Search results for: computational accuracy

Authors: Antonio A. M. Laudani, Igor O. Golosnoy, Ole T. Thomsen

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Rotor blades of large, modern wind turbines are highly susceptible to downward lightning strikes, as well as to triggering upward lightning; consequently, it is necessary to equip them with an effective lightning protection system (LPS) in order to avoid any damage. The performance of existing LPSs is affected by carbon fibre reinforced polymer (CFRP) structures, which lead to lightning-induced damage in the blades, e.g. via electrical sparks. A solution to prevent internal arcing would be to electrically bond the LPS and the composite structures such that to obtain the same electric potential. Nevertheless, elevated temperatures are achieved at the joint interfaces because of high contact resistance, which melts and vaporises some of the epoxy resin matrix around the bonding. The produced high-pressure gasses open up the bonding and can ignite thermal sparks. The objective of this paper is to predict the current density distribution and the temperature field in the adhesive joint cross-section, in order to check whether the resin pyrolysis temperature is achieved and any damage is expected. The finite element method has been employed to solve both the current and heat transfer problems, which are considered weakly coupled. The mathematical model for electric current includes Maxwell-Ampere equation for induced electric field solved together with current conservation, while the thermal field is found from heat diffusion equation. In this way, the current sub-model calculates Joule heat release for a chosen bonding configuration, whereas the thermal analysis allows to determining threshold values of voltage and current density not to be exceeded in order to maintain the temperature across the joint below the pyrolysis temperature, therefore preventing the occurrence of outgassing. In addition, it provides an indication of the minimal number of bonding points. It is worth to mention that the numerical procedures presented in this study can be tailored and applied to any type of joints other than adhesive ones for wind turbine blades. For instance, they can be applied for lightning protection of aerospace bolted joints. Furthermore, they can even be customized to predict the electromagnetic response under lightning strikes of other wind turbine systems, such as nacelle and hub components.

Keywords: carbon fibre reinforced polymer, equipotential bonding, finite element method, FEM, lightning protection system, LPS, wind turbine blades

Procedia PDF Downloads 164

Authors: Abdallah Touaibia, Nachida Kasbadji Merzouk, Mustapha Merzouk, Ryma Belarbi

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accuracy assessment and a better understand of the wind resource distribution are the most important tasks for decision making before installing wind energy operating systems in a given region, there where our interest come to the Algerian coastline and its Mediterranean sea area. Despite its large coastline overlooking the border of Mediterranean Sea, there is still no strategy encouraging the development of offshore wind farms in Algerian waters. The present work aims to estimate the offshore wind fields for the Algerian Mediterranean Sea based on wind data measurements ranging from 1995 to 2018 provided of 24 years of measurement by seven observation stations focusing on three coastline cities in Algeria under a different measurement time step recorded from 30 min, 60 min, and 180 min variate from one to each other, two stations in Spain, two other ones in Italy and three in the coast of Algeria from the east Annaba, at the center Algiers, and to Oran taken place at the west of it. The idea behind consists to have multiple measurement points that helping to characterize this area in terms of wind potential by the use of interpolation method of their average wind speed values between these available data to achieve the approximate values of others locations where aren’t any available measurement because of the difficulties against the implementation of masts within the deep depth water. This study is organized as follow: first, a brief description of the studied area and its climatic characteristics were done. After that, the statistical properties of the recorded data were checked by evaluating wind histograms, direction roses, and average speeds using MatLab programs. Finally, ArcGIS and MapInfo soft-wares were used to establish offshore wind maps for better understanding the wind resource distribution, as well as to identify windy sites for wind farm installation and power management. The study pointed out that Cap Carbonara is the windiest site with an average wind speed of 7.26 m/s at 10 m, inducing a power density of 902 W/m², then the site of Cap Caccia with 4.88 m/s inducing a power density of 282 W/m². The average wind speed of 4.83 m/s is occurred for the site of Oran, inducing a power density of 230 W/m². The results indicated also that the dominant wind direction where the frequencies are highest for the site of Cap Carbonara is the West with 34%, an average wind speed of 9.49 m/s, and a power density of 1722 W/m². Then comes the site of Cap Caccia, where the prevailing wind direction is the North-west, about 20% and 5.82 m/s occurring a power density of 452 W/m². The site of Oran comes in third place with the North dominant direction with 32% inducing an average wind speed of 4.59 m/s and power density of 189 W/m². It also shown that the proposed method is either crucial in understanding wind resource distribution for revealing windy sites over a large area and more effective for wind turbines micro-siting.

Keywords: wind ressources, mediterranean sea, offshore, arcGIS, mapInfo, wind maps, wind farms

Procedia PDF Downloads 147

Authors: Mustapha Rilwan Adewale, Salau Ayobami Muhammed

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This study focuses on the heat transfer analysis of magneto-hydrodynamics (MHD) squeezing flow between parallel disks, considering a viscous incompressible fluid. The upper disk exhibits both upward and downward motion, while the lower disk remains stationary but permeable. By employing similarity transformations, a system of nonlinear ordinary differential equations is derived to describe the flow behavior. To solve this system, a numerical approach, namely the Chebyshev collocation method, is utilized. The study investigates the influence of flow parameters and compares the obtained results with existing literature. The significance of this research lies in understanding the heat transfer characteristics of MHD squeezing flow, which has practical implications in various engineering and industrial applications. By employing the similarity transformations, the complex governing equations are simplified into a system of nonlinear ordinary differential equations, facilitating the analysis of the flow behavior. To obtain numerical solutions for the system, the Chebyshev collocation method is implemented. This approach provides accurate approximations for the nonlinear equations, enabling efficient computations of the heat transfer properties. The obtained results are compared with existing literature, establishing the validity and consistency of the numerical approach. The study's major findings shed light on the influence of flow parameters on the heat transfer characteristics of the squeezing flow. The analysis reveals the impact of parameters such as magnetic field strength, disk motion amplitude, fluid viscosity on the heat transfer rate between the disks, the squeeze number(S), suction/injection parameter(A), Hartman number(M), Prandtl number(Pr), modified Eckert number(Ec), and the dimensionless length(δ). These findings contribute to a comprehensive understanding of the system's behavior and provide insights for optimizing heat transfer processes in similar configurations. In conclusion, this study presents a thorough heat transfer analysis of magneto-hydrodynamics squeezing flow between parallel disks. The numerical solutions obtained through the Chebyshev collocation method demonstrate the feasibility and accuracy of the approach. The investigation of flow parameters highlights their influence on heat transfer, contributing to the existing knowledge in this field. The agreement of the results with previous literature further strengthens the reliability of the findings. These outcomes have practical implications for engineering applications and pave the way for further research in related areas.

Keywords: squeezing flow, magneto-hydro-dynamics (MHD), chebyshev collocation method(CCA), parallel manifolds, finite difference method (FDM)

Procedia PDF Downloads 76

Authors: Apeng Dong, Shu Li, Wenguo Zhu, Ming Qi, Qiuyi Xu

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The primary objective of this paper is to focus on the elasticity properties of three-dimensional full five directional (3Df5d) braided composite. A large body of research has been focused on the 3D four directional (4d) and 3D five directional (5d) structure but not much research on the 3Df5d material. Generally, the influence of the yarn shape on mechanical properties of braided materials tends to be ignored, which makes results too ideal. Besides, with the improvement of the computational ability, people are accustomed to using computers to predict the material parameters, which fails to give an explicit and concise result facilitating production and application. Based on the traditional mechanics, this paper firstly deduced the functional relation between elasticity properties and braiding parameters. In addition, considering the actual shape of yarns after consolidation, the longitudinal modulus is modified and defined practically. Firstly, the analytic model is established based on the certain assumptions for the sake of clarity, this paper assumes that: A: the cross section of axial yarns is square; B: The cross section of braiding yarns is hexagonal; C: the characters of braiding yarns and axial yarns are the same; D: The angle between the structure boundary and the projection of braiding yarns in transverse plane is 45°; E: The filling factor ε of composite yarns is π/4; F: The deformation of unit cell is under constant strain condition. Then, the functional relation between material constants and braiding parameters is systematically deduced aimed at the yarn deformation mode. Finally, considering the actual shape of axial yarns after consolidation, the concept of technology factor is proposed and the longitudinal modulus of the material is modified based on the energy theory. In this paper, the analytic solution of material parameters is given for the first time, which provides a good reference for further research and application for 3Df5d materials. Although the analysis model is established based on certain assumptions, the analysis method is also applicable for other braided structures. Meanwhile, it is crucial that the cross section shape and straightness of axial yarns play dominant roles in the longitudinal elastic property. So in the braiding and solidifying process, the stability of the axial yarns should be guaranteed to increase the technology factor to reduce the dispersion of material parameters. Overall, the elastic properties of this materials are closely related to the braiding parameters and can be strongly designable, and although the longitudinal modulus of the material is greatly influenced by the technology factors, it can be defined to certain extent.

