From projects into operations: lessons for data handover

Data from civil engineering projects can inform the operation of built infrastructure. This paper captures lessons for such data handover, from projects into operations, through interviews with leading clients and their supply chain. Clients are found to value receiving accurate and complete data. They recognise opportunities to use high quality information in decision-making about capital and operational expenditure; as well as in ensuring compliance with regulatory requirements. Providing this value to clients is a motivation for information management in projects. However, data handover is difficult as key people leave before project completion; and different data formats and structures are used in project delivery and operations. Lessons learnt from leading practice include defining data requirements at the outset, getting operations teams involved early, shaping the evolution of interoperable systems and standards, developing handover processes to check data rather than documentation, and fostering skills to use and update project data in operations

Combined neurostimulation and neuroimaging in cognitive neuroscience: past, present, and future

Modern neurostimulation approaches in humans provide controlled inputs into the operations of cortical regions, with highly specific behavioral consequences. This enables causal structure–function inferences, and in combination with neuroimaging, has provided novel insights into the basic mechanisms of action of neurostimulation on dis- tributed networks. For example,more recent work has established the capacity of transcranialmagnetic stimulation (TMS) to probe causal interregional influences, and their interaction with cognitive state changes. Combinations of neurostimulation and neuroimaging now face the challenge of integrating the known physiological effects of neu- rostimulationwith theoretical and biologicalmodels of cognition, for example,when theoretical stalemates between opposing cognitive theories need to be resolved. This will be driven by novel developments, including biologically informedcomputational network analyses for predicting the impactofneurostimulationonbrainnetworks, as well as novel neuroimaging and neurostimulation techniques. Such future developments may offer an expanded set of tools withwhich to investigate structure–function relationships, and to formulate and reconceptualize testable hypotheses about complex neural network interactions and their causal roles in cognition

Palm oil, land rights and ecosystem services in Gbarpolu County, Liberia

As part of the rebuilding efforts following the long civil war, the Liberian government has renegotiated long-term contracts with international investors to exploit natural resources. Substantial areas of land have been handed out in large-scale concessions across Liberia during the last five years. While this may promote economic growth at the national level, such concessions are likely to have major environmental, social and economic impacts on local communities, who may not have been consulted on the proposed developments. This report examines the potential socio-economic and environmental impacts of a proposed large-scale oil palm concession in Bopolu District, Gbarpolu County in Liberia. The research provided an in-depth mapping of current resource use, livelihoods and ecosystems services, in addition to analysis of community consultation and perceptions of the potential impacts of the proposed development. This case study of a palm oil concession in Liberia highlights wider policy considerations regarding large-scale land acquisitions in the global South: • Formal mechanisms may be needed to ensure the process of Free, Prior, Informed Consent takes place effectively with affected communities and community land rights are safeguarded. • Rigorous Environmental and Social Impact Assessments need to be conducted before operations start. Accurate mapping of customary land rights, community resources and cultural sites, livelihoods, land use, biodiversity and ecosystems services is a critical tool in this process. • Greater clarity and awareness-raising of land tenure laws and policies is needed at all levels. Good governance and capacity-building of key institutions would help to ensure effective implementation of relevant laws and policies. • Efforts are needed to improve basic services and infrastructure in rural communities and invest in food crop cultivation in order to enhance food security and poverty alleviation. Increasing access to inputs, equipment, training and advice is especially important if male and female farmers are no longer able to practice shifting cultivation due to the reduction/ loss of customary land and the need to farm more intensively on smaller areas of land.

