Toward a document evaluation methodology: What does research tell us about the validity and reliability of evaluation methods?

Although the usefulness of evaluating documents has become generally accepted among communication professionals, the supporting research that puts evaluation practices empirically to the test is only beginning to emerge. This article presents an overview of the available research on troubleshooting evaluation methods. Four lines of research are distinguished concerning the validity of evaluation methods, sample composition, sample size, and the implementation of evaluation results during revisio

Enhancing Energy Production with Exascale HPC Methods

High Performance Computing (HPC) resources have become the key actor for achieving more ambitious challenges in many disciplines. In this step beyond, an explosion on the available parallelism and the use of special purpose processors are crucial. With such a goal, the HPC4E project applies new exascale HPC techniques to energy industry simulations, customizing them if necessary, and going beyond the state-of-the-art in the required HPC exascale simulations for different energy sources. In this paper, a general overview of these methods is presented as well as some specific preliminary results.The research leading to these results has received funding from the European Union's Horizon 2020 Programme (2014-2020) under the HPC4E Project (www.hpc4e.eu), grant agreement n° 689772, the Spanish Ministry of Economy and Competitiveness under the CODEC2 project (TIN2015-63562-R), and from the Brazilian Ministry of Science, Technology and Innovation through Rede Nacional de Pesquisa (RNP). Computer time on Endeavour cluster is provided by the Intel Corporation, which enabled us to obtain the presented experimental results in uncertainty quantification in seismic imagingPostprint (author's final draft

Enabling quantitative data analysis through e-infrastructures

This paper discusses how quantitative data analysis in the social sciences can engage with and exploit an e-Infrastructure. We highlight how a number of activities which are central to quantitative data analysis, referred to as ‘data management’, can benefit from e-infrastructure support. We conclude by discussing how these issues are relevant to the DAMES (Data Management through e-Social Science) research Node, an ongoing project that aims to develop e-Infrastructural resources for quantitative data analysis in the social sciences

Fast human detection for video event recognition

Human body detection, which has become a research hotspot during the last two years, can be used in many video content analysis applications. This paper investigates a fast human detection method for volume based video event detection. Compared with other object detection systems, human body detection brings more challenge due to threshold problems coming from a wide range of dynamic properties. Motivated by approaches successfully introduced in facial recognition applications, it adapts and adopts feature extraction and machine learning mechanism to classify certain areas from video frames. This method starts from the extraction of Haar-like features from large numbers of sample images for well-regulated feature distribution and is followed by AdaBoost learning and detection algorithm for pattern classification. Experiment on the classifier proves the Haar-like feature based machine learning mechanism can provide a fast and steady result for human body detection and can be further applied to reduce negative aspects in human modelling and analysis for volume based event detection

Multimodal Observation and Interpretation of Subjects Engaged in Problem Solving

In this paper we present the first results of a pilot experiment in the capture and interpretation of multimodal signals of human experts engaged in solving challenging chess problems. Our goal is to investigate the extent to which observations of eye-gaze, posture, emotion and other physiological signals can be used to model the cognitive state of subjects, and to explore the integration of multiple sensor modalities to improve the reliability of detection of human displays of awareness and emotion. We observed chess players engaged in problems of increasing difficulty while recording their behavior. Such recordings can be used to estimate a participant's awareness of the current situation and to predict ability to respond effectively to challenging situations. Results show that a multimodal approach is more accurate than a unimodal one. By combining body posture, visual attention and emotion, the multimodal approach can reach up to 93% of accuracy when determining player's chess expertise while unimodal approach reaches 86%. Finally this experiment validates the use of our equipment as a general and reproducible tool for the study of participants engaged in screen-based interaction and/or problem solving

1st EFORT European Consensus: Medical & Scientific Research Requirements for the Clinical Introduction of Artificial Joint Arthroplasty Devices

