138 research outputs found
Design and Implementation of Constraints for 3D Spatial Database: Using Climate City Campus Database as an Example
No full textNowadays the field of geo-information is undergoing major changes, and the transition from 2D to 3D is having a major influence. A significant amount of 3D datasets are stored in the database. Experts are aware that new quality control mechanisms need to be built into the database systems in order to secure and guarantee high-performing data. Constraints are effective in providing solutions needed to avoid errors and enable maintenance of data quality. Whereas constraints for 2D geographic datasets have already been the subject of several research projects, studies into 3D geo-data constraints are largely unexplored. This thesis research discovers a new approach to model, conceptualise and implement 3D geo-constraints which can function in the database. At the outset, constraints can be formulated using natural language. As natural language is subjective and varies between individuals, expressions can be ambiguous and can easily cause confusion. So spatial constraints are abstracted using geometry that depicts the exact shape, and also topology that reveals the spatial relationship between geometries. This step makes the meaning of a constraint clearer to others. Furthermore, using standardised UML diagrams and OCL expressions, geo-constraints can be formalised to an extent that not only humans, but also machines can understand them. With model-driven architecture supported by various softwares, OCL expressions can be automatically converted to other models/executable codes (e.g. PL/SQL) just by a few clicks. And with small modifications, database triggers can be formulated to carry out constraints check. A database including various topographic objects (e.g. buildings, trees, roads, grass, water-bodies and terrains) is used as a study case to apply the discovered approach. During this research, a first attempt to formulate 3D geo-constraints in OCL has been made. These expressions can be tested and translated to other models/implementations when the OCL standard is extended with spatial types and operations. In the implementation stage, the current 3D functions in Oracle Spatial database are found to be insufficient. A new 3D function using existing 2D functions - plus additional code relating to computational geometry - has been developed by the author to bridge the gap. Based upon this function, a large group of spatial constraints which apply to objects in 3D space can be checked. Bentley Map and Python IDLE are used to test the performance of constraints as well as the visualisation of warning messages to clients. Database error messages are immediately displayed on the front-ends when a modification that does not satisfy a constraint is attempted to commit to the database. During the case study, new classes of constraints are also discovered. They are higher-level constraints, parameterised constraints, constraints allowing exceptional instances, extra-check rules to detect conflicting constraints and constraints relating to multi-scale representations.MSc GeomaticsGIS-technologyOTB Research Institute for the Built Environmen
Elimination of correlation in random codes for arbitrarily varying channels
No full textAhlswede R. Elimination of correlation in random codes for arbitrarily varying channels. Zeitschrift für Wahrscheinlichkeitstheorie und verwandte Gebiete. 1978;44(2):159-175.The author determines for arbitrarily varying channels a) the average error capacity and b) the maximal error capacity in case of randomized encoding. A formula for the average error capacity in case of randomized encoding was announced several years ago by Dobrushin ([3]). Under a mild regularity condition this formula turns out to be valid and follows as consequence from either a) or b)
Smart Operation of Gas Turbine Combined Cycle Plants: Prediction and Improvement of Thermal Efficiency at Part Load
No full textThis thesis investigates various operational aspects of Gas Turbine Combined Cycle Power Plants (GTCC). GTCC power plants are expected to play an increasingly important role in the balancing of supply and demand in the electricity grid. Although originally meant for predominantly base load operation with high efficiencies, market circumstances, namely the increasing supply of unpredictable wind and solar power, force these units to be operated frequently across a wide range of load settings. The required flexibility opens a need for models, that can predict the plant performance accurately at design point as well as off-design conditions. The models and performance data, made available by equipment manufacturers, are usually too general to be applied for accurate prediction and optimization purposes. Adding to this, the electricity producing companies usually do not possess detailed design information, so that creating accurate process models presents an extra challenge. The chapters of this thesis are dedicated to the proposal of several methods for overcoming challenges related to the operation of existing gas turbine combined cycle plants in current and future energy markets. All models and procedures developed in the framework of this thesis, are applied to the Alstom KA-26-1 GTCC as a case study. Two of these units were installed at the Maxima Power Station in Lelystad for GDF SUEZ Energy The Netherlands. The current study is first placed in the broader context of the developments in the electricity market, and of research fields related to this subject. The importance of accurate simulation tools is motivated, and the potential role of uncertainty management and optimization is put forward. The implications of the market developments are synthesized into a concise problem statement. The productive core of GTCC plants is the gas turbine, especially when there