advantage of an initial set of User Stories to build a reasoning model (called the Rationale Tree; typically several of these are built for a single project) that documents decompositions and means-end alternatives in scenarios for requirements realization. A Rationale Tree can then be aligned with a Multi-Agent design and implemented in an agent-oriented development language. In this paper the transformation is targeted to the JAVA Agent DEvelopment (JADE) framework. The process fragment (at least partially) covers the Requirements Analysis, Multi-Agent System Design and Multi-Agent System Implementation phases. Transformation from one phase to the other is overseen and illustrated on an example.

The scientific literature lacks comprehensive and updated information regarding the airtightness of building components, this hindering the development and improvement of models for a robust prediction of their performance. To fill this gap, this paper presents a meta-analysis conducted on literature studies reporting laboratory and in-situ measurements of airtightness for various building components. Meta-analysis is a powerful methodological tool to compare and combine data from previous research, even when they have inherent differences. The results of the meta-analysis allowed to attain three outcomes: a classification of building components related to their airtightness performance; the update and improvement of existing databases of building component airtightness; and, the identification of factors specifically influencing airtightness performances of windows. In addition, the critical appraisal of the findings led to the definition of the requirements for future studies reporting measurements of airtightness of building components. This study offers a step ahead from existing knowledge by improving existing databases and by proposing a methodological framework of analysis that can be extended to several other domains of the built environment.

Batteries have rapidly evolved and are widely applied in both stationary and transport applications. The safe and reliable operation is of vital importance to all types of batteries, herein an effective battery sensing system with high performance and easy implementation is critically needed. This also requires the sensing system to monitor the states of batteries in real time. Among the available methods, optical fibre sensors have shown a significant advantage due to their advanced capabilities of which include the fast measurement of multiple parameters with high sensitivity, working without interfering the battery performance, being able to be composited in multiplexed configurations and being robust to various harsh environment conditions. This paper mainly discusses the current optical fibre sensing methods for batteries in terms of the working principles and critical reviews the sensing performance corresponding to different sensing parameters. Moreover, the challenges and outlooks for future research on battery sensing are derived.

In this paper we present a spherical soft robotic arm made from fabric. The inflatable arm has a small mass and is pneumatically actuated. A configuration is employed with only three actuators controlling the two rotational degrees of freedom of a spherical joint. This differs from the commonly employed antagonistic pairs, including four actuators for two degrees of freedom. The fabrication procedure of the lightweight and compliant system is discussed in detail and uses commonly available materials and tools. The capability of the robotic arm to adjust the joint stiffness as a function of the actuator pressures is investigated and characterized for different deflection directions. The static mapping from the actuator pressures to the orientation of the robotic arm is identified from data and the inverse mapping is employed in a position controller. The modeling and controller derivation are performed for three different stiffness levels demonstrating the ability of the spherical robotic arm to change the joint stiffness independently of controlling the position. The position tracking performance is experimentally evaluated by tracking a square trajectory. A comparison of the tracking performance for the different stiffness levels shows that accurate tracking is more challenging for the smallest joint stiffness. A gray-box model capturing the interactions of the two degrees of freedom is used in a learning scheme that is applied for the smallest stiffness level. The learning approach reduces interactions between the two degrees of freedom and demonstrates the control performance achievable with the system developed., Mechatronics, 68, ISSN:0957-4158, ISSN:1873-4006

Hardware trends over the last decade show increasing complexity and heterogeneity in high performance computing architectures, which presents developers of CFD applications with three key challenges; the need for achieving good performance, being able to utilise current and future hardware by being portable, and doing so in a productive manner. These three appear to contradict each other when using traditional programming approaches, but in recent years, several strategies such as template libraries and Domain Specific Languages have emerged as a potential solution; by giving up generality and focusing on a narrower domain of problems, all three can be achieved. This paper gives an overview of the state-of-the-art for delivering performance, portability, and productivity to CFD applications, ranging from high-level libraries that allow the symbolic description of PDEs to low-level techniques that target individual algorithmic patterns. We discuss advantages and challenges in using each approach, and review the performance benchmarking literature that compares implementations for hardware architectures and their programming methods, giving an overview of key applications and their comparative performance.

Hydraulic hybrid technologies improve the fuel economy of city-use medium duty vehicles by recovering braking energy and reusing it for driving, most notably vehicle launching. In this paper, a parametric design of hydraulic driving system for a medium duty truck is conducted based on its power demand in typical urban driving cycles. The braking control strategy is designed considering both the safety regulations and braking energy recovery rate. The proportional relationship of hydraulic pump/motor output torque and its working pressure is considered when designing the braking control strategy. The influence of the high pressure accumulator minimum working pressure and the corresponding gas volume at the minimum pressure on the braking energy recovery rate is analyzed. The simulation results show that the proposed hydraulic driving system and braking control strategy helps greatly increase the braking energy recovery rate. Besides, different vehicle mass and load distributions are investigated to analyze the regenerative braking effect on various loading conditions.