Keywords: analytic solution, braided composites, elasticity properties, technology factor

Procedia PDF Downloads 237

Authors: Elaheh Talebi, Fariba Yavari, Lucy Philip, Lesley Town

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The mining industry generates vast amounts of data, necessitating robust data management systems and advanced analytics tools to achieve better decision-making processes in the development of mining production and maintaining safety. This paper highlights the advantages of Power BI, a powerful intelligence tool, over traditional Excel-based approaches for effectively managing and harnessing mining data. Power BI enables professionals to connect and integrate multiple data sources, ensuring real-time access to up-to-date information. Its interactive visualizations and dashboards offer an intuitive interface for exploring and analyzing geotechnical data. Advanced analytics is a collection of data analysis techniques to improve decision-making. Leveraging some of the most complex techniques in data science, advanced analytics is used to do everything from detecting data errors and ensuring data accuracy to directing the development of future project phases. However, while Power BI is a robust tool, specific visualizations required by geotechnical engineers may have limitations. This paper studies the capability to use Python or R programming within the Power BI dashboard to enable advanced analytics, additional functionalities, and customized visualizations. This dashboard provides comprehensive tools for analyzing and visualizing key geotechnical data metrics, including spatial representation on maps, field and lab test results, and subsurface rock and soil characteristics. Advanced visualizations like borehole logs and Stereonet were implemented using Python programming within the Power BI dashboard, enhancing the understanding and communication of geotechnical information. Moreover, the dashboard's flexibility allows for the incorporation of additional data and visualizations based on the project scope and available data, such as pit design, rock fall analyses, rock mass characterization, and drone data. This further enhances the dashboard's usefulness in future projects, including operation, development, closure, and rehabilitation phases. Additionally, this helps in minimizing the necessity of utilizing multiple software programs in projects. This geotechnical dashboard in Power BI serves as a user-friendly solution for analyzing, visualizing, and communicating both new and historical geotechnical data, aiding in informed decision-making and efficient project management throughout various project stages. Its ability to generate dynamic reports and share them with clients in a collaborative manner further enhances decision-making processes and facilitates effective communication within geotechnical projects in the mining industry.

Keywords: geotechnical data analysis, power BI, visualization, decision-making, mining industry

Procedia PDF Downloads 92

Authors: Dmitry A. Koptelov, Leonid M. Mestetskiy

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Voronoi Diagram (VD) of finite set of disjoint simple polygons, called sites, is a partition of plane into loci (for each site at the locus) – regions, consisting of points that are closer to a given site than to all other. Set of polygons is a universal model for many applications in engineering, geoinformatics, design, computer vision, and graphics. VD of polygons construction usually done with a reduction to task of constructing VD of segments, for which there are effective O(n log n) algorithms for n segments. Preprocessing – constructing segments from polygons’ sides, and postprocessing – polygon’s loci construction by merging the loci of the sides of each polygon are also included in reduction. This approach doesn’t take into account two specific properties of the resulting segment sites. Firstly, all this segments are connected in pairs in the vertices of the polygons. Secondly, on the one side of each segment lies the interior of the polygon. The polygon is obviously included in its locus. Using this properties in the algorithm for VD construction is a resource to reduce computations. The article proposes an algorithm for the direct construction of VD of polygonal sites. Algorithm is based on sweepline paradigm, allowing to effectively take into account these properties. The solution is performed based on reduction. Preprocessing is the constructing of set of sites from vertices and edges of polygons. Each site has an orientation such that the interior of the polygon lies to the left of it. Proposed algorithm constructs VD for set of oriented sites with sweepline paradigm. Postprocessing is a selecting of edges of this VD formed by the centers of empty circles touching different polygons. Improving the efficiency of the proposed sweepline algorithm in comparison with the general Fortune algorithm is achieved due to the following fundamental solutions: 1. Algorithm constructs only such VD edges, which are on the outside of polygons. Concept of oriented sites allowed to avoid construction of VD edges located inside the polygons. 2. The list of events in sweepline algorithm has a special property: the majority of events are connected with “medium” polygon vertices, where one incident polygon side lies behind the sweepline and the other in front of it. The proposed algorithm processes such events in constant time and not in logarithmic time, as in the general Fortune algorithm. The proposed algorithm is fully implemented and tested on a large number of examples. The high reliability and efficiency of the algorithm is also confirmed by computational experiments with complex sets of several thousand polygons. It should be noted that, despite the considerable time that has passed since the publication of Fortune's algorithm in 1986, a full-scale implementation of this algorithm for an arbitrary set of segment sites has not been made. The proposed algorithm fills this gap for an important special case - a set of sites formed by polygons.

Keywords: voronoi diagram, sweepline, polygon sites, fortunes' algorithm, segment sites

Procedia PDF Downloads 177

Authors: Marcos Silva Tavares, Jamile Raquel Regazzo, Edson José de Souza Sardinha, Murilo Mesquita Baesso

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Common beans are a widely cultivated and consumed legume globally, serving as a staple food for humans, especially in developing countries, due to their nutritional characteristics. Nitrogen (N) is the most limiting nutrient for productivity, and foliar analysis is crucial to ensure balanced nitrogen fertilization. Excessive N applications can cause, either isolated or cumulatively, soil and water contamination, plant toxicity, and increase their susceptibility to diseases and pests. However, the quantification of N using conventional methods is time-consuming and costly, demanding new technologies to optimize the adequate supply of N to plants. Thus, it becomes necessary to establish constant monitoring of the foliar content of this macronutrient in plants, mainly at the V4 stage, aiming at precision management of nitrogen fertilization. In this work, the objective was to evaluate the performance of a deep learning model, Resnet-50, in the classification of foliar nitrogen in common beans using RGB images. The BRS Estilo cultivar was sown in a greenhouse in a completely randomized design with four nitrogen doses (T1 = 0 kg N ha-1, T2 = 25 kg N ha-1, T3 = 75 kg N ha-1, and T4 = 100 kg N ha-1) and 12 replications. Pots with 5L capacity were used with a substrate composed of 43% soil (Neossolo Quartzarênico), 28.5% crushed sugarcane bagasse, and 28.5% cured bovine manure. The water supply of the plants was done with 5mm of water per day. The application of urea (45% N) and the acquisition of images occurred 14 and 32 days after sowing, respectively. A code developed in Matlab© R2022b was used to cut the original images into smaller blocks, originating an image bank composed of 4 folders representing the four classes and labeled as T1, T2, T3, and T4, each containing 500 images of 224x224 pixels obtained from plants cultivated under different N doses. The Matlab© R2022b software was used for the implementation and performance analysis of the model. The evaluation of the efficiency was done by a set of metrics, including accuracy (AC), F1-score (F1), specificity (SP), area under the curve (AUC), and precision (P). The ResNet-50 showed high performance in the classification of foliar N levels in common beans, with AC values of 85.6%. The F1 for classes T1, T2, T3, and T4 was 76, 72, 74, and 77%, respectively. This study revealed that the use of RGB images combined with deep learning can be a promising alternative to slow laboratory analyses, capable of optimizing the estimation of foliar N. This can allow rapid intervention by the producer to achieve higher productivity and less fertilizer waste. Future approaches are encouraged to develop mobile devices capable of handling images using deep learning for the classification of the nutritional status of plants in situ.

Keywords: convolutional neural network, residual network 50, nutritional status, artificial intelligence

Procedia PDF Downloads 19

Authors: Mona Shojaei

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The Sub-Atmospheric Vapor Pipeline (SAVP) introduces a distinct approach to seawater desalination with promising applications in both land and industrial sectors. SAVP systems exploit the temperature difference between a hot source and a cold environment to facilitate efficient vapor transfer, offering substantial benefits in diverse industrial and field applications. This approach incorporates dynamic boundary conditions, where the temperatures of hot and cold sources vary over time, particularly in natural and industrial environments. Such variations critically influence convection and diffusion processes, introducing challenges that require the refinement of the convection-diffusion equation and the derivation of temperature profiles along the pipeline through advanced engineering mathematics. This study formulates vapor temperature as a function of time and length using two mathematical approaches: Eigen functions and Green’s equation. Combining detailed theoretical modeling, mathematical simulations, and extensive field and industrial tests, this research underscores the SAVP system’s scalability for real-world applications. Results reveal a high degree of accuracy, highlighting SAVP’s significant potential for energy conservation and environmental sustainability. Furthermore, the integration of SAVP technology within smart and green building systems creates new opportunities for sustainable urban water management. By capturing and repurposing vapor for non-potable uses such as irrigation, greywater recycling, and ecosystem support in green spaces, SAVP aligns with the principles of smart and green buildings. Smart buildings emphasize efficient resource management, enhanced system control, and automation for optimal energy and water use, while green buildings prioritize environmental impact reduction and resource conservation. SAVP technology bridges both paradigms, enhancing water self-sufficiency and reducing reliance on external water supplies. The sustainable and energy-efficient properties of SAVP make it a vital component in resilient infrastructure development, addressing urban water scarcity while promoting eco-friendly living. This dual alignment with smart and green building goals positions SAVP as a transformative solution in the pursuit of sustainable urban resource management.

