Search results for: machine modelling

Authors: O. O. Aiyelokun, O. A. Agbede

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Numerical modelling of groundwater flow can be susceptible to calibration errors due to lack of adequate ground-based hydro-metrological stations in river basins. Groundwater resources management in Southwest Nigeria is currently challenged by overexploitation, lack of planning and monitoring, urbanization and climate change; hence to adopt models as decision support tools for sustainable management of groundwater; they must be adequately calibrated. Since river basins in Southwest Nigeria are characterized by missing data, and lack of adequate ground-based hydro-meteorological stations; the need for adopting satellite-based data for constructing distributed models is crucial. This study seeks to evaluate the suitability of satellite-based data as substitute for ground-based, for computing boundary conditions; by determining if ground and satellite based meteorological data fit well in Ogun and Oshun River basins. The Climate Forecast System Reanalysis (CFSR) global meteorological dataset was firstly obtained in daily form and converted to monthly form for the period of 432 months (January 1979 to June, 2014). Afterwards, ground-based meteorological data for Ikeja (1981-2010), Abeokuta (1983-2010), and Oshogbo (1981-2010) were compared with CFSR data using Goodness of Fit (GOF) statistics. The study revealed that based on mean absolute error (MEA), coefficient of correlation, (r) and coefficient of determination (R²); all meteorological variables except wind speed fit well. It was further revealed that maximum and minimum temperature, relative humidity and rainfall had high range of index of agreement (d) and ratio of standard deviation (rSD), implying that CFSR dataset could be used to compute boundary conditions such as groundwater recharge and potential evapotranspiration. The study concluded that satellite-based data such as the CFSR should be used as input when constructing groundwater flow models in river basins in Southwest Nigeria, where majority of the river basins are partially gaged and characterized with long missing hydro-metrological data.

Keywords: boundary condition, goodness of fit, groundwater, satellite-based data

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Authors: Shakila N. Senarath, Dinesh Asanka, Janaka Wijayanayake

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The necessity of cosmetic products is arising to fulfill consumer needs of personality appearance and hygiene. A cosmetic product consists of various chemical ingredients which may help to keep the skin healthy or may lead to damages. Every chemical ingredient in a cosmetic product does not perform on every human. The most appropriate way to select a healthy cosmetic product is to identify the texture of the body first and select the most suitable product with safe ingredients. Therefore, the selection process of cosmetic products is complicated. Consumer surveys have shown most of the time, the selection process of cosmetic products is done in an improper way by consumers. From this study, a content-based system is suggested that recommends cosmetic products for the human factors. To such an extent, the skin type, gender and price range will be considered as human factors. The proposed system will be implemented by using Machine Learning. Consumer skin type, gender and price range will be taken as inputs to the system. The skin type of consumer will be derived by using the Baumann Skin Type Questionnaire, which is a value-based approach that includes several numbers of questions to derive the user’s skin type to one of the 16 skin types according to the Bauman Skin Type indicator (BSTI). Two datasets are collected for further research proceedings. The user data set was collected using a questionnaire given to the public. Those are the user dataset and the cosmetic dataset. Product details are included in the cosmetic dataset, which belongs to 5 different kinds of product categories (Moisturizer, Cleanser, Sun protector, Face Mask, Eye Cream). An alternate approach of TF-IDF (Term Frequency – Inverse Document Frequency) is applied to vectorize cosmetic ingredients in the generic cosmetic products dataset and user-preferred dataset. Using the IF-IPF vectors, each user-preferred products dataset and generic cosmetic products dataset can be represented as sparse vectors. The similarity between each user-preferred product and generic cosmetic product will be calculated using the cosine similarity method. For the recommendation process, a similarity matrix can be used. Higher the similarity, higher the match for consumer. Sorting a user column from similarity matrix in a descending order, the recommended products can be retrieved in ascending order. Even though results return a list of similar products, and since the user information has been gathered, such as gender and the price ranges for product purchasing, further optimization can be done by considering and giving weights for those parameters once after a set of recommended products for a user has been retrieved.