is no external firing in the steam cycle. A step wise method for accurately modeling the design and off-design steady state performance of gas turbines is presented. Tuning performance models to measured data typically available to an engine user is an important task. Therefore, a method for achieving this is proposed and applied to a case study: the GT26, an industrial gas turbine (part of the KA-26 plant) with two sequential combustor components. The results of this modeling effort indicate that the accuracy decreases towards part load. Thermodynamic modeling of the steam cycle, although a widely practiced discipline, still presents some challenges in case of industrial-size units. Second-law analysis is often added to thermodynamic flow sheet calculations; this can be enhanced by analyzing the interaction between plant components with the help of a novel procedure presented in the thesis. For this purpose, the plant model is calculated over a randomly and uniformly distributed set of input conditions, calculating the (second law) thermodynamic losses of major components for every case. (The term numerical experiment is used for this procedure.) After this, the resulting data is processed and visualized to reveal expected as well as unexpected mutual relations between the losses of individual plant components. When gas turbine and steam cycle models, and computer models in general, are applied to make predictions, and economical decisions are based on these models, there is always an amount of uncertainty present with respect to the validity of the predictions. Quantification and reduction of this uncertainty can be of significant value for stakeholders. In this context, an existing method for statistical analysis and calibration of computer models, the Kennedy & O'Hagan framework, is applied to the the previously presented gas turbine and steam cycle models. The purpose is to enhance the accuracy of (especially) part load efficiency prediction by calibrating the models with the available (industrial) measurements. The mathematical tools applied in this framework are explained, along with the manner in which it is applied to the gas turbine and steam cycle models respectively. For plant performance prediction, it is necessary to integrate the models, so that uncertainties in one model are propagated through the next. Two methods are described for achieving this: integration of the models can be done either before or after calibration. The two stochastic integration methods are applied to predict the efficiency of the case study plant. While both methods produce accurate results, there is an indication that integration after calibration is slightly more accurate. The most important objective for the current study, besides accurate performance prediction, is the proposal of efficiency optimization methods. The final part of the thesis illustrates methods for analyzing efficiency improvement possibilities of existing (gas turbine combined cycle) power plants, and optimizing part load efficiency with steady state plant models. Firstly, the data from the numerical experiment mentioned earlier are processed. By comparing how strong the exergy losses in major components are correlated to overall thermal efficiency of the plant, the low pressure steam turbine is shown to be the component whose thermodynamic losses have the largest effect on the variations in overall plant efficiency. However, it is also known that gas turbine losses represent the largest exergy loss. This seeming contradiction is thoroughly explained in the thesis. By using a clustering algorithm, operational regimes are revealed with respect to the losses in the low pressure steam turbine and gas turbine. Efficiency optimization is performed at ambient conditions corresponding to these distinct operational regimes. The results of optimization indicate that the optimum set of operational settings is different for each of the identified regimes, thereby confirming that they are distinct regimes. After using deterministic models for the efficiency maximization, model uncertainty is incorporated in the calculations, and the stochastic models presented earlier are applied. The difference with the previous optimizations is that in this case, the applied model has been proven to give more accurate results, and it provides the statistical distribution and expected value of the plant efficiency, not just a deterministic value. The results of optimization under uncertainty are compared to results of deterministic optimization under equal ambient conditions: the resulting optimal operational settings for both cases are shown to be similar in many aspects; differences are analyzed and put into perspective. The final part of the thesis synthesizes the main conclusions and recommendations from the previous sub-studies and places them in the general context of the research field. Suggestions are proposed for possible applications of the proposed methodologies to problems which are outside the scope of the thesis.Process & EnergyMechanical, Maritime and Materials Engineerin
Author correction: obesity and ethnicity alter gene expression in skin
No full textDaniel Butler was omitted from the author list in the original version of this Article. The Author contributions section now reads: “J.M.W. designed, conducted, and contributed to the writing of the manuscript, prepared Fig. 1. S.G. evaluated and did statistical analysis on the skin and fat samples, prepared Figs. 2–9. J.O.A. evaluated and contributed to writing the manuscript. D.B prepared and sequenced DNA libraries for the skin microbiota data, and wrote the applicable parts of the methods section. C.M. analyzed and wrote up the skin microbiota data, prepared Fig. 10. All authors have read the manuscript and approved its contents. D.D. analyzed and wrote up the skin microbiota data. S.Z. ran and analyzed the skin metabolite data. J.S. assisted in design, analysis and wrote up the skin metabolite data. J.K. assisted in analysis write up of skin and fat data. J.L.B. assisted in analysis, interpretation and writing of the manuscript. P.R.H. designed, analyzed, interpreted the data, and was the primary author of the manuscript.” This has been corrected in the PDF and HTML versions of the Article, and in the accompanying Supplementary Information file.</p