Keywords: sub-atmospheric vapor pipeline, seawater desalination, energy efficiency, vapor transfer dynamics, mathematical modeling, sustainable water solutions, smart buildings

Procedia PDF Downloads 12

Authors: Kathrina Aseanne Acapulco-Gomez, Shayne Julieane Morales, Tzar Francis Verame

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Mean platelet volume (MPV) is the most accurate measure of the size of platelets and is routinely measured by most automated hematological analyzers. Several studies have shown associations between MPV and cardiovascular risks and outcomes. Although its measurement may provide useful data, MPV remains to be a diagnostic tool that is yet to be included in routine clinical decision making. The aim of this systematic review and meta-analysis is to determine summary estimates of the diagnostic accuracy of mean platelet volume for the diagnosis of myocardial infarction among adult patients with angina and/or its equivalents in terms of sensitivity, specificity, diagnostic odds ratio, and likelihood ratios, and to determine the difference of the mean MPV values between those with MI and those in the non-MI controls. The primary search was done through search in electronic databases PubMed, Cochrane Review CENTRAL, HERDIN (Health Research and Development Information Network), Google Scholar, Philippine Journal of Pathology, and Philippine College of Physicians Philippine Journal of Internal Medicine. The reference list of original reports was also searched. Cross-sectional, cohort, and case-control articles studying the diagnostic performance of mean platelet volume in the diagnosis of acute myocardial infarction in adult patients were included in the study. Studies were included if: (1) CBC was taken upon presentation to the ER or upon admission (within 24 hours of symptom onset); (2) myocardial infarction was diagnosed with serum markers, ECG, or according to accepted guidelines by the Cardiology societies (American Heart Association (AHA), American College of Cardiology (ACC), European Society of Cardiology (ESC); and, (3) if outcomes were measured as significant difference AND/OR sensitivity and specificity. The authors independently screened for inclusion of all the identified potential studies as a result of the search. Eligible studies were appraised using well-defined criteria. Any disagreement between the reviewers was resolved through discussion and consensus. The overall mean MPV value of those with MI (9.702 fl; 95% CI 9.07 – 10.33) was higher than in those of the non-MI control group (8.85 fl; 95% CI 8.23 – 9.46). Interpretation of the calculated t-value of 2.0827 showed that there was a significant difference in the mean MPV values of those with MI and those of the non-MI controls. The summary sensitivity (Se) and specificity (Sp) for MPV were 0.66 (95% CI; 0.59 - 0.73) and 0.60 (95% CI; 0.43 – 0.75), respectively. The pooled diagnostic odds ratio (DOR) was 2.92 (95% CI; 1.90 – 4.50). The positive likelihood ratio of MPV in the diagnosis of myocardial infarction was 1.65 (95% CI; 1.20 – 22.27), and the negative likelihood ratio was 0.56 (95% CI; 0.50 – 0.64). The intended role for MPV in the diagnostic pathway of myocardial infarction would perhaps be best as a triage tool. With a DOR of 2.92, MPV values can discriminate between those who have MI and those without. For a patient with angina presenting with elevated MPV values, it is 1.65 times more likely that he has MI. Thus, it is implied that the decision to treat a patient with angina or its equivalents as a case of MI could be supported by an elevated MPV value.

Keywords: mean platelet volume, MPV, myocardial infarction, angina, chest pain

Procedia PDF Downloads 87

Authors: Najibullah M, Soumendra Nath Kuiry

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Dams and reservoirs are perceived for their estimable alms to irrigation, water supply, flood control, electricity generation, etc. which civilize the prosperity and wealth of society across the world. Meantime the dam breach could cause devastating flood that can threat to the human lives and properties. Failures of large dams remain fortunately very seldom events. Nevertheless, a number of occurrences have been recorded in the world, corresponding in an average to one to two failures worldwide every year. Some of those accidents have caused catastrophic consequences. So it is decisive to predict the dam break flow for emergency planning and preparedness, as it poses high risk to life and property. To mitigate the adverse impact of dam break, modeling is necessary to gain a good understanding of the temporal and spatial evolution of the dam-break floods. This study will mainly deal with one-dimensional (1D) dam break modeling. Less commonly used in the hydraulic research community, another possible option for modeling the rapidly varied dam-break flows is the extended Boussinesq equations (BEs), which can describe the dynamics of short waves with a reasonable accuracy. Unlike the Shallow Water Equations (SWEs), the BEs taken into account the wave dispersion and non-hydrostatic pressure distribution. To capture the dam-break oscillations accurately it is very much needed of at least fourth-order accurate numerical scheme to discretize the third-order dispersion terms present in the extended BEs. The scope of this work is therefore to develop an 1D fourth-order accurate in both space and time Boussinesq model for dam-break flow analysis by using finite-volume / finite difference scheme. The spatial discretization of the flux and dispersion terms achieved through a combination of finite-volume and finite difference approximations. The flux term, was solved using a finite-volume discretization whereas the bed source and dispersion term, were discretized using centered finite-difference scheme. Time integration achieved in two stages, namely the third-order Adams Basforth predictor stage and the fourth-order Adams Moulton corrector stage. Implementation of the 1D Boussinesq model done using PYTHON 2.7.5. Evaluation of the performance of the developed model predicted as compared with the volume of fluid (VOF) based commercial model ANSYS-CFX. The developed model is used to analyze the risk of cascading dam failures similar to the Panshet dam failure in 1961 that took place in Pune, India. Nevertheless, this model can be used to predict wave overtopping accurately compared to shallow water models for designing coastal protection structures.

Keywords: Boussinesq equation, Coastal protection, Dam-break flow, One-dimensional model

Procedia PDF Downloads 232

Authors: Erika Yamaguchi, Sirawadee Arunyanrt, Shunichi Ohmori, Kazuho Yoshimoto

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In this paper, we suggest a simulation tool to make a decision of monthly production planning for maximizing a profit of Specialty store retailer of Private label Apparel (SPA) firms. Most of SPA firms are fabless and make outsourcing deals for productions with factories of their subcontractors. Every month, SPA firms make a booking for production lines and manpower in the factories. The booking is conducted a few months in advance based on a demand prediction and a monthly production planning at that time. However, the demand prediction is updated month by month, and the monthly production planning would change to meet the latest demand prediction. Then, SPA firms have to change the capacities initially booked within a certain range to suit to the monthly production planning. The booking system is called “capacity-booking”. These days, though it is an issue for SPA firms to make precise monthly production planning, many firms are still conducting the production planning by empirical rules. In addition, it is also a challenge for SPA firms to match their products and factories with considering their demand predictabilities and regulation abilities. In this paper, we suggest a model for considering these two issues. An objective is to maximize a total profit of certain periods, which is sales minus costs of production, inventory, and capacity-booking penalty. To make a better monthly production planning at SPA firms, these points should be considered: demand predictabilities by random trends, previous and next month’s production planning of the target month, and regulation abilities of the capacity-booking. To decide matching products and factories for outsourcing, it is important to consider seasonality, volume, and predictability of each product, production possibility, size, and regulation ability of each factory. SPA firms have to consider these constructions and decide orders with several factories per one product. We modeled these issues as a linear programming. To validate the model, an example of several computational experiments with a SPA firm is presented. We suppose four typical product groups: basic, seasonal (Spring / Summer), seasonal (Fall / Winter), and spot product. As a result of the experiments, a monthly production planning was provided. In the planning, demand predictabilities from random trend are reduced by producing products which are different product types. Moreover, priorities to produce are given to high-margin products. In conclusion, we developed a simulation tool to make a decision of monthly production planning which is useful when the production planning is set every month. We considered the features of capacity-booking, and matching of products and factories which have different features and conditions.

