Search results for: numerical weather prediction

Authors: Sarah E. Nelson, Casey Weiner, Alexander Sigmon, Jun Hua, Haris I. Sair, Jose I. Suarez, Robert D. Stevens

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Graph-theoretical information from structural connectomes indicated significant connectivity changes and improved acute prognostication in a Random Forest (RF) model in aneurysmal subarachnoid hemorrhage (aSAH), which can lead to significant morbidity and mortality and has traditionally been fraught by poor methods to predict outcome. This study’s hypothesis was that structural connectivity changes occur in canonical brain networks of acute aSAH patients, and that these changes are associated with functional outcome at six months. In a prospective cohort of patients admitted to a single institution for management of acute aSAH, patients underwent diffusion tensor imaging (DTI) as part of a multimodal MRI scan. A weighted undirected structural connectome was created of each patient’s images using Constant Solid Angle (CSA) tractography, with 176 regions of interest (ROIs) defined by the Johns Hopkins Eve atlas. ROIs were sorted into four networks: Default Mode Network, Executive Control Network, Salience Network, and Whole Brain. The resulting nodes and edges were characterized using graph-theoretic features, including Node Strength (NS), Betweenness Centrality (BC), Network Degree (ND), and Connectedness (C). Clinical (including demographics and World Federation of Neurologic Surgeons scale) and graph features were used separately and in combination to train RF and Logistic Regression classifiers to predict two outcomes: dichotomized modified Rankin Score (mRS) at discharge and at six months after discharge (favorable outcome mRS 0-2, unfavorable outcome mRS 3-6). A total of 56 aSAH patients underwent DTI a median (IQR) of 7 (IQR=8.5) days after admission. The best performing model (RF) combining clinical and DTI graph features had a mean Area Under the Receiver Operator Characteristic Curve (AUROC) of 0.88 ± 0.00 and Area Under the Precision Recall Curve (AUPRC) of 0.95 ± 0.00 over 500 trials. The combined model performed better than the clinical model alone (AUROC 0.81 ± 0.01, AUPRC 0.91 ± 0.00). The highest-ranked graph features for prediction were NS, BC, and ND. These results indicate reorganization of the connectome early after aSAH. The performance of clinical prognostic models was increased significantly by the inclusion of DTI-derived graph connectivity metrics. This methodology could significantly improve prognostication of aSAH.

Keywords: connectomics, diffusion tensor imaging, graph theory, machine learning, subarachnoid hemorrhage

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Authors: Shengnan Chen, Shuhua Wang

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Successful production of hydrocarbon from unconventional tight oil reserves has changed the energy landscape in North America. The oil contained within these reservoirs typically will not flow to the wellbore at economic rates without assistance from advanced horizontal well and multi-stage hydraulic fracturing. Efficient and economic development of these reserves is a priority of society, government, and industry, especially under the current low oil prices. Meanwhile, society needs technological and process innovations to enhance oil recovery while concurrently reducing environmental impacts. Recently, big data analysis and artificial intelligence become very popular, developing data-driven insights for better designs and decisions in various engineering disciplines. However, the application of data mining in petroleum engineering is still in its infancy. The objective of this research aims to apply intelligent data analysis and data-driven models to exploit unconventional oil reserves both efficiently and economically. More specifically, a comprehensive database including the reservoir geological data, reservoir geophysical data, well completion data and production data for thousands of wells is firstly established to discover the valuable insights and knowledge related to tight oil reserves development. Several data analysis methods are introduced to analysis such a huge dataset. For example, K-means clustering is used to partition all observations into clusters; principle component analysis is applied to emphasize the variation and bring out strong patterns in the dataset, making the big data easy to explore and visualize; exploratory factor analysis (EFA) is used to identify the complex interrelationships between well completion data and well production data. Different data mining techniques, such as artificial neural network, fuzzy logic, and machine learning technique are then summarized, and appropriate ones are selected to analyze the database based on the prediction accuracy, model robustness, and reproducibility. Advanced knowledge and patterned are finally recognized and integrated into a modified self-adaptive differential evolution optimization workflow to enhance the oil recovery and maximize the net present value (NPV) of the unconventional oil resources. This research will advance the knowledge in the development of unconventional oil reserves and bridge the gap between the big data and performance optimizations in these formations. The newly developed data-driven optimization workflow is a powerful approach to guide field operation, which leads to better designs, higher oil recovery and economic return of future wells in the unconventional oil reserves.

Keywords: big data, artificial intelligence, enhance oil recovery, unconventional oil reserves

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Authors: Merouane Salhi, Mohamed Roudane, Abdelkader Kirad

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In this work, we present a new form of characteristics method, which is a technique for solving partial differential equations. Typically, it applies to first-order equations; the aim of this method is to reduce a partial differential equation to a family of ordinary differential equations along which the solution can be integrated from some initial data. This latter developed under the real gas theory, because when the thermal and the caloric imperfections of a gas increases, the specific heat and their ratio do not remain constant anymore and start to vary with the gas parameters. The gas doesn’t stay perfect. Its state equation change and it becomes for a real gas. The presented equations of the characteristics remain valid whatever area or field of study. Here we need have inserted the developed Prandtl Meyer function in the mathematical system to find a new model when the effect of stagnation pressure is taken into account. In this case, the effects of molecular size and intermolecular attraction forces intervene to correct the state equation, the thermodynamic parameters and the value of Prandtl Meyer function. However, with the assumptions that Berthelot’s state equation accounts for molecular size and intermolecular force effects, expressions are developed for analyzing the supersonic flow for thermally and calorically imperfect gas. The supersonic parameters depend directly on the stagnation parameters of the combustion chamber. The resolution has been made by the finite differences method using the corrector predictor algorithm. As results, the developed mathematical model used to design 2D minimum length nozzles under effect of the stagnation parameters of fluid flow. A comparison for air with the perfect gas PG and high temperature models on the one hand and our results by the real gas theory on the other of nozzles shapes and characteristics are made.

Keywords: numerical methods, nozzles design, real gas, stagnation parameters, supersonic expansion, the characteristics method

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Authors: Ahmadreza Ebadati, Asaad Y. Shamseldin, Amin Ghadirian

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Seawall geometry plays a crucial role in mitigating wave impacts, though detailed exploration of its manipulation is limited. This study delves into the effects of varying cross-shore seawall geometry on the dynamics of wave impacts, with a particular focus on vertical seawalls. Inspired by foundational insights linking seawall shape to hydraulic efficiency, this investigation centres on how alterations in seawall geometry can influence wave energy dissipation and subsequent wave impacts. The study investigates the 2D interaction of regular waves with a period of 2.1s with a vertical seawall and berm featuring small-scale cross-shore protrusions and recesses. Utilising OpenFOAM® simulations and a k-ω SST turbulence model, this investigation compares results to a base case simulation, which is partially calibrated with experimental data from a flume study. The analysis evaluates various geometric modifications, specifically interchanged protrusions and recesses at different heights and orientations along the seawall. Findings suggest that specific configurations, such as interchanged protrusions and recesses, can mitigate initial impact forces, while certain arrangements may intensify subsequent impacts. Key insights include the identification of geometry configurations that can effectively reduce the force impulse of slamming waves on coastal structures and potentially decrease the frequency and cost of seawall maintenance. This research contributes to the field by advancing the understanding of how seawall geometry influences wave forces and by providing actionable insights for the design of more resilient seawall structures. Further exploration of seawall geometry variation is recommended, advocating additional case studies to optimise designs tailored to specific coastal environments.