Keywords: content-based filtering, cosmetics, machine learning, recommendation system

Procedia PDF Downloads 134

Authors: Nathaniel Carpenter

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Web-enabled systems for supporting youth mental health management in Australia are pioneering in their field; however, with their success, these systems are experiencing exponential growth in demand which is straining an already stretched service. Supporting youth mental is critical as the lack of support is associated with significant and lasting negative consequences. To meet this growing demand, and provide critical support, investigations are needed on evaluating and improving existing online support services. Improvements should focus on developing frameworks capable of augmenting and scaling service provisions. There are few investigations informing best-practice frameworks when implementing e-mental health support systems for youth mental health; there are fewer which implement machine learning or artificially intelligent systems to facilitate the delivering of services. This investigation will use a case study methodology to highlight the design features which are important for systems to enable young people to self-manage their mental health. The investigation will also highlight the current information system challenges, to include challenges associated with service quality, provisioning, and scaling. This work will propose methods of meeting these challenges through improved design, service augmentation and automation, service quality, and through artificially intelligent inspired solutions. The results of this study will inform a framework for supporting youth mental health with intelligent and scalable web-enabled technologies to support an ever-growing user base.

Keywords: artificial intelligence, information systems, machine learning, youth mental health

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Authors: Aïssa Manallah, Mohamed Bouafia

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Ellipsometry is an optical method based on the study of the behavior of polarized light. The light reflected on a surface induces a change in the polarization state which depends on the characteristics of the material (complex refractive index and thickness of the different layers constituting the device). The purpose of this work is to determine the optical properties of semiconductor thin films by ellipsometry. This paper describes the experimental aspects concerning the semiconductor samples, the SE400 ellipsometer principle, and the results obtained by direct measurements of ellipsometric parameters and modelling using appropriate software.

Keywords: ellipsometry, optical constants, semiconductors, thin films

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Authors: Remo Waser, Simon Maranda, Anastasia Stamatiou, Ludger J. Fischer, Joerg Worlitschek

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In latent heat storage, a phase change material (PCM) is used to store thermal energy. The heat transfer rate during solidification is limited and considered as a key challenge in the development of latent heat storages. Thus, finned heat exchangers (HEX) are often utilized to increase the heat transfer rate of the storage system. In this study, a new modeling approach to calculating the heat transfer rate in latent thermal energy storages with complex HEX geometries is presented. This model allows for an optimization of the HEX design in terms of costs and thermal performance of the system. Modeling solidification processes requires the calculation of time-dependent heat conduction with moving boundaries. Commonly used computational fluid dynamic (CFD) methods enable the analysis of the heat transfer in complex HEX geometries. If applied to the entire storage, the drawback of this approach is the high computational effort due to small time steps and fine computational grids required for accurate solutions. An alternative to describe the process of solidification is the so-called temperature-based approach. In order to minimize the computational effort, a quasi-stationary assumption can be applied. This approach provides highly accurate predictions for tube heat exchangers. However, it shows unsatisfactory results for more complex geometries such as finned tube heat exchangers. The presented simulation model uses a temporal and spatial discretization of heat exchanger tube. The spatial discretization is based on the smallest possible symmetric segment of the HEX. The heat flow in each segment is calculated using finite volume method. Since the heat transfer fluid temperature can be derived using energy conservation equations, the boundary conditions at the inner tube wall is dynamically updated for each time step and segment. The model allows a prediction of the thermal performance of latent thermal energy storage systems using complex HEX geometries with considerably low computational effort.

Keywords: modelling of solidification, finned tube heat exchanger, latent thermal energy storage

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Authors: Sung Uk Park, Young Su Kang, Sangmo Kang, Young Kweon Suh

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Mineral product, waste concrete (fine aggregates), waste in the optical field, industry, and construction employ separators to separate solids and classify them according to their size. Various sorting machines are used in the industrial field such as those operating under electrical properties, centrifugal force, wind power, vibration, and magnetic force. Study on separators has been carried out to contribute to the environmental industry. In this study, we perform CFD analysis for understanding the basic mechanism of the separation of waste concrete (fine aggregate) particles from air with a machine built with a rotor with blades. In CFD, we first performed two-dimensional particle tracking for various particle sizes for the model with 1 degree, 1.5 degree, and 2 degree angle between each blade to verify the boundary conditions and the method of rotating domain method to be used in 3D. Then we developed 3D numerical model with ANSYS CFX to calculate the air flow and track the particles. We judged the capability of particle separation for given size by counting the number of particles escaping from the domain toward the exit among 10 particles issued at the inlet. We confirm that particles experience stagnant behavior near the exit of the rotating blades where the centrifugal force acting on the particles is in balance with the air drag force. It was also found that the minimum particle size that can be separated by the machine with the rotor is determined by its capability to stay at the outlet of the rotor channels.