Keywords: capacity-booking, SPA, monthly production planning, linear programming

Procedia PDF Downloads 519

Authors: Ali Ashtiani, Hamid Shirazi

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This paper reviews the status of existing practice and research related to measuring pavement cracking and using crack density as a pavement surface evaluation protocol. Crack density for pavement evaluation is currently not widely used within the airport community and its use by the highway community is limited. However, surface cracking is a distress that is closely monitored by airport staff and significantly influences the development of maintenance, rehabilitation and reconstruction plans for airport pavements. Therefore crack density has the potential to become an important indicator of pavement condition if the type, severity and extent of surface cracking can be accurately measured. A pavement distress survey is an essential component of any pavement assessment. Manual crack surveying has been widely used for decades to measure pavement performance. However, the accuracy and precision of manual surveys can vary depending upon the surveyor and performing surveys may disrupt normal operations. Given the variability of manual surveys, this method has shown inconsistencies in distress classification and measurement. This can potentially impact the planning for pavement maintenance, rehabilitation and reconstruction and the associated funding strategies. A substantial effort has been devoted for the past 20 years to reduce the human intervention and the error associated with it by moving toward automated distress collection methods. The automated methods refer to the systems that identify, classify and quantify pavement distresses through processes that require no or very minimal human intervention. This principally involves the use of a digital recognition software to analyze and characterize pavement distresses. The lack of established protocols for measurement and classification of pavement cracks captured using digital images is a challenge to developing a reliable automated system for distress assessment. Variations in types and severity of distresses, different pavement surface textures and colors and presence of pavement joints and edges all complicate automated image processing and crack measurement and classification. This paper summarizes the commercially available systems and technologies for automated pavement distress evaluation. A comprehensive automated pavement distress survey involves collection, interpretation, and processing of the surface images to identify the type, quantity and severity of the surface distresses. The outputs can be used to quantitatively calculate the crack density. The systems for automated distress survey using digital images reviewed in this paper can assist the airport industry in the development of a pavement evaluation protocol based on crack density. Analysis of automated distress survey data can lead to a crack density index. This index can be used as a means of assessing pavement condition and to predict pavement performance. This can be used by airport owners to determine the type of pavement maintenance and rehabilitation in a more consistent way.

Keywords: airport pavement management, crack density, pavement evaluation, pavement management

Procedia PDF Downloads 185

Authors: M. Bonello, D. Micallef, S. P. Borg

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Driven by increased comfort standards, but at the same time high energy consciousness, energy-efficient space cooling has become an essential aspect of building design. Its aims are simple, aiming at providing satisfactory thermal comfort for individuals in an interior space using low energy consumption cooling systems. In this context, evaporative cooling is both an energy-efficient and an eco-friendly cooling process. In the past two decades, several academic studies have been performed to determine the resulting thermal comfort produced by an evaporative cooling system, including studies on temperature profiles, air speed profiles, effect of clothing and personnel activity. To the best knowledge of the authors, no studies have yet considered the analysis of relative humidity (RH) profiles in a space cooled using evaporative cooling. Such a study will determine the effect of different humidity levels on a person's thermal comfort and aid in the consequent improvement designs of such future systems. Under this premise, the research objective is to characterise the resulting different RH profiles in a chamber micro-climate using the evaporative cooling system in which the inlet air speed, temperature and humidity content are varied. The chamber shall be modelled using Computational Fluid Dynamics (CFD) in ANSYS Fluent. Relative humidity shall be modelled using a species transport model while the k-ε RNG formulation is the proposed turbulence model that is to be used. The model shall be validated with measurements taken using an identical test chamber in which tests are to be conducted under the different inlet conditions mentioned above, followed by the verification of the model's mesh and time step. The verified and validated model will then be used to simulate other inlet conditions which would be impractical to conduct in the actual chamber. More details of the modelling and experimental approach will be provided in the full paper The main conclusions from this work are two-fold: the micro-climatic relative humidity spatial distribution within the room is important to consider in the context of investigating comfort at occupant level; and the investigation of a human being's thermal comfort (based on Predicted Mean Vote – Predicted Percentage Dissatisfied [PMV-PPD] values) and its variation with different locations of relative humidity values. The study provides the necessary groundwork for investigating the micro-climatic RH conditions of environments cooled using evaporative cooling. Future work may also target the analysis of ways in which evaporative cooling systems may be improved to better the thermal comfort of human beings, specifically relating to the humidity content around a sedentary person.

Keywords: chamber micro-climate, evaporative cooling, relative humidity, thermal comfort

Procedia PDF Downloads 155

Authors: M. Khorshed Alam, H. Takaba

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The catalytic properties of metal usually change by intermixturing with another metal in polymer electrolyte fuel cells. Pt-Ru alloy is one of the much-talked used alloy to enhance the CO oxidation. In this work, we have investigated the CO coverage on the Pt2Ru3 nanoparticle with different atomic conformation of Pt and Ru using a combination of material informatics with computational chemistry. Density functional theory (DFT) calculations used to describe the adsorption strength of CO and H with different conformation of Pt Ru ratio in the Pt2Ru3 slab surface. Then through the Monte Carlo (MC) simulations we examined the segregation behaviour of Pt as a function of surface atom ratio, subsurface atom ratio, particle size of the Pt2Ru3 nanoparticle. We have constructed a regression equation so as to reproduce the results of DFT only from the structural descriptors. Descriptors were selected for the regression equation; xa-b indicates the number of bonds between targeted atom a and neighboring atom b in the same layer (a,b = Pt or Ru). Terms of xa-H2 and xa-CO represent the number of atoms a binding H2 and CO molecules, respectively. xa-S is the number of atom a on the surface. xa-b- is the number of bonds between atom a and neighboring atom b located outside the layer. The surface segregation in the alloying nanoparticles is influenced by their component elements, composition, crystal lattice, shape, size, nature of the adsorbents and its pressure, temperature etc. Simulations were performed on different size (2.0 nm, 3.0 nm) of nanoparticle that were mixing of Pt and Ru atoms in different conformation considering of temperature range 333K. In addition to the Pt2Ru3 alloy we also considered pure Pt and Ru nanoparticle to make comparison of surface coverage by adsorbates (H2, CO). Hence, we assumed the pure and Pt-Ru alloy nanoparticles have an fcc crystal structures as well as a cubo-octahedron shape, which is bounded by (111) and (100) facets. Simulations were performed up to 50 million MC steps. From the results of MC, in the presence of gases (H2, CO), the surfaces are occupied by the gas molecules. In the equilibrium structure the coverage of H and CO as a function of the nature of surface atoms. In the initial structure, the Pt/Ru ratios on the surfaces for different cluster sizes were in range of 0.50 - 0.95. MC simulation was employed when the partial pressure of H2 (PH2) and CO (PCO) were 70 kPa and 100-500 ppm, respectively. The Pt/Ru ratios decrease as the increase in the CO concentration, without little exception only for small nanoparticle. The adsorption strength of CO on the Ru site is higher than the Pt site that would be one of the reason for decreasing the Pt/Ru ratio on the surface. Therefore, our study identifies that controlling the nanoparticle size, composition, conformation of alloying atoms, concentration and chemical potential of adsorbates have impact on the steadiness of nanoparticle alloys which ultimately and also overall catalytic performance during the operations.

Keywords: anode catalysts, fuel cells, material informatics, Monte Carlo

Procedia PDF Downloads 192

Authors: Ameni Mejri, Moufida Hajjaj, Salem Hasnaoui, Ridha Bouallegue

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The technology advances have most lately made the millimeter wave (mmWave) communication possible. Due to the huge amount of spectrum that is available in MmWave frequency bands, this promising candidate is considered as a key technology for the deployment of 5G cellular networks. In order to enhance system capacity and achieve spectral efficiency, very large antenna arrays are employed at mmWave systems by exploiting array gain. However, it has been shown that conventional beamforming strategies are not suitable for mmWave hardware implementation. Therefore, new features are required for mmWave cellular applications. Unlike traditional multiple-input-multiple-output (MIMO) systems for which only digital precoders are essential to accomplish precoding, MIMO technology seems to be different at mmWave because of digital precoding limitations. Moreover, precoding implements a greater number of radio frequency (RF) chains supporting more signal mixers and analog-to-digital converters. As RF chain cost and power consumption is increasing, we need to resort to another alternative. Although the hybrid precoding architecture has been regarded as the best solution based on a combination between a baseband precoder and an RF precoder, we still do not get the optimal design of hybrid precoders. According to the mapping strategies from RF chains to the different antenna elements, there are two main categories of hybrid precoding architecture. Given as a hybrid precoding sub-array architecture, the partially-connected structure reduces hardware complexity by using a less number of phase shifters, whereas it sacrifices some beamforming gain. In this paper, we treat the hybrid precoder design in mmWave MIMO systems as a problem of matrix factorization. Thus, we adopt the alternating minimization principle in order to solve the design problem. Further, we present our proposed algorithm for the partially-connected structure, which is based on the iterative hard thresholding method. Through simulation results, we show that our hybrid precoding algorithm provides significant performance gains over existing algorithms. We also show that the proposed approach reduces significantly the computational complexity. Furthermore, valuable design insights are provided when we use the proposed algorithm to make simulation comparisons between the hybrid precoding partially-connected structure and the fully-connected structure.