Keywords: seawall geometry, wave impact loads, numerical simulation, coastal engineering, wave-structure interaction

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Authors: Arvan Prakash Ankitha, Madasamy Arockiasamy

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The increasing demand for adopting pile foundations in bridgeshas pointed towardsthe need to constantly improve the existing analytical techniques for better understanding of the behavior of such foundation systems. This study presents a simplistic approach using the energy-based method to assess the displacement responses of piles subjected to general loading conditions: Axial Load, Lateral Load, and a Bending Moment. The governing differential equations and the boundary conditions for a bridge pile embedded in multi-layered soil strata subjected to the general loading conditions are obtained using the Hamilton’s principle employing variational principles and minimization of energies. The soil non-linearity has been incorporated through simple constitutive relationships that account for degradation of soil moduli with increasing strain values.A simple power law based on published literature is used where the soil is assumed to be nonlinear-elastic and perfectly plastic. A Tresca yield surface is assumed to develop the soil stiffness variation with different strain levels that defines the non-linearity of the soil strata. This numerical technique has been applied to a pile foundation in a two - layered soil strata for a pier supporting the bridge and solved using the software MATLAB R2019a. The analysis yields the bridge pile displacements at any depth along the length of the pile. The results of the analysis are in good agreement with the published field data and the three-dimensional finite element analysis results performed using the software ANSYS 2019R3. The methodology can be extended to study the response of the multi-strata soil supporting group piles underneath the bridge piers.

Keywords: pile foundations, deep foundations, multilayer soil strata, energy based method

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Authors: Corinna Zeli, Silvia Calati, Marco Simeoni, Chiara Comastri

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Developmental stuttering affects about 10% of preschool children; although the high percentage of natural recovery, a quarter of them will become an adult who stutters. An effective early intervention should help those children with high persistence risk for the future. The Psicodizione method for early stuttering is an Italian behavior indirect treatment for preschool children who stutter in which method parents act as good guides for communication, modeling their own fluency. In this study, we give a preliminary measure to evaluate the long-term effectiveness of Psicodizione method on stuttering preschool children with a high persistence risk. Among all Italian children treated with the Psicodizione method between 2018 and 2019, we selected 8 kids with at least 3 high risk persistence factors from the Illinois Prediction Criteria proposed by Yairi and Seery. The factors chosen for the selection were: one parent who stutters (1pt mother; 1.5pt father), male gender, ≥ 4 years old at onset; ≥ 12 months from onset of symptoms before treatment. For this study, the families were contacted after an average period of time of 14,7 months (range 3 - 26 months). Parental reports were gathered with a standard online questionnaire in order to obtain data reflecting fluency from a wide range of the children’s life situations. The minimum worthwhile outcome was set at "mild evidence" in a 5 point Likert scale (1 mild evidence- 5 high severity evidence). A second group of 6 children, among those treated with the Piscodizione method, was selected as high potential for spontaneous remission (low persistence risk). The children in this group had to fulfill all the following criteria: female gender, symptoms for less than 12 months (before treatment), age of onset <4 years old, none of the parents with persistent stuttering. At the time of this follow-up, the children were aged 6–9 years, with a mean of 15 months post-treatment. Among the children in the high persistence risk group, 2 (25%) hadn’t had stutter anymore, and 3 (37,5%) had mild stutter based on parental reports. In the low persistency risk group, the children were aged 4–6 years, with a mean of 14 months post-treatment, and 5 (84%) hadn’t had stutter anymore (for the past 16 months on average).62,5% of children at high risk of persistence after Psicodizione treatment showed mild evidence of stutter at most. 75% of parents confirmed a better fluency than before the treatment. The low persistence risk group seemed to be representative of spontaneous recovery. This study’s design could help to better evaluate the success of the proposed interventions for stuttering preschool children and provides a preliminary measure of the effectiveness of the Psicodizione method on high persistence risk children.

Keywords: early treatment, fluency, preschool children, stuttering

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Authors: Alexander Buhr, Klaus Ehrenfried

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Trackside induced airflow velocities, also known as slipstream velocities, are an important criterion for the design of high-speed trains. The maximum permitted values are given by the Technical Specifications for Interoperability (TSI) and have to be checked in the approval process. For train manufactures it is of great interest to know in advance, how new train geometries would perform in TSI tests. The Reynolds number in moving model experiments is lower compared to full-scale. Especially the limited model length leads to a thinner boundary layer at the rear end. The hypothesis is that the boundary layer rolls up to characteristic flow structures in the train wake, in which the maximum flow velocities can be observed. The idea is to enlarge the boundary layer using roughness elements at the train model head so that the ratio between the boundary layer thickness and the car width at the rear end is comparable to a full-scale train. This may lead to similar flow structures in the wake and better prediction accuracy for TSI tests. In this case, the design of the roughness elements is limited by the moving model rig. Small rectangular roughness shapes are used to get a sufficient effect on the boundary layer, while the elements are robust enough to withstand the high accelerating and decelerating forces during the test runs. For this investigation, High-Speed Particle Image Velocimetry (HS-PIV) measurements on an ICE3 train model have been realized in the moving model rig of the DLR in Göttingen, the so called tunnel simulation facility Göttingen (TSG). The flow velocities within the boundary layer are analysed in a plain parallel to the ground. The height of the plane corresponds to a test position in the EN standard (TSI). Three different shapes of roughness elements are tested. The boundary layer thickness and displacement thickness as well as the momentum thickness and the form factor are calculated along the train model. Conditional sampling is used to analyse the size and dynamics of the flow structures at the time of maximum velocity in the train wake behind the train. As expected, larger roughness elements increase the boundary layer thickness and lead to larger flow velocities in the boundary layer and in the wake flow structures. The boundary layer thickness, displacement thickness and momentum thickness are increased by using larger roughness especially when applied in the height close to the measuring plane. The roughness elements also cause high fluctuations in the form factors of the boundary layer. Behind the roughness elements, the form factors rapidly are approaching toward constant values. This indicates that the boundary layer, while growing slowly along the second half of the train model, has reached a state of equilibrium.

Keywords: boundary layer, high-speed PIV, ICE3, moving train model, roughness elements

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Authors: Badli Shah Mohd Yusoff, Khairur Rijal Jamaludin, Rozetta Dollah

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The emerging of reverse logistics and product recovery management is an important concept in reconciling economic and environmental objectives through recapturing values of the end of life product returns. End of life products contains valuable modules, parts, residues and materials that can create value if recovered efficiently. The main objective of this study is to explore and develop a model to recover as much of the economic value as reasonably possible to find the optimality of return acquisition and sorting to meet demand and maximize profits over time. In this study, the benefits that can be obtained for remanufacturer is to develop demand forecasting of used products in the future with uncertainty of returns and quality of products. Formulated based on a generic disassembly tree, the proposed model focused on three reverse logistics activity, namely refurbish, remanufacture and disposal incorporating all plausible means quality levels of the returns. While stricter sorting policy, constitute to the decrease amount of products to be refurbished or remanufactured and increases the level of discarded products. Numerical experiments carried out to investigate the characteristics and behaviour of the proposed model with mathematical programming model using Lingo 16.0 for medium-term planning of return acquisition, disassembly (refurbish or remanufacture) and disposal activities. Moreover, the model seeks an analysis a number of decisions relating to trade off management system to maximize revenue from the collection of use products reverse logistics services through refurbish and remanufacture recovery options. The results showed that full utilization in the sorting process leads the system to obtain less quantity from acquisition with minimal overall cost. Further, sensitivity analysis provides a range of possible scenarios to consider in optimizing the overall cost of refurbished and remanufactured products.