Electrochemical micromachining (micro-ECM) is an unconventional micromachining technology that has capability to fabricate high aspect ratio micro-holes, micro-cavities, micro-channels and grooves on conductive and difficult-to-cut materials. Both academia and industry have the consensus that it offers promising machining performance especially in terms of high surface finish, no tool wear and absence of thermally induced defects. Furthermore in order to machine novel materials with extreme properties, novel hybrid electrochemical micromachining technologies are under development. With these hybrid micro-ECM technologies, capabilities of micro-ECM can be expanded by combining it with other processes. To fully exploit the potential as well as improve micro-ECM technology and related hybrid processes, a wide spectrum of multidisciplinary knowledge is needed. The present review systematically discusses process capabilities and research developments of electrochemical micromachining and its hybrid variants considering knowledge of both basic and applied research fields. After few introductory review articles in prior state of the art, this review fills an important gap in research literature by presenting first time an extended literature source with a wide coverage of recent research developments in electrochemical micromachining technology and its hybrid variants. This paper outlines the research and engineering developments in electrochemical micromachining technology and its hybrid variants, review of the related concepts, aspects of tooling, advanced process capabilities and process energy sources. It also provides new sights into technological understanding of micro-ECM technology which will be helpful in future engineering developments of this technology. ispartof: International Journal of Machine Tools & Manufacture vol:127 pages:28-56 status: published

Most of the caregivers working in hospitals are highly skilled and educated work force. Even if they are supported by administrative staff, part of their time still consists of administrative procedures or trying to empirically fill or retrieve agendas on the basis of real-time constraints. Similarly, patients waste time waiting for adequate treatments so they occupy slots (beds) in hospitals while their hospitalization is not a necessity. This leads to huge wastes of resources for the hospital that has to hospitalize and prioritize patients in more effective need of care and monitoring. In order to ensure real-time patient support during its entire stay in the hospital (in terms of bed occupancy, appointments with doctors and/or for medico-technical exams) as well as to furnish a central solution open for future IT integration we propose, in this paper, to build a Multi-Agent System (MAS) based software solution. The latter has been built following a socio-technical approach; it maps, at runtime, the working processes of a Belgian hospital. Processes have been modeled in a patient-centric way in order to identify most relevant stages and bottlenecks in bed, cares and medico-technical exams management; real-time data transmission allows to update and re-optimize slots statuses as well as schedules. Also, data analysis (i.e., business intelligence) functions are supported by the MAS implementation for decision making/taking. Further than the proof of concept that shows the managerial support of the software solution, the MAS modules’ benefits to the overall hospital strategy is studied. The latter shows the relevance of the solution to the hospital’s governance. ispartof: Telematics and Informatics vol:35 issue:4 pages:854-882 status: published

Data analytics have become increasingly complicated as the amount of data has increased. One technique that is used to enable data analytics in large datasets is data sampling, in which a portion of the data is selected to preserve the data characteristics for use in data analytics. In this paper, we introduce a novel data sampling technique that is rooted in formal concept analysis theory. This technique is used to create samples reliant on the data distribution across a set of binary patterns. The proposed sampling technique is applied in classifying the regions of breast cancer histology images as malignant or benign. The performance of our method is compared to other classical sampling methods. The results indicate that our method is efficient and generates an illustrative sample of small size. It is also competing with other sampling methods in terms of sample size and sample quality represented in classification accuracy and F1 measure. 1 2017 Elsevier Ltd This contribution was made possible by NPRP grant #07- 794-1-145 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. Scopus

This paper proposes an arbitrary view gait recognition method where the gait recognition is performed in 3-dimensional (3D) to be robust to variation in speed, inclined plane and clothing, and in the presence of a carried item. 3D parametric gait models in a gait period are reconstructed by an optimized 3D human pose, shape and simulated clothes estimation method using multi-view gait silhouettes. The gait estimation involves morphing a new subject with constant semantic constraints using silhouette cost function as observations. Using a clothes-independent 3D parametric gait model reconstruction method, gait models of different subjects with various postures in a cycle are obtained and used as galleries to construct 3D gait dictionary. Using a carrying-items posture synthesized model, virtual gait models with different carrying-items postures are synthesized to further construct an over-complete 3D gait dictionary. A self-occlusion optimized simultaneous sparse representation model is also introduced to achieve high robustness in limited gait frames. Experimental analyses on CASIA B dataset and CMU MoBo dataset show a significant performance gain in terms of accuracy and robustness. Introduces a clothes-independent 3D human pose and shape estimation method.Introduces a carried-item posture synthesized model for 3D gait recognition.Discusses a self-occlusion optimized gait simultaneous sparse representation model.

Proliferative diabetic retinopathy (PDR) is a condition that carries a high risk of severe visual impairment. The hallmark of PDR is the growth of abnormal new vessels. In this paper, an automated method for the detection of new vessels from retinal images is presented. This method is based on a dual classification approach. Two vessel segmentation approaches are applied to create two separate binary vessel map which each hold vital information. Local morphology features are measured from each binary vessel map to produce two separate 4-D feature vectors. Independent classification is performed for each feature vector using a support vector machine (SVM) classifier. The system then combines these individual outcomes to produce a final decision. This is followed by the creation of additional features to generate 21-D feature vectors, which feed into a genetic algorithm based feature selection approach with the objective of finding feature subsets that improve the performance of the classification. Sensitivity and specificity results using a dataset of 60 images are 0.9138 and 0.9600, respectively, on a per patch basis and 1.000 and 0.975, respectively, on a per image basis.