Keywords: alternating minimization, hybrid precoding, iterative hard thresholding, low-complexity, millimeter wave communication, partially-connected structure

Procedia PDF Downloads 322

Authors: Shing-Cheng Chang, Cheng-Hao Yang, Wen-Sheng Chang, Chih-Chia Lin, Chun-Han Li

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The solid oxide fuel cell (SOFC) is a promising green technology which can achieve a high electrical efficiency. Due to the high operating temperature of SOFC stack, the off-gases at high temperature from anode and cathode outlets are introduced into an afterburner to convert the chemical energy into thermal energy by combustion. The heat is recovered to preheat the fresh air and fuel gases before they pass through the stack during the SOFC power generation system operation. For an afterburner of the SOFC system, the temperature control with a good thermal uniformity is important. A burner with a well-designed geometry usually can achieve a satisfactory performance. To design an afterburner for an SOFC system, the computational fluid dynamics (CFD) simulation is adoptable. In this paper, the hydrogen combustion characteristics in an afterburner with simple geometry are studied by using CFD. The burner is constructed by a cylinder chamber with the configuration of a fuel gas inlet, an air inlet, and an exhaust outlet. The flow field and temperature distributions inside the afterburner under different fuel and air flow rates are analyzed. To improve the temperature uniformity of the afterburner during the SOFC system operation, the flow paths of anode/cathode off-gases are varied by changing the positions of fuels and air inlet channel to improve the heat and flow field synergy in the burner furnace. Because the air flow rate is much larger than the fuel gas, the flow structure and heat transfer in the afterburner is dominated by the air flow path. The present work studied the effects of fluid flow structures on the combustion characteristics of an SOFC afterburner by three simulation models with a cylindrical combustion chamber and a tapered outlet. All walls in the afterburner are assumed to be no-slip and adiabatic. In each case, two set of parameters are simulated to study the transport phenomena of hydrogen combustion. The equivalence ratios are in the range of 0.08 to 0.1. Finally, the pattern factor for the simulation cases is calculated to investigate the effect of gas inlet locations on the temperature uniformity of the SOFC afterburner. The results show that the temperature uniformity of the exhaust gas can be improved by simply adjusting the position of the gas inlet. The field synergy analysis indicates the design of the fluid flow paths should be in the way that can significantly contribute to the heat transfer, i.e. the field synergy angle should be as small as possible. In the study cases, the averaged synergy angle of the burner is about 85̊, 84̊, and 81̊ respectively.

Keywords: afterburner, combustion, field synergy, solid oxide fuel cell

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Authors: Melese Wondatir

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Flooding ranks among the most destructive natural disasters, impacting millions of individuals globally and resulting in substantial economic, social, and environmental repercussions. This study's objective was to create a comprehensive model that assesses the Nile River basin's susceptibility to flood damage and improves existing flood risk management strategies. Authorities responsible for enacting policies and implementing measures may benefit from this research to acquire essential information about the flood, including its scope and susceptible areas. The identification of severe flood damage locations and efficient mitigation techniques were made possible by the use of geospatial data. Slope, elevation, distance from the river, drainage density, topographic witness index, rainfall intensity, distance from road, NDVI, soil type, and land use type were all used throughout the study to determine the vulnerability of flood damage. Ranking elements according to their significance in predicting flood damage risk was done using the Analytic Hierarchy Process (AHP) and geospatial approaches. The analysis finds that the most important parameters determining the region's vulnerability are distance from the river, topographic witness index, rainfall, and elevation, respectively. The consistency ratio (CR) value obtained in this case is 0.000866 (<0.1), which signifies the acceptance of the derived weights. Furthermore, 10.84m2, 83331.14m2, 476987.15m2, 24247.29m2, and 15.83m2 of the region show varying degrees of vulnerability to flooding—very low, low, medium, high, and very high, respectively. Due to their close proximity to the river, the northern-western regions of the Nile River basin—especially those that are close to Sudanese cities like Khartoum—are more vulnerable to flood damage, according to the research findings. Furthermore, the AUC ROC curve demonstrates that the categorized vulnerability map achieves an accuracy rate of 91.0% based on 117 sample points. By putting into practice strategies to address the topographic witness index, rainfall patterns, elevation fluctuations, and distance from the river, vulnerable settlements in the area can be protected, and the impact of future flood occurrences can be greatly reduced. Furthermore, the research findings highlight the urgent requirement for infrastructure development and effective flood management strategies in the northern and western regions of the Nile River basin, particularly in proximity to major towns such as Khartoum. Overall, the study recommends prioritizing high-risk locations and developing a complete flood risk management plan based on the vulnerability map.

Keywords: analytic hierarchy process, Blue Nile Basin, geospatial techniques, flood vulnerability, multi-criteria decision making

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Authors: Matthew E. Potter, Daniel J. Stewart, Lindsay M. Armstrong, Pier J. A. Sazio, Robert R. Raja

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Rising CO₂ levels in the atmosphere means that CO₂ is a highly desirable feedstock. This requires specific catalysts to be designed to activate this inert molecule, combining a catalytic site tailored for CO₂ transformations with a support that can readily adsorb CO₂. Metal organic frameworks (MOFs) are regularly used as CO₂ sorbents. The organic nature of the linker molecules, connecting the metal nodes, offers many post-synthesis modifications to introduce catalytic active sites into the frameworks. However, the metal nodes may be coordinatively unsaturated, allowing them to bind to organic moieties. Imidazoles have shown promise catalyzing the formation of cyclic carbonates from epoxides with CO₂. Typically, this synthesis route employs toxic reagents such as phosgene, liberating HCl. Therefore an alternative route with CO₂ is highly appealing. In this work we design active sites for CO₂ activation, by tethering substituted-imidazole organocatalytic species to the available Cr3+ metal nodes of a Cr-MIL-101 MOF, for the first time, to create a tailored species for carbon capture utilization applications. Our tailored design strategy combining a CO₂ sorbent, Cr-MIL-101, with an anchored imidazole results in a highly active and selective multifunctional catalyst, achieving turnover frequencies of over 750 hr-1. These findings demonstrate the synergy between the MOF framework and imidazoles for CO₂ utilization applications. Further, the effect of substrate variation has been explored yielding mechanistic insights into this process. Through characterization, we show that the structural and compositional integrity of the Cr-MIL-101 has been preserved on functionalizing the imidazoles. Further, we show the binding of the imidazoles to the Cr3+ metal nodes. This can be seen through our EPR study, where the distortion of the Cr3+ on binding to the imidazole shows the CO₂ binding site is close to the active imidazole. This has a synergistic effect, improving catalytic performance. We believe the combination of MOF support and organocatalyst allows many possibilities to generate new multifunctional catalysts for CO₂ utilisation. In conclusion, we have validated our design procedure, combining a known CO₂ sorbent, with an active imidazole species to create a unique tailored multifunctional catalyst for CO₂ utilization. This species achieves high activity and selectivity for the formation of cyclic carbonates and offers a sustainable alternative to traditional synthesis methods. This work represents a unique design strategy for CO₂ utilization while offering exciting possibilities for further work in characterization, computational modelling, and post-synthesis modification.

Keywords: carbonate, catalysis, MOF, utilisation

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Authors: F. Ghani, T. S. O’Donovan

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Today, the global solar thermal market is dominated by two collector types; the flat plate and evacuated tube collector. With regards to the number of installations worldwide, the evacuated tube collector is the dominant variant primarily due to the Chinese market but the flat plate collector dominates both the Australian and European markets. The market share of the evacuated tube collector is, however, growing in Australia due to a common belief that this collector type is ‘more efficient’ and, therefore, the better choice for hot water applications. In this study, we investigate this issue further to assess the validity of this statement. This was achieved by methodically comparing the performance and economics of several solar thermal systems comprising of; a low-performance flat plate collector, a high-performance flat collector, and an evacuated tube collector coupled with a storage tank and pump. All systems were simulated using the commercial software package Polysun for four climate zones in Australia to take into account different weather profiles in the study and subjected to a thermal load equivalent to a household comprising of four people. Our study revealed that the energy savings and payback periods varied significantly for systems operating under specific environmental conditions. Solar fractions ranged between 58 and 100 per cent, while payback periods range between 3.8 and 10.1 years. Although the evacuated tube collector was found to operate with a marginally higher thermal efficiency over the selective surface flat plate collector due to reduced ambient heat loss, the high-performance flat plate collector outperformed the evacuated tube collector on thermal yield. This result was obtained as the flat plate collector possesses a significantly higher absorber to gross collector area ratio over the evacuated tube collector. Furthermore, it was found for Australian regions operating with a high average solar radiation intensity and ambient temperature, the lower performance collector is the preferred choice due to favorable economics and reduced stagnation temperature. Our study has provided additional insight into the thermal performance and economics of the two prevalent solar thermal collectors currently available. A computational investigation has been carried out specifically for the Australian climate due to its geographic size and significant variation in weather. For domestic hot water applications were fluid temperatures between 50 and 60 degrees Celsius are sought, the flat plate collector is both technically and economically favorable over the evacuated tube collector. This research will be useful to system design engineers, solar thermal manufacturers, and those involved in policy to encourage the implementation of solar thermal systems into the hot water market.