Keywords: core acquisition, end of life, reverse logistics, quality uncertainty

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Authors: Aarthi Sridhar, Henri Gavin, Karah Kelly

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Rolling Isolation Systems (RISs) are simple and effective means to mitigate earthquake hazards to equipment in critical and precious facilities, such as hospitals, network collocation facilities, supercomputer centers, and museums. The RIS works by isolating components acceleration the inertial forces felt by the subsystem. The RIS consists of two platforms with counter-facing concave surfaces (dishes) in each corner. Steel balls lie inside the dishes and allow the relative motion between the top and bottom platform. Formerly, a mathematical model for the dynamics of RISs was developed using Lagrange’s equations (LE) and experimentally validated. A new mathematical model was developed using Gauss’s Principle of Least Constraint (GPLC) and verified by comparing impulse response trajectories of the GPLC model and the LE model in terms of the peak displacements and accelerations of the top platform. Mathematical models for the RIS are tedious to derive because of the non-holonomic rolling constraints imposed on the system. However, using Gauss’s Principle of Least constraint to find the equations of motion removes some of the obscurity and yields a system that can be easily extended. Though the GPLC model requires more state variables, the equations of motion are far simpler. The non-holonomic constraint is enforced in terms of accelerations and therefore requires additional constraint stabilization methods in order to avoid the possibility that numerical integration methods can cause the system to go unstable. The GPLC model allows the incorporation of more physical aspects related to the RIS, such as contribution of the vertical velocity of the platform to the kinetic energy and the mass of the balls. This mathematical model for the RIS is a tool to predict the motion of the isolation platform. The ability to statistically quantify the expected responses of the RIS is critical in the implementation of earthquake hazard mitigation.

Keywords: earthquake hazard mitigation, earthquake isolation, Gauss’s Principle of Least Constraint, nonlinear dynamics, rolling isolation system

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Authors: Md. Asif Ullah, M. A. R. Sarkar

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This paper illustrates 2-D and 3-D simulations of sub-channels of a Pressurized Water Reactor (PWR) having hexagonal array of fuel rods. At a steady state, the temperature of outer surface of the cladding of fuel rod is kept about 1200°C. The temperature of this isothermal surface is taken as boundary condition for simulation. Water with temperature of 290°C is given as a coolant inlet to the primary water circuit which is pressurized upto 157 bar. Turbulent flow of pressurized water is used for heat removal. In 2-D model, temperature, velocity, pressure and Nusselt number distributions are simulated in a vertical sectional plane through the sub-channels of a hexagonal fuel rod assembly. Temperature, Nusselt number and Y-component of convective heat flux along a line in this plane near the end of fuel rods are plotted for different Reynold’s number. A comparison between X-component and Y-component of convective heat flux in this vertical plane is analyzed. Hexagonal fuel rod assembly has three types of sub-channels according to geometrical shape whose boundary conditions are different too. In 3-D model, temperature, velocity, pressure, Nusselt number, total heat flux magnitude distributions for all the three sub-channels are studied for a suitable Reynold’s number. A horizontal sectional plane is taken from each of the three sub-channels to study temperature, velocity, pressure, Nusselt number and convective heat flux distribution in it. Greater values of temperature, Nusselt number and Y-component of convective heat flux are found for greater Reynold’s number. X-component of convective heat flux is found to be non-zero near the bottom of fuel rod and zero near the end of fuel rod. This indicates that the convective heat transfer occurs totally along the direction of flow near the outlet. As, length to radius ratio of sub-channels is very high, simulation for a short length of the sub-channels are done for graphical interface advantage. For the simulations, Turbulent Flow (K-Є ) module and Heat Transfer in Fluids (ht) module of COMSOL MULTIPHYSICS 5.0 are used.

Keywords: sub-channels, Reynold’s number, Nusselt number, convective heat transfer

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Authors: Ivan Gunass Govender, Andrea Giampiccoli

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A developmental state should use community engagement to facilitate socio-economic development for disadvantaged groups and individual members of society through empowerment, social justice, sustainability, and self-reliance. In this regard, community-based tourism (CBT) as a growing market should be an indigenous effort aided by external facilitation. Since this form of tourism presents its own preconditions, characteristics, and challenges, it could be guided by higher education institutions engagement. In particular, the facilitation should not only serve to assist the community members to reach their own goals; but rather also focus on learning through knowledge creation and sharing with the engagement of higher education institutions. While the increased relevance of CBT has produced various CBT manuals (or handbooks/guidelines) documents aimed to ‘teach’ and assist various entities in CBT development, this research aims to analyse the current monitoring & evaluation (M&E) manuals and thereafter, propose an M&E model for CBT. It is important to mention that all too often effective monitoring is seldom carried out thus risking the long-term sustainability and improvement of the CBT ventures. Therefore, the proposed model will also consider some inputs external to the tourism field, but in relation to local economic development (LED) matters from the previously proposed development monitoring and evaluation system framework. M&E should be seen as fundamental components of any CBT initiative, and the whole CBT intervention should be evaluated. In this context, M&E in CBT should go beyond strict ‘numerical’ economic matters and should be understood in a holistic development. In addition, M&E in CBT should not consider issues in various ‘compartments’ such as tourists, tourism attractions, CBT owners/participants, and stakeholder engagement but as interdependent components of a macro-ecosystem. Finally, the external facilitation process should be structured in a way to promote community self-reliance in both the intervention and the M&E process. The research will attempt to propose an M&E model for CBT so as to enhance the CBT possibilities of long-term growth and success through effective collaborations with key stakeholders.

Keywords: community-based tourism, community-engagement, monitoring and evaluation, stakeholders

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Authors: Arlene López-Sampson, Tony Page, Betsy Jackes

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Aquilaria genus produces a highly valuable fragrant oleoresin known as agarwood. Agarwood forms in a few trees in the wild as a response to injure or pathogen attack. The resin is used in perfume and incense industry and medicine. Cultivation of Aquilaria species as a sustainable source of the resin is now a common strategy. Physiological traits are frequently used as a proxy of crop and tree productivity. Aquilaria species growing in Queensland, Australia were studied to investigate relationship between leaf-productivity traits with tree growth. Specifically, 28 trees, representing 12 plus trees and 16 trees from yield plots, were selected to conduct carbon isotope analysis (δ13C) and monitor six leaf attributes. Trees were grouped on four diametric classes (diameter at 150 mm above ground level) ensuring the variability in growth of the whole population was sampled. Model averaging technique based on the Akaike’s information criterion (AIC) was computed to identify whether leaf traits could assist in diameter prediction. Carbon isotope values were correlated with height classes and leaf traits to determine any relationship. In average four leaves per shoot were recorded. Approximately one new leaf per week is produced by a shoot. Rate of leaf expansion was estimated in 1.45 mm day-1. There were no statistical differences between diametric classes and leaf expansion rate and number of new leaves per week (p > 0.05). Range of δ13C values in leaves of Aquilaria species was from -25.5 ‰ to -31 ‰ with an average of -28.4 ‰ (± 1.5 ‰). Only 39% of the variability in height can be explained by δ13C in leaf. Leaf δ13C and nitrogen content values were positively correlated. This relationship implies that leaves with higher photosynthetic capacities also had lower intercellular carbon dioxide concentrations (ci/ca) and less depleted values of 13C. Most of the predictor variables have a weak correlation with diameter (D). However, analysis of the 95% confidence of best-ranked regression models indicated that the predictors that could likely explain growth in Aquilaria species are petiole length (PeLen), values of δ13C (true13C) and δ15N (true15N), leaf area (LA), specific leaf area (SLA) and number of new leaf produced per week (NL.week). The model constructed with PeLen, true13C, true15N, LA, SLA and NL.week could explain 45% (R2 0.4573) of the variability in D. The leaf traits studied gave a better understanding of the leaf attributes that could assist in the selection of high-productivity trees in Aquilaria.

