THE FEASIBILITY OF USING WEB-BASED TECHNOLOGY FOR THE MANAGEMENT OF DREDGING PROJECTS

The research investigates the feasibility of using web-based project management systems for dredging. To achieve this objective the research assessed both the positive and negative aspects of using web-based technology for the management of dredging projects. Information gained from literature review and prior investigations of dredging projects revealed that project performance, social, political, technical, and business aspects of the organization were important factors in deciding to use web-based systems for the management of dredging projects. These factors were used to develop the research assumptions. An exploratory case study methodology was used to gather the empirical evidence and perform the analysis. An operational prototype of the system was developed to help evaluate developmental and functional requirements, as well as the influence on performance, and on the organization. The evidence gathered from three case study projects, and from a survey of 31 experts, were used to validate the assumptions. Baselines, representing the assumptions, were created as a reference to assess the responses and qualitative measures. The deviation of the responses was used to evaluate for the analysis. Finally, the conclusions were assessed by validating the assumptions with the evidence, derived from the analysis. The research findings are as follows: 1. The system would help improve project performance. 2. Resistance to implementation may be experienced if the system is implemented. Therefore, resistance to implementation needs to be investigated further and more R&D work is needed in order to advance to the final design and implementation. 3. System may be divided into standalone modules in order to simplify the system and facilitate incremental changes. 4. The QA/QC conceptual approach used by this research needs to be redefined during future R&D to satisfy both owners and contractors. Yin (2009) Case Study Research Design and Methods was used to develop the research approach, design, data collection, and analysis. Markus (1983) Resistance Theory was used during the assumptions definition to predict potential problems to the implementation of web-based project management systems for the dredging industry. Keen (1981) incremental changes and facilitative approach tactics were used as basis to classify solutions, and how to overcome resistance to implementation of the web-based project management system. Davis (1989) Technology Acceptance Model (TAM) was used to assess the solutions needed to overcome the resistances to the implementation of web-base management systems for dredging projects.

Setting the record straight: assessing the reliability of retrospective accounts of change

Conservation Letters published by Wiley Periodicals, Inc. Ecological degradation is accelerating, reducing our ability to detect and reverse declines. Resource user accounts have the potential to provide critical information on past change but their reliability can rarely be tested, hence they are often perceived as less valid than other forms of scientific data. We compared individual fishers' catch records, recorded 1-50 years ago, with their memories of past good, typical and poor catches for the corresponding time period. Good and poor catches were recalled with reasonable accuracy, matching variability in recorded catch with no significant change observed over time. Typical recalled catches were overestimated and became significantly more exaggerated over time, but were more comparable to mean than median recorded values. While accuracy of resource users' memory varied with the type of information recalled, our results suggest that carefully structured interview questions can produce reliable quantitative data to inform resource management, even after several decades have elapsed.

Budgeted semi-supervised support vector machine

Due to the prevalence of unlabeled data, semi-supervised learning has drawn significant attention and has been found applicable in many real-world applications. In this paper, we present the so-called Budgeted Semi-supervised Sup-port Vector Machine (BS3VM), a method that leverages the excellent generalization capacity of kernel-based method with the adjacent and distributive information carried in a spectral graph for semi-supervised learning purpose. The fact that the optimization problem of BS3VM can be solved directly in the primal form makes it fast and efficient in memory usage. We validate the proposed method on several benchmark datasets to demonstrate its accuracy and efficiency. The experimental results show that BS3VM can scale up efficiently to the large-scale datasets where it yields a comparable classification accuracy while simultaneously achieving a significant computational speed-up compared with the baselines.