Author Correction: the Influence of Nano Filter Elements on Pressure Drop and Pollutant Elimination Efficiency in Town Border Stations
No full textThe original version of this Article contained an error in the order of the author names, which was incorrectly given as Hamed Ebadiyan, Saeed Zeinali Heris, Seyed Borhan Mousavi, Shamin Hosseini Nami ; Mousa Mohammadpourfard. Consequently, in the Author Contributions section, “H.E. Investigation. S.Z.H. Supervision, Conceptualization, Methodology, Validation. S.B.M. Formal analysis, Writing original draft. S.H.N. Formal analysis, Writing original draft. M.M. Validation.” now reads: “S.Z.H. Supervision, Conceptualization, Methodology, Validation. H.E. Investigation. S.B.M. Formal analysis, Writing original draft. S.H.N. Formal analysis, Writing original draft. M.M. Validation.” The original Article has been corrected. © 2023, The Author(s)
Effects of tuber size and burial depth on germination and plant growth of the submerged macrophyte Vallisneria spinulosa S.Z. Yan at different light intensities
No full text© 2022, The Author(s), under exclusive licence to Springer Nature Switzerland AG.The characteristics of propagules markedly impact the germination and plant growth of submerged macrophytes. Vallisneria spinulosa S.Z. Yan is a common submerged macrophyte and has been widely used in lake restoration projects. The responses of tuber germination and plant growth to different tuber sizes and burial depths are not well known for this species and may vary with light intensity. In this study, the tuber germination and plant growth of V. spinulosa germinated from two levels of tuber sizes (large and small) and two different burial depths in the sediment (5 and 15 cm) were tested at two light intensities (high and low) by measuring morphological and physiological traits. Although light intensity, tuber size and burial depth did not affect the tuber germination, they significantly influenced the morphological and physiological traits of the plants. Light intensity had the greatest effect on plant growth, followed by tuber size, while burial depth had the least effect. High light, large tuber and shallow burial depth favoured the plant growth performance (plant biomass, ramet and leaf numbers). The growth performance of plants germinated from small tubers was more susceptible to changes in light intensity and burial depth. Soluble carbohydrate and free amino acid contents were negatively and starch content positively correlated with the morphological traits. The study highlights the importance of tuber size and burial depth for plant growth as well as their interactions with light, which should be considered when determining lake management and restoration strategies
Surgical Training During and After COVID-19: A Joint Trainee and Trainers Manifesto.
No full textinfo:eu-repo/semantics/publishedVersio
Making Use of Patient-Reported Outcome Measures for Haemorrhoidal Disease in Clinical Practice: A Perspective
No full textHaemorrhoidal disease (HD) affects millions of people around the world and for most it is a recurring problem. Increasingly, clinicians broaden their focus on the patient's experiences with haemorrhoidal symptoms, including their impact on daily life. The patient's experience can be assessed using a patient-reported outcome measure (PROM). A PROM facilitates a deeper understanding of the disease-burden and allows a clinician to obtain information directly from the patients about their experiences with the ailment. Over the last years, PROMs have shown their additional role to traditional outcomes for several diseases and have earned their place in the daily consultation room. In order to improve and personalize the treatment of HD, we endorse the use of validated PROMs in clinical care.</p>
The effect of delay in measurement on the performance of isolated DCA algorithms using DECT system
No full textDynamic Channel Assignment is an algorithm for assigning channels in a mobile wireless environment. In a system based on isolated Dynamic Channel Assignment, the base stations (Radio Fixed Parts or RFPs) operate total independently. The mobiles (Portable Parts or PPs) have the most responsibility for call set-ups and handovers. Portable Parts collect information about the available channels through measuring the carrier and interference power received on each active channel. The RFPs send the paging information and the information about the active channels to the PPs through a beacon signal. Each PP is at least locked to a channel and receives the information from the RFP it is locked to. The PPs listen once in a multiframe time interval to the beacon signal. The rest of the multiframe time, they are measuring. The measured values are up-dated once each multiframe. By an incoming call-request, the PP uses the gathered information and assigns the best channel to that call request. The gathered data are actually between zero to one multiframe old at the moment of assignment. This is called measurement delay. The state of mobile wireless environment may be changed during this delay time and the measured information may not be valid any more. The effect of this delay on the performance of DCA-based ‘Digital Enhanced Cordless Telecommunications (DECT)” is explored.Electrical Engineering, Mathematics and Computer ScienceTelecommunicatie- en Verkeersbegeleidingssysteme
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