Keywords: 13C, petiole length, specific leaf area, tree growth

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Authors: Sergo Esadze

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Seismic resistance norms require calculation of cantilevers on vertical components of the base seismic acceleration. Long span cantilevers, as a rule, must be calculated as a separate construction element. According to the architectural-planning solution, functional purposes and environmental condition of a designing buildings/structures, long span cantilever construction may be of very different types: both by main bearing element (beam, truss, slab), and by material (reinforced concrete, steel). A choice from these is always linked with bearing construction system of the building. Research of vertical seismic vibration of these constructions requires individual approach for each (which is not specified in the norms) in correlation with model of seismic load. The latest may be given both as deterministic load and as a random process. Loading model as a random process is more adequate to this problem. In presented paper, two types of long span (from 6m – up to 12m) reinforcement concrete cantilever beams have been considered: a) bearing elements of cantilevers, i.e., elements in which they fixed, have cross-sections with large sizes and cantilevers are made with haunch; b) cantilever beam with load-bearing rod element. Calculation models are suggested, separately for a) and b) types. They are presented as systems with finite quantity degree (concentrated masses) of freedom. Conditions for fixing ends are corresponding with its types. Vertical acceleration and vertical component of the angular acceleration affect masses. Model is based on assumption translator-rotational motion of the building in the vertical plane, caused by vertical seismic acceleration. Seismic accelerations are considered as random processes and presented by multiplication of the deterministic envelope function on stationary random process. Problem is solved within the framework of the correlation theory of random process. Solved numerical examples are given. The method is effective for solving the specific problems.

Keywords: cantilever, random process, seismic load, vertical acceleration

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Authors: Chong Hu, Tiantian Wang, Zhe Li, Ourui Huang, Yichen Pan

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When the train is running in a high geothermal tunnel, changes in the temperature field will cause disturbances in the propagation and superposition of pressure waves in the tunnel, which in turn have an effect on the aerodynamic resistance of the train. The aim of this paper is to investigate the effect of the changes in the lengths of the high-temperature zone of the tunnel on the aerodynamic resistance of the train, clarifying the evolution mechanism of aerodynamic resistance of trains in tunnels with high ground temperatures. Firstly, moving model tests of trains passing through wall-heated tunnels were conducted to verify the reliability of the numerical method in this paper. Subsequently, based on the three-dimensional unsteady compressible RANS method and the standard k-ε two-equation turbulence model, the change laws of the average aerodynamic resistance under different high-temperature zone lengths were analyzed, and the influence of frictional resistance and pressure difference resistance on total resistance at different times was discussed. The results show that as the length of the high-temperature zone LH increases, the average aerodynamic resistance of a train running in a tunnel gradually decreases; when LH = 330 m, the aerodynamic resistance can be reduced by 5.7%. At the moment of maximum resistance, the total resistance, differential pressure resistance, and friction resistance all decrease gradually with the increase of LH and then remain basically unchanged. At the moment of the minimum value of resistance, with the increase of LH, the total resistance first increases and then slowly decreases; the differential pressure resistance first increases and then remains unchanged, while the friction resistance first remains unchanged and then gradually decreases, and the ratio of the differential pressure resistance to the total resistance gradually increases with the increase of LH. The results of this paper can provide guidance for scholars who need to investigate the mechanism of aerodynamic resistance change of trains in high geothermal environments, as well as provide a new way of thinking for resistance reduction in non-high geothermal tunnels.

Keywords: high-speed trains, aerodynamic resistance, high-ground temperature, tunnel

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Authors: Carlos Riascos, Peter Thomson

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Real-time hybrid simulation (RTHS) is a modern cyber-physical technique used for the experimental evaluation of complex systems, that treats the system components with predictable behavior as a numerical substructure and the components that are difficult to model as an experimental substructure. Therefore it is an attractive method for evaluation of the response of civil structures under earthquake, wind and anthropic loads. Another practical application of RTHS is the evaluation of control systems, as these devices are often nonlinear and their characterization is an important step in the design of controllers with the desired performance. In this paper, the response of three-story shear frame controlled by a tuned liquid column damper (TLCD) and subject to base excitation is considered. Both passive and semi-active control strategies were implemented and are compared. While the passive TLCD achieved a reduction of 50% in the acceleration response of the main structure in comparison with the structure without control, the semi-active TLCD achieved a reduction of 70%, and was robust to variations in the dynamic properties of the main structure. In addition, a RTHS was implemented with the main structure modeled as a linear, time-invariant (LTI) system through a state space representation and the TLCD, with both control strategies, was evaluated on a shake table that reproduced the displacement of the virtual structure. Current assessment measures for RTHS were used to quantify the performance with parameters such as generalized amplitude, equivalent time delay between the target and measured displacement of the shake table, and energy error using the measured force, and prove that the RTHS described in this paper is an accurate method for the experimental evaluation of structural control systems.

Keywords: structural control, hybrid simulation, tuned liquid column damper, semi-active sontrol strategy

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Authors: Oluwafunmibi Omotayo Fasanya, Augustine Kena Adjei

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To develop a machine learning model to predict diabetes (DM) prevalence in the U.S. population using demographic characteristics, physical indicators, and lifestyle habits, and to analyze how these factors contribute to the likelihood of diabetes. We analyzed data from 23,546 participants aged 20 and older, who were non-pregnant, from the 2009-2018 National Health and Nutrition Examination Survey (NHANES). The dataset included key demographic (age, sex, ethnicity), physical (BMI, leg length, total cholesterol [TCHOL], fasting plasma glucose), and lifestyle indicators (smoking habits). A weighted sample was used to account for NHANES survey design features such as stratification and clustering. A classification machine learning model was trained to predict diabetes status. The target variable was binary (diabetes or non-diabetes) based on fasting plasma glucose measurements. The following models were evaluated: Logistic Regression (baseline), Random Forest Classifier, Gradient Boosting Machine (GBM), Support Vector Machine (SVM). Model performance was assessed using accuracy, F1-score, AUC-ROC, and precision-recall metrics. Feature importance was analyzed using SHAP values to interpret the contributions of variables such as age, BMI, ethnicity, and smoking status. The Gradient Boosting Machine (GBM) model outperformed other classifiers with an AUC-ROC score of 0.85. Feature importance analysis revealed the following key predictors: Age: The most significant predictor, with diabetes prevalence increasing with age, peaking around the 60s for males and 70s for females. BMI: Higher BMI was strongly associated with a higher risk of diabetes. Ethnicity: Black participants had the highest predicted prevalence of diabetes (14.6%), followed by Mexican-Americans (13.5%) and Whites (10.6%). TCHOL: Diabetics had lower total cholesterol levels, particularly among White participants (mean decline of 23.6 mg/dL). Smoking: Smoking showed a slight increase in diabetes risk among Whites (0.2%) but had a limited effect in other ethnic groups. Using machine learning models, we identified key demographic, physical, and lifestyle predictors of diabetes in the U.S. population. The results confirm that diabetes prevalence varies significantly across age, BMI, and ethnic groups, with lifestyle factors such as smoking contributing differently by ethnicity. These findings provide a basis for more targeted public health interventions and resource allocation for diabetes management.