A framework for mixed-type multi-outcome prediction with applications in healthcare

Health analysis often involves prediction of multiple outcomes of mixed-type. Existing work is restrictive to either a limited number or specific outcome types. We propose a framework for mixed-type multi-outcome prediction. Our proposed framework proposes a cumulative loss function composed of a specific loss function for each outcome type - as an example, least square (continuous outcome), hinge (binary outcome), poisson (count outcome) and exponential (non-negative outcome). Tomodel these outcomes jointly, we impose a commonality across the prediction parameters through a common matrix-Normal prior. The framework is formulated as iterative optimization problems and solved using an efficient Block coordinate descent method (BCD). We empirically demonstrate both scalability and convergence. We apply the proposed model to a synthetic dataset and then on two real-world cohorts: a Cancer cohort and an Acute Myocardial Infarction cohort collected over a two year period. We predict multiple emergency related outcomes - as example, future emergency presentations (binary), emergency admissions (count), emergency length-of-stay-days (non-negative) and emergency time-to-next-admission-day (non-negative). Weshow that the predictive performance of the proposed model is better than several state-of-the-art baselines.

Improving sentiment analysis via sentence type classification using BiLSTM-CRF and CNN

Different types of sentences express sentiment in very different ways. Traditional sentence-level sentiment classification research focuses on one-technique-fits-all solution or only centers on one special type of sentences. In this paper, we propose a divide-and-conquer approach which first classifies sentences into different types, then performs sentiment analysis separately on sentences from each type. Specifically, we find that sentences tend to be more complex if they contain more sentiment targets. Thus, we propose to first apply a neural network based sequence model to classify opinionated sentences into three types according to the number of targets appeared in a sentence. Each group of sentences is then fed into a one-dimensional convolutional neural network separately for sentiment classification. Our approach has been evaluated on four sentiment classification datasets and compared with a wide range of baselines. Experimental results show that: (1) sentence type classification can improve the performance of sentence-level sentiment analysis; (2) the proposed approach achieves state-of-the-art results on several benchmarking datasets.

Ontology forecasting in scientific literature:semantic concepts prediction based on innovation-adoption priors

The ontology engineering research community has focused for many years on supporting the creation, development and evolution of ontologies. Ontology forecasting, which aims at predicting semantic changes in an ontology, represents instead a new challenge. In this paper, we want to give a contribution to this novel endeavour by focusing on the task of forecasting semantic concepts in the research domain. Indeed, ontologies representing scientific disciplines contain only research topics that are already popular enough to be selected by human experts or automatic algorithms. They are thus unfit to support tasks which require the ability of describing and exploring the forefront of research, such as trend detection and horizon scanning. We address this issue by introducing the Semantic Innovation Forecast (SIF) model, which predicts new concepts of an ontology at time t + 1, using only data available at time t. Our approach relies on lexical innovation and adoption information extracted from historical data. We evaluated the SIF model on a very large dataset consisting of over one million scientific papers belonging to the Computer Science domain: the outcomes show that the proposed approach offers a competitive boost in mean average precision-at-ten compared to the baselines when forecasting over 5 years.

ICES meets marine historical ecology: placing the history of fish and fisheries in current policy context

As a discipline, marine historical ecology (MHE) has contributed significantly to our understanding of the past state of the marine environment when levels of human impact were often very different from those today.What is less widely known is that insights from MHE have made headway into being applied within the context of present-day and long-term management and policy. This study draws attention to the applied value of MHE. We demonstrate that a broad knowledge base exists with potential for management application and advice, including the development of baselines and reference levels.Using a number of case studies from around the world,we showcase the value of historical ecology in understanding change and emphasize how it either has already informed management or has the potential to do so soon.We discuss these case studies in a context of the science–policy interface around six themes that are frequently targeted by current marine and maritime policies: climate change, biodiversity conservation, ecosystem structure, habitat integrity, food security, and human governance.We encourage science–policy bodies to actively engage with contributions from MHE, as well informed policy decisions need to be framed within the context of historical reference points and past resource or ecosystem changes.