Keywords: solar thermal, energy analysis, flat plate, evacuated tube, collector performance

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Authors: Lakshmidevi Sreeramareddy, Komalpreet Kaur, Nane Pothier

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Graphical-based passwords have existed for decades. Their major advantage is that they are easier to remember than an alphanumeric password. However, their disadvantage (especially recognition-based passwords) is the smaller password space, making them more vulnerable to brute force attacks. Graphical passwords are also highly susceptible to the shoulder-surfing effect. The gesture-based password method that we developed is a grid-free, template-free method. In this study, we evaluated the gesture-based passwords for usability and vulnerability. The results of the study are significant. We developed a gesture-based password application for data collection. Two modes of data collection were used: Creation mode and Replication mode. In creation mode (Session 1), users were asked to create six different passwords and reenter each password five times. In replication mode, users saw a password image created by some other user for a fixed duration of time. Three different duration timers, such as 5 seconds (Session 2), 10 seconds (Session 3), and 15 seconds (Session 4), were used to mimic the shoulder-surfing attack. After the timer expired, the password image was removed, and users were asked to replicate the password. There were 74, 57, 50, and 44 users participated in Session 1, Session 2, Session 3, and Session 4 respectfully. In this study, the machine learning algorithms have been applied to determine whether the person is a genuine user or an imposter based on the password entered. Five different machine learning algorithms were deployed to compare the performance in user authentication: namely, Decision Trees, Linear Discriminant Analysis, Naive Bayes Classifier, Support Vector Machines (SVMs) with Gaussian Radial Basis Kernel function, and K-Nearest Neighbor. Gesture-based password features vary from one entry to the next. It is difficult to distinguish between a creator and an intruder for authentication. For each password entered by the user, four features were extracted: password score, password length, password speed, and password size. All four features were normalized before being fed to a classifier. Three different classifiers were trained using data from all four sessions. Classifiers A, B, and C were trained and tested using data from the password creation session and the password replication with a timer of 5 seconds, 10 seconds, and 15 seconds, respectively. The classification accuracies for Classifier A using five ML algorithms are 72.5%, 71.3%, 71.9%, 74.4%, and 72.9%, respectively. The classification accuracies for Classifier B using five ML algorithms are 69.7%, 67.9%, 70.2%, 73.8%, and 71.2%, respectively. The classification accuracies for Classifier C using five ML algorithms are 68.1%, 64.9%, 68.4%, 71.5%, and 69.8%, respectively. SVMs with Gaussian Radial Basis Kernel outperform other ML algorithms for gesture-based password authentication. Results confirm that the shorter the duration of the shoulder-surfing attack, the higher the authentication accuracy. In conclusion, behavioral features extracted from the gesture-based passwords lead to less vulnerable user authentication.

Keywords: authentication, gesture-based passwords, machine learning algorithms, shoulder-surfing attacks, usability

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Authors: Atbin Mahabbati, Jason Beringer, Matthias Leopold

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To address the global challenges of climate and environmental changes, there is a need for quantifying and reducing uncertainties in environmental data, including observations of carbon, water, and energy. Global eddy covariance flux tower networks (FLUXNET), and their regional counterparts (i.e., OzFlux, AmeriFlux, China Flux, etc.) were established in the late 1990s and early 2000s to address the demand. Despite the capability of eddy covariance in validating process modelling analyses, field surveys and remote sensing assessments, there are some serious concerns regarding the challenges associated with the technique, e.g. data gaps and uncertainties. To address these concerns, this research has developed an ensemble model to fill the data gaps of CO₂ flux to avoid the limitations of using a single algorithm, and therefore, provide less error and decline the uncertainties associated with the gap-filling process. In this study, the data of five towers in the OzFlux Network (Alice Springs Mulga, Calperum, Gingin, Howard Springs and Tumbarumba) during 2013 were used to develop an ensemble machine learning model, using five feedforward neural networks (FFNN) with different structures combined with an eXtreme Gradient Boosting (XGB) algorithm. The former methods, FFNN, provided the primary estimations in the first layer, while the later, XGB, used the outputs of the first layer as its input to provide the final estimations of CO₂ flux. The introduced model showed slight superiority over each single FFNN and the XGB, while each of these two methods was used individually, overall RMSE: 2.64, 2.91, and 3.54 g C m⁻² yr⁻¹ respectively (3.54 provided by the best FFNN). The most significant improvement happened to the estimation of the extreme diurnal values (during midday and sunrise), as well as nocturnal estimations, which is generally considered as one of the most challenging parts of CO₂ flux gap-filling. The towers, as well as seasonality, showed different levels of sensitivity to improvements provided by the ensemble model. For instance, Tumbarumba showed more sensitivity compared to Calperum, where the differences between the Ensemble model on the one hand and the FFNNs and XGB, on the other hand, were the least of all 5 sites. Besides, the performance difference between the ensemble model and its components individually were more significant during the warm season (Jan, Feb, Mar, Oct, Nov, and Dec) compared to the cold season (Apr, May, Jun, Jul, Aug, and Sep) due to the higher amount of photosynthesis of plants, which led to a larger range of CO₂ exchange. In conclusion, the introduced ensemble model slightly improved the accuracy of CO₂ flux gap-filling and robustness of the model. Therefore, using ensemble machine learning models is potentially capable of improving data estimation and regression outcome when it seems to be no more room for improvement while using a single algorithm.

Keywords: carbon flux, Eddy covariance, extreme gradient boosting, gap-filling comparison, hybrid model, OzFlux network

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Authors: Yanxue Shang, Jingbin Zeng

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Chlorobenzene homologues (CBHs) are a category of environmental pollutants that can not be ignored. They can stay in the environment for a long period and are potentially carcinogenic. The traditional degradation method of CBHs is dechlorination followed by sample preparation and analysis. This is not only time-consuming and laborious, but the detection and analysis processes are used in conjunction with large-scale instruments. Therefore, this can not achieve rapid and low-cost detection. Compared with traditional sensing methods, colorimetric sensing is simpler and more convenient. In recent years, chromaticity sensors based on fluorescence have attracted more and more attention. Compared with sensing methods based on changes in fluorescence intensity, changes in color gradients are easier to recognize by the naked eye. Accordingly, this work proposes to use single drop microextraction (SDME) technology to solve the above problems. After the dechlorination reaction was completed, the organic droplet extracts Cl⁻ and realizes fluorescence colorimetric sensing at the same time. This method was integrated sample processing and visual in-situ detection, simplifying the detection process. As a fluorescence colorimetric sensor material, CsPbBr₃ was encapsulated in MOF-5 to construct CsPbBr₃@MOF-5 fluorescence colorimetric composite. Then the fluorescence colorimetric sensor was constructed by dispersing the composite in SDME organic droplets. When the Br⁻ in CsPbBr₃ exchanges with Cl⁻ produced by the dechlorination reactions, it is converted into CsPbCl₃. The fluorescence color of the single droplet of SDME will change from green to blue emission, thereby realizing visual observation. Therein, SDME can enhance the concentration and enrichment of Cl⁻ and instead of sample pretreatment. The fluorescence color change of CsPbBr₃@MOF-5 can replace the detection process of large-scale instruments to achieve real-time rapid detection. Due to the absorption ability of MOF-5, it can not only improve the stability of CsPbBr₃, but induce the adsorption of Cl⁻. Simultaneously, accelerate the exchange of Br- and Cl⁻ in CsPbBr₃ and the detection process of Cl⁻. The absorption process was verified by density functional theory (DFT) calculations. This method exhibits exceptional linearity for Cl⁻ in the range of 10⁻² - 10⁻⁶ M (10000 μM - 1 μM) with a limit of detection of 10⁻⁷ M. Whereafter, the dechlorination reactions of different kinds of CBHs were also carried out with this method, and all had satisfactory detection ability. Also verified the accuracy by gas chromatography (GC), and it was found that the SDME we developed in this work had high credibility. In summary, the in-situ visualization method of dechlorination reaction detection was a combination of sample processing and fluorescence colorimetric sensing. Thus, the strategy researched herein represents a promising method for the visual detection of dechlorination reactions and can be extended for applications in environments, chemical industries, and foods.

Keywords: chlorobenzene homologues, colorimetric sensor, metal halide perovskite, metal-organic frameworks, single drop microextraction

Procedia PDF Downloads 143

Authors: Yew Mun Yip, Dawei Zhang

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Proteins are the biological machinery that executes specific vital functions in every cell of the human body by folding into their 3D structures. When a protein misfolds from its native structure, the machinery will malfunction and lead to misfolding diseases. Although in vitro experiments are able to conclude that the mutations of the amino acid sequence lead to incorrectly folded protein structures, these experiments are unable to decipher the folding process. Therefore, molecular dynamic (MD) simulations are employed to simulate the folding process so that our improved understanding of the folding process will enable us to contemplate better treatments for misfolding diseases. MD simulations make use of force fields to simulate the folding process of peptides. Secondary structures are formed via the hydrogen bonds formed between the backbone atoms (C, O, N, H). It is important that the hydrogen bond energy computed during the MD simulation is accurate in order to direct the folding process to the native structure. Since the atoms involved in a hydrogen bond possess very dissimilar electronegativities, the more electronegative atom will attract greater electron density from the less electronegative atom towards itself. This is known as the polarization effect. Since the polarization effect changes the electron density of the two atoms in close proximity, the atomic charges of the two atoms should also vary based on the strength of the polarization effect. However, the fixed atomic charge scheme in force fields does not account for the polarization effect. In this study, we introduce the polarized structure-specific backbone charge (PSBC) model. The PSBC model accounts for the polarization effect in MD simulation by updating the atomic charges of the backbone hydrogen bond atoms according to equations derived between the amount of charge transferred to the atom and the length of the hydrogen bond, which are calculated from quantum-mechanical calculations. Compared to other polarizable models, the PSBC model does not require quantum-mechanical calculations of the peptide simulated at every time-step of the simulation and maintains the dynamic update of atomic charges, thereby reducing the computational cost and time while accounting for the polarization effect dynamically at the same time. The PSBC model is applied to two different β-peptides, namely the Beta3s/GS peptide, a de novo designed three-stranded β-sheet whose structure is folded in vitro and studied by NMR, and the trpzip peptides, a double-stranded β-sheet where a correlation is found between the type of amino acids that constitute the β-turn and the β-propensity.