Keywords: diabetes, NHANES, random forest, gradient boosting machine, support vector machine

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Authors: Liu Bijin

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Floating breakwaters have several advantages, including being environmentally friendly, easy to construct, and cost-effective regardless of water depth. They have a broad range of applications in coastal engineering. However, they face significant challenges due to the unstable effect of wave dissipation, structural vulnerability, and high mooring system requirements. This paper investigates the wave dissipation performance of a floating breakwater structure. The structure consists of double cylinders, double vertical plates, and horizontal connecting plates. The investigation is carried out using physical model tests and numerical simulation methods based on STAR-CCM+. This paper discusses the impact of wave elements, relative vertical plate heights, and relative horizontal connecting plate widths on the wave dissipation performance of the double-cylinder, double-plate floating breakwater (DCDPFB). The study also analyses the changes in local vorticity and velocity fields around the DCDPFB to determine the optimal structural dimensions. The study found that the relative width of the horizontal connecting plate, the relative height of the vertical plate, and the size of the semi-cylinder are the key factors affecting the wave dissipation performance of the DCDPFB. The transmittance coefficient is minimally affected by the wave height and the depth of water entry. The local vortex and velocity field formed around the DCDPFB are important factors for dissipating wave energy. The test section of the DCDPFB, constructed according to the relative optimal structural dimensions, showed good wave dissipation performance during offshore prototype tests. The test section of DCDPFB, constructed with optimal structural dimensions, exhibits excellent wave dissipation performance in offshore prototype tests.

Keywords: floating breakwater, wave dissipation performance, transmittance coefficient, model test

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Authors: Mahan Qwamizadeh, Kun Zhou, Zuoqi Zhang, Yong Wei Zhang

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Typical load-bearing biological materials like bone, mineralized tendon and shell, are biocomposites made from both organic (collagen) and inorganic (biomineral) materials. This amazing class of materials with intrinsic internally designed hierarchical structures show superior mechanical properties with regard to their weak components from which they are formed. Extensive investigations concentrating on static loading conditions have been done to study the biological materials failure. However, most of the damage and failure mechanisms in load-bearing biological materials will occur whenever their structures are exposed to dynamic loading conditions. The main question needed to be answered here is: What is the relation between the layout and architecture of the load-bearing biological materials and their dynamic behavior? In this work, a staggered model has been developed based on the structure of natural materials at nanoscale and Finite Element Analysis (FEA) has been used to study the dynamic behavior of the structure of load-bearing biological materials to answer why the staggered arrangement has been selected by nature to make the nanocomposite structure of most of the biological materials. The results showed that the staggered structures will efficiently attenuate the stress wave rather than the layered structure. Furthermore, such staggered architecture is effectively in charge of utilizing the capacity of the biostructure to resist both normal and shear loads. In this work, the geometrical parameters of the model like the thickness and aspect ratio of the mineral inclusions selected from the typical range of the experimentally observed feature sizes and layout dimensions of the biological materials such as bone and mineralized tendon. Furthermore, the numerical results validated with existing theoretical solutions. Findings of the present work emphasize on the significant effects of dynamic behavior on the natural evolution of load-bearing biological materials and can help scientists to design bioinspired materials in the laboratories.

Keywords: load-bearing biological materials, nanostructure, staggered structure, stress wave decay

Procedia PDF Downloads 458

Authors: Suresh Salla

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In this paper, author explores for most sustainable last mile transport mode addressing present problems of traffic congestion, jams, pollution and travel stress. Development of energy-efficient sustainable integrated transport system(s) is/are must to make our cities more livable. Emphasis on autonomous, connected, electric, sharing system for effective utilization of systems (vehicles and public infrastructure) is on the rise. Many surface mobility innovations like PBS, Ride hailing, ride sharing, etc. are, although workable but if we analyze holistically, add to the already congested roads, difficult to ride in hostile weather, causes pollution and poses commuter stress. Sustainability of transportation is evaluated with respect to public adoption, average speed, energy consumption, and pollution. Why public prefer certain mode over others? How commute time plays a role in mode selection or shift? What are the factors play-ing role in energy consumption and pollution? Based on the study, it is clear that public prefer a transport mode which is exhaustive (i.e., less need for interchange – network is widespread) and intensive (i.e., less waiting time - vehicles are available at frequent intervals) and convenient with latest technologies. Average speed is dependent on stops, number of intersections, signals, clear route availability, etc. It is clear from Physics that higher the kerb weight of a vehicle; higher is the operational energy consumption. Higher kerb weight also demands heavier infrastructure. Pollution is dependent on source of energy, efficiency of vehicle, average speed. Mode can be made exhaustive when the unit infrastructure cost is less and can be offered intensively when the vehicle cost is less. Reliable and seamless integrated mobility till last ¼ mile (Five Minute Walk-FMW) is a must to encourage sustainable public transportation. Study shows that average speed and reliability of dedicated modes (like Metro, PRT, BRT, etc.) is high compared to road vehicles. Electric vehicles and more so battery-less or 3rd rail vehicles reduce pollution. One potential mode can be Cycleloop PRT, where commuter rides e-cycle in a dedicated path – elevated, at grade or underground. e-Bike with kerb weight per rider at 15 kg being 1/50th of car or 1/10th of other PRT systems makes it sustainable mode. Cycleloop tube will be light, sleek and scalable and can be modular erected, either on modified street lamp-posts or can be hanged/suspended between the two stations. Embarking and dis-embarking points or offline stations can be at an interval which suits FMW to mass public transit. In terms of convenience, guided e-Bike can be made self-balancing thus encouraging driverless on-demand vehicles. e-Bike equipped with smart electronics and drive controls can intelligently respond to field sensors and autonomously move reacting to Central Controller. Smart switching allows travel from origin to destination without interchange of cycles. DC Powered Batteryless e-cycle with voluntary manual pedaling makes it sustainable and provides health benefits. Tandem e-bike, smart switching and Platoon operations algorithm options provide superior through-put of the Cycleloop. Thus Cycleloop PRT will be exhaustive, intensive, convenient, reliable, speedy, sustainable, safe, pollution-free and healthy alternative mode for last mile connectivity in cities.