Bollène-2002 experiment: radar quantitative precipitation estimation in the Cévennes–Vivarais region, France

The Bollène-2002 Experiment was aimed at developing the use of a radar volume-scanning strategy for conducting radar rainfall estimations in the mountainous regions of France. A developmental radar processing system, called Traitements Régionalisés et Adaptatifs de Données Radar pour l’Hydrologie (Regionalized and Adaptive Radar Data Processing for Hydrological Applications), has been built and several algorithms were specifically produced as part of this project. These algorithms include 1) a clutter identification technique based on the pulse-to-pulse variability of reflectivity Z for noncoherent radar, 2) a coupled procedure for determining a rain partition between convective and widespread rainfall R and the associated normalized vertical profiles of reflectivity, and 3) a method for calculating reflectivity at ground level from reflectivities measured aloft. Several radar processing strategies, including nonadaptive, time-adaptive, and space–time-adaptive variants, have been implemented to assess the performance of these new algorithms. Reference rainfall data were derived from a careful analysis of rain gauge datasets furnished by the Cévennes–Vivarais Mediterranean Hydrometeorological Observatory. The assessment criteria for five intense and long-lasting Mediterranean rain events have proven that good quantitative precipitation estimates can be obtained from radar data alone within 100-km range by using well-sited, well-maintained radar systems and sophisticated, physically based data-processing systems. The basic requirements entail performing accurate electronic calibration and stability verification, determining the radar detection domain, achieving efficient clutter elimination, and capturing the vertical structure(s) of reflectivity for the target event. Radar performance was shown to depend on type of rainfall, with better results obtained with deep convective rain systems (Nash coefficients of roughly 0.90 for point radar–rain gauge comparisons at the event time step), as opposed to shallow convective and frontal rain systems (Nash coefficients in the 0.6–0.8 range). In comparison with time-adaptive strategies, the space–time-adaptive strategy yields a very significant reduction in the radar–rain gauge bias while the level of scatter remains basically unchanged. Because the Z–R relationships have not been optimized in this study, results are attributed to an improved processing of spatial variations in the vertical profile of reflectivity. The two main recommendations for future work consist of adapting the rain separation method for radar network operations and documenting Z–R relationships conditional on rainfall type.

Pocket data mining - big data on small devices

Owing to continuous advances in the computational power of handheld devices like smartphones and tablet computers, it has become possible to perform Big Data operations including modern data mining processes onboard these small devices. A decade of research has proved the feasibility of what has been termed as Mobile Data Mining, with a focus on one mobile device running data mining processes. However, it is not before 2010 until the authors of this book initiated the Pocket Data Mining (PDM) project exploiting the seamless communication among handheld devices performing data analysis tasks that were infeasible until recently. PDM is the process of collaboratively extracting knowledge from distributed data streams in a mobile computing environment. This book provides the reader with an in-depth treatment on this emerging area of research. Details of techniques used and thorough experimental studies are given. More importantly and exclusive to this book, the authors provide detailed practical guide on the deployment of PDM in the mobile environment. An important extension to the basic implementation of PDM dealing with concept drift is also reported. In the era of Big Data, potential applications of paramount importance offered by PDM in a variety of domains including security, business and telemedicine are discussed.

Basic theory behind parameterizing atmospheric convection

Last fall, a network of the European Cooperation in Science and Technology (COST), called “Basic Concepts for Convection Parameterization in Weather Forecast and Climate Models” (COST Action ES0905; see http://w3.cost.esf.org/index.php?id=205&action_number=ES0905), organized a 10-day training course on atmospheric convection and its parameterization. The aim of the workshop, held on the island of Brac, Croatia, was to help young scientists develop an in-depth understanding of the core theory underpinning convection parameterizations. The speakers also sought to impart an appreciation of the various approximations, compromises, and ansatz necessary to translate theory into operational practice for numerical models.

Rationality of service composition of Workflow Net in a service oriented architecture

Users’ requirements change drives an information system evolution. Consequently, such evolution affects those atomic services which provide functional operations from one state of their composition to another state of composition. A challenging issue associated with such evolution of the state of service composition is to ensure a resultant service composition remaining rational. This paper presents a method of Service Composition Atomic-Operation Set (SCAOS). SCAOS defines 2 classes of atomic operations and 13 kinds of basic service compositions to aid a state change process by using Workflow Net. The workflow net has algorithmic capabilities to compose the required services with rationality and maintain any changes to the services in a different composition also rational. This method can improve the adaptability to the ever changing business requirements of information systems in the dynamic environment.