Keywords: hydrogen bond, polarization effect, protein folding, PSBC

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Authors: Martin Juckel

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Probably no other field of inorganic chemistry has undergone such a rapid development in the past two decades than the low oxidation state chemistry of main group elements. This rapid development has only been possible by the development of new bulky ligands. In case of our research group, super-bulky monodentate amido ligands and β-diketiminate ligands have been used to a great success. We first synthesized the unprecedented magnesium(I) dimer [ᴹᵉˢNacnacMg]₂ (ᴹᵉˢNacnac = [(ᴹᵉˢNCMe)₂CH]-; Mes = mesityl, which has since been used both as reducing agent and also for the synthesis of new metal-magnesium bonds. In case of the zinc bromide precursor [L*ZnBr] (L*=(N(Ar*)(SiPri₃); (Ar* = C₆H₂{C(H)Ph₂}₂Me-2,6,4, the reduction with [ᴹᵉˢNacnacMg]₂ led to such a metal-magnesium bond. This [L*ZnMg(ᴹᵉˢNacnac)] compound can be seen as an ‘inorganic Grignard reagent’, which can be used to transfer the metal fragment onto other functional groups or other metal centers; just like the conventional Grignard reagent. By simple addition of (TBoN)GeCl (TBoN = N(SiMe₃){B(DipNCH)₂) to the aforesaid compound, we were able to transfer the amido-zinc fragment to the Ge center of the germylene starting material and to synthesize the first example of a germanium(II)-zinc bond: [:Ge(TBoN)(ZnL*)]. While these reactions typically led to complex product mixture, [:Ge(TBoN)(ZnL*)] could be isolated as dark blue crystals in a good yield. This new compound shows interesting reactivity towards small molecules, especially dihydrogen gas. This is of special interest as dihydrogen is one of the more difficult small molecules to activate, due to its strong (BDE = 108 kcal/mol) and non-polar bond. In this context, the interaction between H₂ σ-bond with the tetrelylene p-Orbital (LUMO), with concomitant donation of the tetrelylene lone pair (HOMO) into the H₂ σ* orbital are responsible for the activation of dihydrogen gas. Accordingly, the narrower the HOMO-LUMO gap of tertelylene, the more reactivity towards H₂ it typically is. The aim of a narrow HOMO-LUMO gap was reached by transferring electropositive substituents respectively metal substituents with relatively low Pauling electronegativity (zinc: 1.65) onto the Ge center (here: the zinc-amido fragment). In consideration of the unprecedented reactivity of [:Ge(TBoN)(ZnL*)], a computational examination of its frontier orbital energies was undertaken. The energy separation between the HOMO, which has significant Ge lone pair character, and the LUMO, which has predominantly Ge p-orbital character, is narrow (40.8 kcal/mol; cf.∆S-T= 24.8 kcal/mol), and comparable to the HOMO-LUMO gaps calculated for other literature known complexes). The calculated very narrow HOMO-LUMO gap for the [:Ge(TBoN)(ZnL*)] complex is consistent with its high reactivity, and is remarkable considering that it incorporates a π-basic amide ligand, which are known to raise the LUMO of germylenes considerably.

Keywords: activation of dihydrogen gas, narrow HOMO-LUMO gap, first germanium(II)-zinc bond, inorganic Grignard reagent

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Authors: W. Linthicum-Blackhorse, P. Martens

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This present study seeks to discover attitudinal commonalities and differences within a musician population relative to the communication of emotion via music. We hypothesized that instrument type, as well as age and gender, would bear significantly on musicians’ opinions. A survey was administered to 178 participants; 152 were current music majors (mean age 20.3 years, 62 female) and 26 were adult participants in a community choir (mean age 54.0 years, 12 female). The adult participants were all vocalists, while student participants represented the full range of orchestral instruments. The students were grouped by degree program, (performance, music education, or other) and instrument type (voice, brass, woodwinds, strings, percussion). The survey asked 'How important are each of the following areas to you for portraying emotion in music?' Participants were asked to rate each of 15 items on a scale of 1 (not at all important) to 10 (very important). Participants were also instructed to leave blank any item that they did not understand. The 15 items were: dynamic contrast, overall volume, phrasing, facial expression, staging (placement), pitch accuracy, tempo changes, bodily movement, your mood, your attitude, vibrato, rubato, stage/room lighting, clothing type, and clothing color. Contrary to our hypothesis, there was no overall effect of gender or age, and neither did any single response item show a significant difference due to these subject parameters. Among the student participants, however, one-way ANOVA revealed a significant effect of degree program on the rated importance of four items: dynamic contrast, tempo changes, vibrato, and rubato. Significant effects of instrument type were found in the responses to eight items: facial expression, staging, body movement, vibrato, rubato, lighting, clothing type, and clothing color. Post hoc comparisons (Tukey) show that some variation follows from obvious differences between instrument types (e.g. string players are more concerned with vibrato than everyone but woodwind players; vocalists are significantly more concerned with facial expression than everyone but string players), but other differences could point to communal mindsets toward vicarious cues within instrument type. These mindsets could be global (e.g. brass players deeming body movement significantly less important than string players, being less often featured as soloists and appearing less often at the front of the stage) or local (e.g. string players being significantly more concerned than all other groups about both clothing color and type, perhaps due to the strongly-expressed opinions of specific teachers). Future work will attempt to identify the source of these self-appraisals, whether enculturated via explicit pedagogy, or whether absorbed from individuals' observations and performance experience.

Keywords: performance, vicarious cues, communication, emotion

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Authors: S. J. Yune, S. Y. Lee, S. J. Im, B. S. Kam, S. Y. Baek

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This study aimed to determine the reliability of clinical performance assessments, having been emphasized by ability-based education, and professors overall assessment methods. We addressed the following problems: First, we try to find out whether there is a difference in what we consider to be the main variables affecting the clinical performance test according to the evaluator’s working period and the number of evaluation experience. Second, we examined the relationship among the global rating score (G), analytic global rating score (Gc), and the sum of the analytical checklists (C). What are the main factors affecting clinical performance assessments in relation to the numbers of times the evaluator had administered evaluations and the length of their working period service? What is the relationship between overall assessment score and analytic checklist score? How does analytic global rating with 6 components in OSCE and 4 components in sub-domains (Gc) CPX: aseptic practice, precision, systemic approach, proficiency, successfulness, and attitude overall assessment score and task-specific analytic checklist score sum (C) affect the professor’s overall global rating assessment score (G)? We studied 75 professors who attended a 2016 Bugyeoung Consortium clinical skills performances test evaluating third and fourth year medical students at the Pusan National University Medical school in South Korea (39 prof. in OSCE, 36 prof. in CPX; all consented to participate in our study). Each evaluator used 3 forms; a task-specific analytic checklist, subsequent analytic global rating scale with sub-6 domains, and overall global scale. After the evaluation, the professors responded to the questionnaire on the important factors of clinical performance assessment. The data were analyzed by frequency analysis, correlation analysis, and hierarchical regression analysis using SPSS 21.0. Their understanding of overall assessment was analyzed by dividing the subjects into groups based on experiences. As a result, they considered ‘precision’ most important in overall OSCE assessment, and ‘precise accuracy physical examination’, ‘systemic approaches to taking patient history’, and ‘diagnostic skill capability’ in overall CPX assessment. For OSCE, there was no clear difference of opinion about the main factors, but there was for CPX. Analytic global rating scale score, overall rating scale score, and analytic checklist score had meaningful mutual correlations. According to the regression analysis results, task-specific checklist score sum had the greatest effect on overall global rating. professors regarded task-specific analytic checklist total score sum as best reflecting overall OSCE test score, followed by aseptic practice, precision, systemic approach, proficiency, successfulness, and attitude on a subsequent analytic global rating scale. For CPX, subsequent analytic global rating scale score, overall global rating scale score, and task-specific checklist score had meaningful mutual correlations. These findings support explanations for validity of professors’ global rating in clinical performance assessment.