Keywords: cycleloop PRT, five-minute walk, lean modular infrastructure, self-balanced intelligent e-cycle

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Authors: Lanlan Xiao, Yang Liu, Shuo Chen, Bingmei Fu

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Most cancer-related deaths are due to metastasis. Metastasis is a complex, multistep processes including the detachment of cancer cells from the primary tumor and the migration to distant targeted organs through blood and/or lymphatic circulations. During hematogenous metastasis, the emigration of tumor cells from the blood stream through the vascular wall into the tissue involves arrest in the microvasculature, adhesion to the endothelial cells forming the microvessel wall and transmigration to the tissue through the endothelial barrier termed as extravasation. The narrow slit between endothelial cells that line the microvessel wall is the principal pathway for tumor cell extravasation to the surrounding tissue. To understand this crucial step for tumor hematogenous metastasis, we used Dissipative Particle Dynamics method to investigate an individual cell passing through a narrow slit numerically. The cell membrane was simulated by a spring-based network model which can separate the internal cytoplasm and surrounding fluid. The effects of the cell elasticity, cell shape and cell surface area increase, and slit size on the cell transmigration through the slit were investigated. Under a fixed driven force, the cell with higher elasticity can be elongated more and pass faster through the slit. When the slit width decreases to 2/3 of the cell diameter, the spherical cell becomes jammed despite reducing its elasticity modulus by 10 times. However, transforming the cell from a spherical to ellipsoidal shape and increasing the cell surface area only by 3% can enable the cell to pass the narrow slit. Therefore the cell shape and surface area increase play a more important role than the cell elasticity in cell passing through the narrow slit. In addition, the simulation results indicate that the cell migration velocity decreases during entry but increases during exit of the slit, which is qualitatively in agreement with the experimental observation.

Keywords: dissipative particle dynamics, deformability, surface area increase, cell migration

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Authors: Vineeth G. Kuriakose, Joseph C. Chen, Ye Li

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The research follows a systematic approach to optimize the parameters for parts machined by turning and milling processes. The quality characteristic chosen is surface roughness since the surface finish plays an important role for parts that require surface contact. A tapered cylindrical surface is designed as a test specimen for the research. The material chosen for machining is aluminum alloy 6061 due to its wide variety of industrial and engineering applications. HAAS VF-2 TR computer numerical control (CNC) vertical machining center is used for milling and HAAS ST-20 CNC machine is used for turning in this research. Taguchi analysis is used to optimize the surface roughness of the machined parts. The L₉ Orthogonal Array is designed for four controllable factors with three different levels each, resulting in 18 experimental runs. Signal to Noise (S/N) Ratio is calculated for achieving the specific target value of 75 ± 15 µin. The controllable parameters chosen for turning process are feed rate, depth of cut, coolant flow and finish cut and for milling process are feed rate, spindle speed, step over and coolant flow. The uncontrollable factors are tool geometry for turning process and tool material for milling process. Hypothesis testing is conducted to study the significance of different uncontrollable factors on the surface roughnesses. The optimal parameter settings were identified from the Taguchi analysis and the process capability C_p and the process capability index C_pk were improved from 1.76 and 0.02 to 3.70 and 2.10 respectively for turning process and from 0.87 and 0.19 to 3.85 and 2.70 respectively for the milling process. The surface roughnesses were improved from 60.17 µin to 68.50 µin, reducing the defect rate from 52.39% to 0% for the turning process and from 93.18 µin to 79.49 µin, reducing the defect rate from 71.23% to 0% for the milling process. The purpose of this study is to efficiently utilize the Taguchi design analysis to improve the surface roughness.

Keywords: surface roughness, Taguchi parameter design, CNC turning, CNC milling

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Authors: Vincent Schlageter, Maroua Mestiri, Florian Denzinger, Hugo Raetzo, Michel Demierre

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Precise landslide monitoring with differential global navigation satellite system (GNSS) is well known, but technical or economic reasons limit its application by geotechnical companies. This study demonstrates the reliability and the usefulness of Geomon (Infrasurvey Sàrl, Switzerland), a stand-alone and cost-effective rover network. The system permits deploying up to 15 rovers, plus one reference station for differential GNSS. A dedicated radio communication links all the modules to a base station, where an embedded computer automatically provides all the relative positions (L1 phase, open-source RTKLib software) and populates an Internet server. Each measure also contains information from an internal inclinometer, battery level, and position quality indices. Contrary to standard GNSS survey systems, which suffer from a limited number of beacons that must be placed in areas with good GSM signal, Geomon offers greater flexibility and permits a real overview of the whole landslide with good spatial resolution. Each module is powered with solar panels, ensuring autonomous long-term recordings. In this study, we have tested the system on several sites in the Swiss mountains, setting up to 7 rovers per site, for an 18 month-long survey. The aim was to assess the robustness and the accuracy of the system in different environmental conditions. In one case, we ran forced blind tests (vertical movements of a given amplitude) and compared various session parameters (duration from 10 to 90 minutes). Then the other cases were a survey of real landslides sites using fixed optimized parameters. Sub centimetric-accuracy with few outliers was obtained using the best parameters (session duration of 60 minutes, baseline 1 km or less), with the noise level on the horizontal component half that of the vertical one. The performance (percent of aborting solutions, outliers) was reduced with sessions shorter than 30 minutes. The environment also had a strong influence on the percent of aborting solutions (ambiguity search problem), due to multiple reflections or satellites obstructed by trees and mountains. The length of the baseline (distance reference-rover, single baseline processing) reduced the accuracy above 1 km but had no significant effect below this limit. In critical weather conditions, the system’s robustness was limited: snow, avalanche, and frost-covered some rovers, including the antenna and vertically oriented solar panels, leading to data interruption; and strong wind damaged a reference station. The possibility of changing the sessions’ parameters remotely was very useful. In conclusion, the rover network tested provided the foreseen sub-centimetric-accuracy while providing a dense spatial resolution landslide survey. The ease of implementation and the fully automatic long-term survey were timesaving. Performance strongly depends on surrounding conditions, but short pre-measures should allow moving a rover to a better final placement. The system offers a promising hazard mitigation technique. Improvements could include data post-processing for alerts and automatic modification of the duration and numbers of sessions based on battery level and rover displacement velocity.

Keywords: GNSS, GSM, landslide, long-term, network, solar, spatial resolution, sub-centimeter.

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Authors: Al Ameen H., Rakesh P.

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To improve the combustion efficiency of fuels and to reduce the emissions of pollutants as well as to improve heat transfer characteristics of fuels, both non-metallic and metallic nanoparticles can be added into it. By varying the concentration and size of nano particles added into the fuels, behaviour of droplet combustion and hence heat generated can be altered. In case of solid or liquid fuels, surface area of the fuel in contact with oxidizer(gaseous) is small because of higher density compared to gases. If the surface area of fuel exposed to the oxidizer is very small, then the combustion will not occur, because the combustion rate is proportional to the surface area of fuel droplet. To avoid such instance there is a way to increase the exposed surface area. To increase the specific surface area available for reaction, the particle size can be reduced. If the additives are solid then by reducing the particles size the specific surface area of liquid fuel can be increased. For the liquid fuels the exposed surface area available for combustion can be increased by suspending nanoparticles. Addition of non-metallic and metallic nanoparticles in fuels improves its combustion efficiency by enhancing the thermo-physical properties. The burn rate constants and temperatures of Kerosene-Oxygen combustion for fuel droplet sizes of 50μm, 75μm, 100μm and 125μm under varying concentrations of 25%, 50%, 75% and 100% are studied numerically and its characteristic velocities are determined. Later the burn rate constants of fuel with concentrations of 0.5%, 1.0% and 2.0% by weight of aluminium nanoparticles are added. The spray combustion characteristics of such nano-fuel has improved the combustion temperature by the addition of aluminium nanoparticles. Thus, aluminium nanoparticles have improved burn rate and characteristic velocity of Kerosene-Oxygen combustion. An increase of 40% in characteristic velocity is observed.