Visual search for basic emotional expressions in autism; impaired processing of anger, fear and sadness, but a typical happy face advantage

Facial expression recognition was investigated in 20 males with high functioning autism (HFA) or Asperger syndrome (AS), compared to typically developing individuals matched for chronological age (TD CA group) and verbal and non-verbal ability (TD V/NV group). This was the first study to employ a visual search, “face in the crowd” paradigm with a HFA/AS group, which explored responses to numerous facial expressions using real-face stimuli. Results showed slower response times for processing fear, anger and sad expressions in the HFA/AS group, relative to the TD CA group, but not the TD V/NV group. Reponses to happy, disgust and surprise expressions showed no group differences. Results are discussed with reference to the amygdala theory of autism.

Simple M-factor algorithm for improved estimation of the basic maximum usable frequency of radio waves reflected from the ionospheric F-region

The equations of Milsom are evaluated, giving the ground range and group delay of radio waves propagated via the horizontally stratified model ionosphere proposed by Bradley and Dudeney. Expressions for the ground range which allow for the effects of the underlying E- and F1-regions are used to evaluate the basic maximum usable frequency or M-factors for single F-layer hops. An algorithm for the rapid calculation of the M-factor at a given range is developed, and shown to be accurate to within 5%. The results reveal that the M(3000)F2-factor scaled from vertical-incidence ionograms using the standard URSI procedure can be up to 7.5% in error. A simple addition to the algorithm effects a correction to ionogram values to make these accurate to 0.5%.

Basic concepts for convection parameterization in weather forecast and climate models: COST Action ES0905 final report

The research network “Basic Concepts for Convection Parameterization in Weather Forecast and Climate Models” was organized with European funding (COST Action ES0905) for the period of 2010–2014. Its extensive brainstorming suggests how the subgrid-scale parameterization problem in atmospheric modeling, especially for convection, can be examined and developed from the point of view of a robust theoretical basis. Our main cautions are current emphasis on massive observational data analyses and process studies. The closure and the entrainment–detrainment problems are identified as the two highest priorities for convection parameterization under the mass–flux formulation. The need for a drastic change of the current European research culture as concerns policies and funding in order not to further deplete the visions of the European researchers focusing on those basic issues is emphasized.

Neural stem cells, inflammation and NF-kappaB: basic principle of maintenance and repair or origin of brain tumours?

Several recent reports suggest that inflammatory signals play a decisive role in the self-renewal, migration and differentiation of multipotent neural stem cells (NSCs). NSCs are believed to be able to ameliorate the symptoms of several brain pathologies through proliferation, migration into the area of the lesion and either differentiation into the appropriate cell type or secretion of anti-inflammatory cytokines. Although NSCs have beneficial roles, current evidence indicates that brain tumours, such as astrogliomas or ependymomas are also caused by tumour-initiating cells with stem-like properties. However, little is known about the cellular and molecular processes potentially generating tumours from NSCs. Most pro-inflammatory conditions are considered to activate the transcription factor NF-kappaB in various cell types. Strong inductive effects of NF-kappaB on proliferation and migration of NSCs have been described. Moreover, NF-kappaB is constitutively active in most tumour cells described so far. Chronic inflammation is also known to initiate cancer. Thus, NF-kappaB might provide a novel mechanistic link between chronic inflammation, stem cells and cancer. This review discusses the apparently ambivalent role of NF-kappaB: physiological maintenance and repair of the brain via NSCs, and a potential role in tumour initiation. Furthermore, it reveals a possible mechanism of brain tumour formation based on inflammation and NF-kappaB activity in NSCs.

The error of representation: basic understanding

Representation error arises from the inability of the forecast model to accurately simulate the climatology of the truth. We present a rigorous framework for understanding this kind of error of representation. This framework shows that the lack of an inverse in the relationship between the true climatology (true attractor) and the forecast climatology (forecast attractor) leads to the error of representation. A new gain matrix for the data assimilation problem is derived that illustrates the proper approaches one may take to perform Bayesian data assimilation when the observations are of states on one attractor but the forecast model resides on another. This new data assimilation algorithm is the optimal scheme for the situation where the distributions on the true attractor and the forecast attractors are separately Gaussian and there exists a linear map between them. The results of this theory are illustrated in a simple Gaussian multivariate model.