Keywords: global rating, clinical performance assessment, medical education, analytic checklist

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Authors: Stefan Papastefanou

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Artificial intelligence (AI) is an interdisciplinary field of computer science with the aim of creating intelligent machine behavior. Early approaches to AI have been configured to operate in very constrained environments where the behavior of the AI system was previously determined by formal rules. Knowledge was presented as a set of rules that allowed the AI system to determine the results for specific problems; as a structure of if-else rules that could be traversed to find a solution to a particular problem or question. However, such rule-based systems typically have not been able to generalize beyond the knowledge provided. All over the world and especially in IT-heavy industries such as the United States, the European Union, Singapore, and China, machine learning has developed to be an immense asset, and its applications are becoming more and more significant. It has to be examined how such products of machine learning models can and should be protected by IP law and for the purpose of this paper patent law specifically, since it is the IP law regime closest to technical inventions and computing methods in technical applications. Genetic breeding models are currently less popular than recursive neural network method and deep learning, but this approach can be more easily described by referring to the evolution of natural organisms, and with increasing computational power; the genetic breeding method as a subset of the evolutionary algorithms models is expected to be regaining popularity. The research method focuses on patentability (according to the world’s most significant patent law regimes such as China, Singapore, the European Union, and the United States) of AI inventions and machine learning. Questions of the technical nature of the problem to be solved, the inventive step as such, and the question of the state of the art and the associated obviousness of the solution arise in the current patenting processes. Most importantly, and the key focus of this paper is the problem of patenting inventions that themselves are developed through machine learning. The inventor of a patent application must be a natural person or a group of persons according to the current legal situation in most patent law regimes. In order to be considered an 'inventor', a person must actually have developed part of the inventive concept. The mere application of machine learning or an AI algorithm to a particular problem should not be construed as the algorithm that contributes to a part of the inventive concept. However, when machine learning or the AI algorithm has contributed to a part of the inventive concept, there is currently a lack of clarity regarding the ownership of artificially created inventions. Since not only all European patent law regimes but also the Chinese and Singaporean patent law approaches include identical terms, this paper ultimately offers a comparative analysis of the most relevant patent law regimes.

Keywords: algorithms, inventor, genetic breeding models, machine learning, patentability

Procedia PDF Downloads 108

Authors: Sofia Papadimitriou

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INTRODUCTION: The differential diagnosis between acute viral and bacterial infections is an important cost-effectiveness parameter at the stage of the treatment process in order to achieve the maximum benefits in therapeutic intervention by combining the minimum cost to ensure the proper use of antibiotics.The discovery of sensitive and robust molecular diagnostic tests in response to the role of the host in infections has enhanced the accurate diagnosis and differentiation of infections. METHOD: The study used a sample of six independent blood samples (total=756) which are associated with human proteins-proteins, each of which at the transcription stage expresses a different response in the host network between viral and bacterial infections.Τhe individual blood samples are subjected to a sequence of computer filters that identify a gene panel corresponding to an autonomous diagnostic score. The data set and the correspondence of the gene panel to the diagnostic patents a new Bangalore -Viral Bacterial (BL-VB). FINDING: We use a biomarker based on the blood of 10 genes(Panel-VB) that are an important prognostic value for the detection of viruses from bacterial infections with a weighted average AUROC of 0.97(95% CL:0.96-0.99) in eleven independent samples (sets n=898). We discovered a base with a patient score (VB 10 ) according to the table, which is a significant diagnostic value with a weighted average of AUROC 0.94(95% CL: 0.91-0.98) in 2996 patient samples from 56 public sets of data from 19 different countries. We also studied VB 10 in a new cohort of South India (BL-VB,n=56) and found 97% accuracy in confirmed cases of viral and bacterial infections. We found that VB 10 (a)accurately identifies the type of infection even in unspecified cases negative to the culture (b) shows its clinical condition recovery and (c) applies to all age groups, covering a wide range of acute bacterial and viral infectious, including non-specific pathogens. We applied our VB 10 rating to publicly available COVID 19 data and found that our rating diagnosed viral infection in patient samples. RESULTS: Τhe results of the study showed the diagnostic power of the biomarker VB 10 as a diagnostic test for the accurate diagnosis of acute infections in recovery conditions. We look forward to helping you make clinical decisions about prescribing antibiotics and integrating them into your policies management of antibiotic stewardship efforts. CONCLUSIONS: Overall, we are developing a new property of the RNA-based biomarker and a new blood test to differentiate between viral and bacterial infections to assist a physician in designing the optimal treatment regimen to contribute to the proper use of antibiotics and reduce the burden on antimicrobial resistance, AMR.

Keywords: acute infections, antimicrobial resistance, biomarker, blood transcriptome, systems biology, classifier diagnostic score

Procedia PDF Downloads 155

Authors: Amir Nazemi, Milad Ramezankhani, Marian Kӧrber, Abbas S. Milani

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The excellent strength to weight ratio of woven fabric composites, along with their high formability, is one of the primary design parameters defining their increased use in modern manufacturing processes, including those in aerospace and automotive. However, for emerging automated preform processes under the smart manufacturing paradigm, complex geometries of finished components continue to bring several challenges to the designers to cope with manufacturing defects on site. Wrinklinge. g. is a common defectoccurring during the forming process and handling of semi-finished textile composites. One of the main reasons for this defect is the weak bending stiffness of fibers in unconsolidated state, causing excessive relative motion between them. Further challenges are represented by the automated handling of large-area fiber blanks with specialized gripper systems. For fabric composites forming simulations, the finite element (FE)method is a longstanding tool usedfor prediction and mitigation of manufacturing defects. Such simulations are predominately meant, not only to predict the onset, growth, and shape of wrinkles but also to determine the best processing condition that can yield optimized positioning of the fibers upon forming (or robot handling in the automated processes case). However, the need for use of small-time steps via explicit FE codes, facing numerical instabilities, as well as large computational time, are among notable drawbacks of the current FEtools, hindering their extensive use as fast and yet efficient digital twins in industry. This paper presents a novel woven fabric simulation technique through the application of the material point method (MPM), which enables the use of much larger time steps, facing less numerical instabilities, hence the ability to run significantly faster and efficient simulationsfor fabric materials handling and forming processes. Therefore, this method has the ability to enhance the development of automated fiber handling and preform processes by calculating the physical interactions with the MPM fiber models and rigid tool components. This enables the designers to virtually develop, test, and optimize their processes based on either algorithmicor Machine Learning applications. As a preliminary case study, forming of a hemispherical plain weave is shown, and the results are compared to theFE simulations, as well as experiments.

Keywords: material point method, woven fabric composites, forming, material handling

Procedia PDF Downloads 181

Authors: Amit Gill, Lalith Wijerathne, Sebastian Poledna

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This research presents the details of the implementation of high performance computing (HPC) extension of agent-based economic models (ABEMs) to simulate hundreds of millions of heterogeneous agents. ABEMs offer an alternative approach to study the economy as a dynamic system of interacting heterogeneous agents, and are gaining popularity as an alternative to standard economic models. Over the last decade, ABEMs have been increasingly applied to study various problems related to monetary policy, bank regulations, etc. When it comes to predicting the effects of local economic disruptions, like major disasters, changes in policies, exogenous shocks, etc., on the economy of the country or the region, it is pertinent to study how the disruptions cascade through every single economic entity affecting its decisions and interactions, and eventually affect the economic macro parameters. However, such simulations with hundreds of millions of agents are hindered by the lack of HPC enhanced ABEMs. In order to address this, a scalable Distributed Memory Parallel (DMP) implementation of ABEMs has been developed using message passing interface (MPI). A balanced distribution of computational load among MPI-processes (i.e. CPU cores) of computer clusters while taking all the interactions among agents into account is a major challenge for scalable DMP implementations. Economic agents interact on several random graphs, some of which are centralized (e.g. credit networks, etc.) whereas others are dense with random links (e.g. consumption markets, etc.). The agents are partitioned into mutually-exclusive subsets based on a representative employer-employee interaction graph, while the remaining graphs are made available at a minimum communication cost. To minimize the number of communications among MPI processes, real-life solutions like the introduction of recruitment agencies, sales outlets, local banks, and local branches of government in each MPI-process, are adopted. Efficient communication among MPI-processes is achieved by combining MPI derived data types with the new features of the latest MPI functions. Most of the communications are overlapped with computations, thereby significantly reducing the communication overhead. The current implementation is capable of simulating a small open economy. As an example, a single time step of a 1:1 scale model of Austria (i.e. about 9 million inhabitants and 600,000 businesses) can be simulated in 15 seconds. The implementation is further being enhanced to simulate 1:1 model of Euro-zone (i.e. 322 million agents).

Keywords: agent-based economic model, high performance computing, MPI-communication, MPI-process

Procedia PDF Downloads 128