Keywords: burn rate, characteristic velocity, combustion, thermo-physical properties

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Authors: Adefarati Oloruntoba, Yongmin Zhang, Hongliang Xiao

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An annular fluidized bed (AFB) is gaining extensive application in the process industry due to its efficient gas-solids contacting. But a direct evaluation of its reaction performance is still lacking. In this paper, comparative 3D Euler–Lagrange multiphase-particle-in-cell (MP-PIC) computations are performed to assess the reaction performance of AFB relative to a bubbling fluidized bed (BFB) in an FCC regeneration process. By using the energy-minimization multi-scale (EMMS) drag model with a suitable heterogeneity index, the MP-PIC simulation predicts the typical fountain region in AFB and solids holdup of BFB, which is consistent with an experiment. Coke combustion rate, flue gas and temperature profile are utilized as the performance indicators, while related bed hydrodynamics are explored to account for the different performance under varying superficial gas velocities (0.5 m/s, 0.6 m/s, and 0.7 m/s). Simulation results indicate that the burning rates of coke and its species are relatively the same in both beds, albeit marginal increase in BFB. Similarly, the shape and evolution time of flue gas (CO, CO₂, H₂O and O₂) curves are indistinguishable but match the coke combustion rates. However, AFB has high proclivity to high temperature-gradient as higher gas and solids temperatures are predicted in the freeboard. Moreover, for both beds, the effect of superficial gas velocity is only conspicuous on the temperature but negligible on combustion efficiency and effluent gas emissions due to constant gas volumetric flow rate and bed loading criteria. Cross-flow of solids from the annulus to the spout region as well as the high primary gas in the AFB directly assume the underlying mechanisms for its unique gas-solids hydrodynamics (pressure, solids holdup, velocity, mass flux) and local spatial homogeneity, which in turn influence the reactor performance. Overall, the study portrays AFB as a cheap alternative reactor to BFB for catalyst regeneration.

Keywords: annular fluidized bed, bubbling fluidized bed, coke combustion, flue gas, fountaining, CFD, MP-PIC, hydrodynamics, FCC regeneration

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Authors: Swee Yang Low, Dong Eon Kim, Canh Tien Trinh Nguyen, Yixiong Cai, Shie-Yui Liong

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Nee Soon swamp forest is one of the last vestiges of tropical wetland in Singapore. Understanding the hydrological regime of the swamp forest and implications for water quality is critical to guide stakeholders in implementing effective measures to preserve the wetland against anthropogenic impacts. In particular, although current field measurement data do not indicate a concern with organic pollution, reviewing the ways in which the wetland responds to elevated organic waste influx (and the corresponding impact on dissolved oxygen, DO) can help identify potential hotspots, and the impact on the outflow from the catchment which drains into downstream controlled watercourses. An integrated water quality model is therefore developed in this study to investigate spatial and temporal concentrations of DO levels and organic pollution (as quantified by biochemical oxygen demand, BOD) within the catchment’s river network under hypothetical, projected scenarios of spiked upstream inflow. The model was developed using MIKE HYDRO for modelling the study domain, as well as the MIKE ECO Lab numerical laboratory for characterising water quality processes. Model parameters are calibrated against time series of observed discharges at three measurement stations along the river network. Over a simulation period of April 2014 to December 2015, the calibrated model predicted that a continuous spiked inflow of 400 mg/l BOD will elevate downstream concentrations at the catchment outlet to an average of 12 mg/l, from an assumed nominal baseline BOD of 1 mg/l. Levels of DO were decreased from an initial 5 mg/l to 0.4 mg/l. Though a scenario of spiked organic influx at the swamp forest’s undeveloped upstream sub-catchments is currently unlikely to occur, the outcomes nevertheless will be beneficial for future planning studies in understanding how the water quality of the catchment will be impacted should urban redevelopment works be considered around the swamp forest.

Keywords: hydrology, modeling, water quality, wetland

Procedia PDF Downloads 141

Authors: Roya Yumul, Ofelia Loani Elvir-Lazo, Sevan Komshian, Ruby Wang, Jun Tang

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BACKGROUND: An objective means for monitoring the anti-nociceptive effects of perioperative medications has long been desired as a way to provide anesthesiologists information regarding a patient’s level of antinociception and preclude any untoward autonomic responses and reflexive muscular movements from painful stimuli intraoperatively. To this end, electroencephalogram (EEG) based tools including BIS and qCON were designed to provide information about the depth of sedation while qNOX was produced to inform on the degree of antinociception. The goal of this study was to compare the reliability of qCON/qNOX to BIS as specific indicators of response to nociceptive stimulation. METHODS: Sixty-two patients undergoing general anesthesia with LMA were included in this study. Institutional Review Board (IRB) approval was obtained, and informed consent was acquired prior to patient enrollment. Inclusion criteria included American Society of Anesthesiologists (ASA) class I-III, 18 to 80 years of age, and either gender. Exclusion criteria included the inability to consent. Withdrawal criteria included conversion to the endotracheal tube and EEG malfunction. BIS and qCON/qNOX electrodes were simultaneously placed on all patients prior to induction of anesthesia and were monitored throughout the case, along with other perioperative data, including patient response to noxious stimuli. All intraoperative decisions were made by the primary anesthesiologist without influence from qCON/qNOX. Student’s t-distribution, prediction probability (PK), and ANOVA were used to statistically compare the relative ability to detect nociceptive stimuli for each index. Twenty patients were included for the preliminary analysis. RESULTS: A comparison of overall intraoperative BIS, qCON and qNOX indices demonstrated no significant difference between the three measures (N=62, p> 0.05). Meanwhile, index values for qNOX (62±18) were significantly higher than those for BIS (46±14) and qCON (54±19) immediately preceding patient responses to nociceptive stimulation in a preliminary analysis (N=20, * p= 0.0408). Notably, certain hemodynamic measurements demonstrated a significant increase in response to painful stimuli (MAP increased from 74 ±13 mm Hg at baseline to 84 ± 18 mm Hg during noxious stimuli [p= 0.032] and HR from 76 ± 12 BPM at baseline to 80 ± 13 BPM during noxious stimuli [p=0.078] respectively). CONCLUSION: In this observational study, BIS and qCON/qNOX provided comparable information on patients’ level of sedation throughout the course of an anesthetic. Meanwhile, increases in qNOX values demonstrated a superior correlation to an imminent response to stimulation relative to all other indices