Innovations in Orthopaedics and Traumatology have contributed to the achievement of a high-quality level of care in musculoskeletal disorders and injuries over the past decades. The applications of new implants as well as diagnostic and therapeutic techniques in addition to implementation of clinical research, have significantly improved patient outcomes, reduced complication rates and length of hospital stay in many areas. However, the regulatory framework is extensive, and there is a lack of understanding and clarity in daily practice what the meaning of clinical & pre‐clinical evidence as required by the MDR is. Thus, understanding and clarity are of utmost importance for introduction of new implants and implant-related instrumentation in combination with surgical technique to ensure a safe use of implants and treatment of patients. Therefore EFORT launched IPSI, The Implant and Patient Safety Initiative, which starting from an inaugural workshop in 2021 issued a set of recommendations, notably through a subsequent Delphi Process involving the National Member Societies of EFORT, European Specialty Societies as well as International Experts. These recommendations provide surgeons, researchers, implant manufacturers as well as patients and health authorities with a consensus of the development, implementation, and dissemination of innovation in the field of arthroplasty. The intended key outcomes of this 1st EFORT European Consensus on “Medical & Scientific Research Requirements for the Clinical Introduction of Artificial Joint Arthroplasty Devices”are consented, practical pathways to maintain innovation and optimisation of orthopaedic products and workflows within the boundaries of MDR 2017/745. Open Access practical guidelines based on adequate, state of the art pre-clinical and clinical evaluation methodologies for the introduction of joint replacements and implant-related instrumentation shall provide hands-on orientation for orthopaedic surgeons, research institutes and laboratories, orthopaedic device manufacturers, Notified Bodies but also for National Institutes and authorities, patient representatives and further stakeholders. We would like to acknowledge and thank the Scientific Committee members, all International Expert Delegates, the Delegates from European National & Specialty Societies and the Editorial Team for their outstanding contributions and support during this EFORT European Consensus

Establishment of a integrative multi-omics expression database CKDdb in the context of chronic kidney disease (CKD)

Complex human traits such as chronic kidney disease (CKD) are a major health and financial burden in modern societies. Currently, the description of the CKD onset and progression at the molecular level is still not fully understood. Meanwhile, the prolific use of high-throughput omic technologies in disease biomarker discovery studies yielded a vast amount of disjointed data that cannot be easily collated. Therefore, we aimed to develop a molecule-centric database featuring CKD-related experiments from available literature publications. We established the Chronic Kidney Disease database CKDdb, an integrated and clustered information resource that covers multi-omic studies (microRNAs, genomics, peptidomics, proteomics and metabolomics) of CKD and related disorders by performing literature data mining and manual curation. The CKDdb database contains differential expression data from 49395 molecule entries (redundant), of which 16885 are unique molecules (non-redundant) from 377 manually curated studies of 230 publications. This database was intentionally built to allow disease pathway analysis through a systems approach in order to yield biological meaning by integrating all existing information and therefore has the potential to unravel and gain an in-depth understanding of the key molecular events that modulate CKD pathogenesis

Performance Measurement Information Systems: Do they Convey (Sustainable) Competitive Advantage?

Performance management information systems (PMIS) have been a ‘hot topic’ for Chief Information Officers (CIOs) and Chief Financial Officers (CFOs) for close to a decade. PMIS range from low-functionality spreadsheet-based solutions through to high-functionality business intelligence solutions. As yet, this area has not yet received sufficient academic enquiry. Our research questions concern: what are PMIS functionalities, and whether and how do they contribute to competitive advantage? We conceptualize functionality as reflected by system usability and data multi-dimensionality. We examine functionalities of the two types of PMIS: performance planning systems (for budgeting and forecasting) and performance reporting systems (for reporting results information to management). We apply resource-based theory. We hypothesize mediation chains, in which the two PMIS functionality constructs link to competitive advantage, mediated by performance management capabilities and mediated by a resource-base of organizational culture. We use partial least squares path modelling using survey data collected from senior managers of 264 Australian firms. We find support for the hypotheses. We also unexpectedly find that the two types of PMIS functionality operate in sequential, rather than parallel, mediation. The findings have implications for CIOs, CFOs and other managers responsible for development of PMIS

Grid Global Behavior Prediction

Complexity has always been one of the most important issues in distributed computing. From the first clusters to grid and now cloud computing, dealing correctly and efficiently with system complexity is the key to taking technology a step further. In this sense, global behavior modeling is an innovative methodology aimed at understanding the grid behavior. The main objective of this methodology is to synthesize the grid's vast, heterogeneous nature into a simple but powerful behavior model, represented in the form of a single, abstract entity, with a global state. Global behavior modeling has proved to be very useful in effectively managing grid complexity but, in many cases, deeper knowledge is needed. It generates a descriptive model that could be greatly improved if extended not only to explain behavior, but also to predict it. In this paper we present a prediction methodology whose objective is to define the techniques needed to create global behavior prediction models for grid systems. This global behavior prediction can benefit grid management, specially in areas such as fault tolerance or job scheduling. The paper presents experimental results obtained in real scenarios in order to validate this approach