Autonomous formation flying: unified control and collision avoidance methods for close manoeuvring spacecraft

The idea of spacecraft formations, flying in tight configurations with maximum baselines of a few hundred meters in low-Earth orbits, has generated widespread interest over the last several years. Nevertheless, controlling the movement of spacecraft in formation poses difficulties, such as in-orbit high-computing demand and collision avoidance capabilities, which escalate as the number of units in the formation is increased and complicated nonlinear effects are imposed to the dynamics, together with uncertainty which may arise from the lack of knowledge of system parameters. These requirements have led to the need of reliable linear and nonlinear controllers in terms of relative and absolute dynamics. The objective of this thesis is, therefore, to introduce new control methods to allow spacecraft in formation, with circular/elliptical reference orbits, to efficiently execute safe autonomous manoeuvres. These controllers distinguish from the bulk of literature in that they merge guidance laws never applied before to spacecraft formation flying and collision avoidance capacities into a single control strategy. For this purpose, three control schemes are presented: linear optimal regulation, linear optimal estimation and adaptive nonlinear control. In general terms, the proposed control approaches command the dynamical performance of one or several followers with respect to a leader to asymptotically track a time-varying nominal trajectory (TVNT), while the threat of collision between the followers is reduced by repelling accelerations obtained from the collision avoidance scheme during the periods of closest proximity. Linear optimal regulation is achieved through a Riccati-based tracking controller. Within this control strategy, the controller provides guidance and tracking toward a desired TVNT, optimizing fuel consumption by Riccati procedure using a non-infinite cost function defined in terms of the desired TVNT, while repelling accelerations generated from the CAS will ensure evasive actions between the elements of the formation. The relative dynamics model, suitable for circular and eccentric low-Earth reference orbits, is based on the Tschauner and Hempel equations, and includes a control input and a nonlinear term corresponding to the CAS repelling accelerations. Linear optimal estimation is built on the forward-in-time separation principle. This controller encompasses two stages: regulation and estimation. The first stage requires the design of a full state feedback controller using the state vector reconstructed by means of the estimator. The second stage requires the design of an additional dynamical system, the estimator, to obtain the states which cannot be measured in order to approximately reconstruct the full state vector. Then, the separation principle states that an observer built for a known input can also be used to estimate the state of the system and to generate the control input. This allows the design of the observer and the feedback independently, by exploiting the advantages of linear quadratic regulator theory, in order to estimate the states of a dynamical system with model and sensor uncertainty. The relative dynamics is described with the linear system used in the previous controller, with a control input and nonlinearities entering via the repelling accelerations from the CAS during collision avoidance events. Moreover, sensor uncertainty is added to the control process by considering carrier-phase differential GPS (CDGPS) velocity measurement error. An adaptive control law capable of delivering superior closed-loop performance when compared to the certainty-equivalence (CE) adaptive controllers is finally presented. A novel noncertainty-equivalence controller based on the Immersion and Invariance paradigm for close-manoeuvring spacecraft formation flying in both circular and elliptical low-Earth reference orbits is introduced. The proposed control scheme achieves stabilization by immersing the plant dynamics into a target dynamical system (or manifold) that captures the desired dynamical behaviour. They key feature of this methodology is the addition of a new term to the classical certainty-equivalence control approach that, in conjunction with the parameter update law, is designed to achieve adaptive stabilization. This parameter has the ultimate task of shaping the manifold into which the adaptive system is immersed. The performance of the controller is proven stable via a Lyapunov-based analysis and Barbalat’s lemma. In order to evaluate the design of the controllers, test cases based on the physical and orbital features of the Prototype Research Instruments and Space Mission Technology Advancement (PRISMA) are implemented, extending the number of elements in the formation into scenarios with reconfigurations and on-orbit position switching in elliptical low-Earth reference orbits. An extensive analysis and comparison of the performance of the controllers in terms of total Δv and fuel consumption, with and without the effects of the CAS, is presented. These results show that the three proposed controllers allow the followers to asymptotically track the desired nominal trajectory and, additionally, those simulations including CAS show an effective decrease of collision risk during the performance of the manoeuvre.