Keywords: antinociception, BIS, general anesthesia, LMA, qCON/qNOX

Procedia PDF Downloads 137

Authors: Marlin Mubarak

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Background: Endometriosis is a complex disabling disease affecting young females in the reproductive period mainly. The aim of this project is to generate a diagnostic model to predict severity and stage of endometriosis prior to Laparoscopic surgery. This will help to improve the pre-operative diagnostic accuracy of stage 3 & 4 endometriosis and as a result, refer relevant women to a specialist centre for complex Laparoscopic surgery. The model is based on the British Society of Gynaecological Endoscopy (BSGE) pain questionnaire, clinical examination and ultrasound scan. Design: This is a prospective, observational, study, in which women completed the BSGE pain questionnaire, a BSGE requirement. Also, as part of the routine preoperative assessment patient had a routine ultrasound scan and when recto-vaginal and deep infiltrating endometriosis was suspected an MRI was performed. Setting: Luton & Dunstable University Hospital. Patients: Symptomatic women (n = 56) scheduled for laparoscopy due to pelvic pain. The age ranged between 17 – 52 years of age (mean 33.8 years, SD 8.7 years). Interventions: None outside the recognised and established endometriosis centre protocol set up by BSGE. Main Outcome Measure(s): Sensitivity and specificity of endometriosis diagnosis predicted by symptoms based on BSGE pain questionnaire, clinical examinations and imaging. Findings: The prevalence of diagnosed endometriosis was calculated to be 76.8% and the prevalence of advanced stage was 55.4%. Deep infiltrating endometriosis in various locations was diagnosed in 32/56 women (57.1%) and some had DIE involving several locations. Logistic regression analysis was performed on 36 clinical variables to create a simple clinical prediction model. After creating the scoring system using variables with P < 0.05, the model was applied to the whole dataset. The sensitivity was 83.87% and specificity 96%. The positive likelihood ratio was 20.97 and the negative likelihood ratio was 0.17, indicating that the model has a good predictive value and could be useful in predicting advanced stage endometriosis. Conclusions: This is a hypothesis-generating project with one operator, but future proposed research would provide validation of the model and establish its usefulness in the general setting. Predictive tools based on such model could help organise the appropriate investigation in clinical practice, reduce risks associated with surgery and improve outcome. It could be of value for future research to standardise the assessment of women presenting with pelvic pain. The model needs further testing in a general setting to assess if the initial results are reproducible.

Keywords: deep endometriosis, endometriosis, minimally invasive, MRI, ultrasound.

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Authors: Ahmed El Naggar, Homyan Saleh

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Dams are crucial for flood control, water supply, and the creation of hydroelectric power. Every dam has a water conveyance system, such as a spillway, providing the safe discharge of catastrophic floods when necessary. Spillway design has historically been investigated in laboratory research owing to the absence of suitable full-scale flow monitoring equipment and safety problems. Prototype measurements of aerated flows are urgently needed to quantify projected scale effects and provide missing validation data for design guidelines and numerical simulations. In this work, an image-based investigation of free-surface flows on a tiered spillway was undertaken at the laboratory (fixed camera installation) and prototype size (drone video) (drone footage) (drone footage). The drone videos were generated using data from citizen science. Analyses permitted the measurement of the free-surface aeration inception point, air-water surface velocities, fluctuations, and residual energy at the chute's downstream end from a remote site. The prototype observations offered full-scale proof of concept, while laboratory results were efficiently confirmed against invasive phase-detection probe data. This paper stresses the efficacy of image-based analyses at prototype spillways. It highlights how citizen science data may enable academics better understand real-world air-water flow dynamics and offers a framework for a small collection of long-missing prototype data.

Keywords: remote sensing, aerated flows, large dams, proof of concept, dam spillways, air-water flows, prototype operation, remote sensing, inception point, optical flow, turbulence, residual energy

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Authors: Gabriela Silva-Maceda, Silvia Romero-Contreras

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Spanish is a more transparent language than English given that it has more direct correspondences between sounds and letters. It has become important to understand how decoding and linguistic comprehension contribute to reading comprehension in the framework of the widely known Simple View Model. This study aimed to identify the level of prediction by these two components in a sample of 1st to 4th grade children attending two schools in central Mexico (one public and one private). Within each school, ten children were randomly selected in each grade level, and their parents were asked about reading habits and socioeconomic information. In total, 79 children completed three standardized tests measuring decoding (pseudo-word reading), linguistic comprehension (understanding of paragraphs) and reading comprehension using subtests from the Clinical Evaluation of Language Fundamentals-Spanish, Fourth Edition, and the Test de Lectura y Escritura en Español (LEE). The data were analyzed using hierarchical regression, with decoding as a first step and linguistic comprehension as a second step. Results showed that decoding accounted for 19.2% of the variance in reading comprehension, while linguistic comprehension accounted for an additional 10%, adding up to 29.2% of variance explained: F (2, 75)= 15.45, p <.001. Socioeconomic status derived from parental questionnaires showed a statistically significant association with the type of school attended, X2 (3, N= 79) = 14.33, p =.002. Nonetheless when analyzing the Simple View components, only decoding differences were statistically significant (t = -6.92, df = 76.81, p < .001, two-tailed); reading comprehension differences were also significant (t = -3.44, df = 76, p = .001, two-tailed). When socioeconomic status was included in the model, it predicted a 5.9% unique variance, even when already accounting for Simple View components, adding to a 35.1% total variance explained. This three-predictor model was also significant: F (3, 72)= 12.99, p <.001. In addition, socioeconomic status was significantly correlated with the amount of non-textbook books parents reported to have at home for both adults (rho = .61, p<.001) and children (rho= .47, p<.001). Results converge with a large body of literature finding socioeconomic differences in reading comprehension; in addition this study suggests that these differences were also present in decoding skills. Although linguistic comprehension differences between schools were expected, it is argued that the test used to collect this variable was not sensitive to linguistic differences, since it came from a test to diagnose clinical language disabilities. Even with this caveat, results show that the components of the Simple View Model can predict less than a third of the variance in reading comprehension in Spanish. However, the results also suggest that a fuller model of reading comprehension is obtained when considering the family’s socioeconomic status, given the potential differences shown by the socioeconomic status association with books at home, factors that are particularly important in countries where inequality gaps are relatively large.

Keywords: decoding, linguistic comprehension, reading comprehension, simple view model, socioeconomic status, Spanish

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Authors: Stephanie Watson, Fabrice Stephenson, Conrad Pilditch, Carolyn Lundquist

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Spatial data detailing both the distribution and density of functionally important marine species are needed to inform management decisions. Species distribution models (SDMs) have proven helpful in this regard; however, models often focus only on species occurrences derived from spatially expansive datasets and lack the resolution and detail required to inform regional management decisions. Boosted regression trees (BRT) were used to produce high-resolution SDMs (250 m) at two spatial scales predicting probability of occurrence, abundance (count per sample unit), density (count per km2) and uncertainty for seven coastal seafloor taxa that vary in habitat usage and distribution to examine prediction differences and implications for coastal management. We investigated if small scale regionally focussed models (82,000 km2) can provide improved predictions compared to data-rich national scale models (4.2 million km2). We explored the variability in predictions across model type (occurrence vs abundance) and model scale to determine if specific taxa models or model types are more robust to geographical variability. National scale occurrence models correlated well with broad-scale environmental predictors, resulting in higher AUC (Area under the receiver operating curve) and deviance explained scores; however, they tended to overpredict in the coastal environment and lacked spatially differentiated detail for some taxa. Regional models had lower overall performance, but for some taxa, spatial predictions were more differentiated at a localised ecological scale. National density models were often spatially refined and highlighted areas of ecological relevance producing more useful outputs than regional-scale models. The utility of a two-scale approach aids the selection of the most optimal combination of models to create a spatially informative density model, as results contrasted for specific taxa between model type and scale. However, it is vital that robust predictions of occurrence and abundance are generated as inputs for the combined density model as areas that do not spatially align between models can be discarded. This study demonstrates the variability in SDM outputs created over different geographical scales and highlights implications and opportunities for managers utilising these tools for regional conservation, particularly in data-limited environments.

Keywords: Benthic ecology, spatial modelling, multi-scalar modelling, marine conservation.

Procedia PDF Downloads 79