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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.

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.