Keywords: environmental industry, separator, CFD, fine aggregate

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Authors: Ammar Edress Mohamed, Mustafa Aziz, David Wright

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This paper analyses and calculates the head fields of asymmetrical 2D magnetic recording heads when the soft-underlayer is present using the appropriate Green's function to derive the surface potential/field by utilising the surface potential for asymmetrical head without underlayer. The results follow closely the corners, while the gap region shows a linear behaviour for d/g < 0.5 compared with the calculated fields from finite-element.

Keywords: magnetic recording, finite elements, asymmetrical magnetic heads, superposition, Laplace's equation

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Authors: John Gibbs, Benjamin Flanders, Dylan Pozorski, Weixuan Liu

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Weight Painting–adjusting the influence a skeletal joint has on a given vertex in a character mesh–is an arduous and time con- suming part of the 3D animation pipeline. This process generally requires a trained technical animator and many hours of work to complete. Our skiNNer plug-in, which works within Autodesk’s Maya 3D animation software, uses Machine Learning and data pro- cessing techniques to create a deep neural network model that can accomplish the weight painting task in seconds rather than hours for bipedal quasi-humanoid character meshes. In order to create a properly trained network, a number of challenges were overcome, including curating an appropriately large data library, managing an arbitrary 3D mesh size, handling arbitrary skeletal architectures, accounting for extreme numeric values (most data points are near 0 or 1 for weight maps), and constructing an appropriate neural network model that can properly capture the high frequency alter- ation between high weight values (near 1.0) and low weight values (near 0.0). The arrived at neural network model is a cross between a traditional CNN, deep residual network, and fully dense network. The resultant network captures the unusually hard-edged features of a weight map matrix, and produces excellent results on many bipedal models.

Keywords: 3d animation, animation, character, rigging, skinning, weight painting, machine learning, artificial intelligence, neural network, deep neural network

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Authors: Goudjil kamel, Boukhatem Ghania, Jlailia Djihene

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This paper delves into the development of a sophisticated desktop application designed to calculate soil bearing capacity and predict limit pressure. Drawing from an extensive review of existing methodologies, the study meticulously examines various approaches employed in soil bearing capacity calculations, elucidating their theoretical foundations and practical applications. Furthermore, the study explores the burgeoning intersection of artificial intelligence (AI) and geotechnical engineering, underscoring the transformative potential of AI- driven solutions in enhancing predictive accuracy and efficiency.Central to the research is the utilization of cutting-edge machine learning techniques, including Artificial Neural Networks (ANN), XGBoost, and Random Forest, for predictive modeling. Through comprehensive experimentation and rigorous analysis, the efficacy and performance of each method are rigorously evaluated, with XGBoost emerging as the preeminent algorithm, showcasing superior predictive capabilities compared to its counterparts. The study culminates in a nuanced understanding of the intricate dynamics at play in geotechnical analysis, offering valuable insights into optimizing soil bearing capacity calculations and limit pressure predictions. By harnessing the power of advanced computational techniques and AI-driven algorithms, the paper presents a paradigm shift in the realm of geotechnical engineering, promising enhanced precision and reliability in civil engineering projects.

Keywords: limit pressure of soil, xgboost, random forest, bearing capacity

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Authors: Alexander Matrosov, Guriy Shirunov

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A superposition method based on Lame's idea is used to get a general analytical solution to analyze a stress and strain state of a rectangular isotropjc elastic thick plate. The solution is built by using three solutions of the method of initial functions in the form of double trigonometric series. The results of bending of a thick plate under normal stress on its top face with two opposite sides clamped while others free of load are presented and compared with FEM modelling.

Keywords: general solution, method of initial functions, superposition method, thick isotropic plates

Procedia PDF Downloads 598

Authors: Phillip Kearney, Constantina Lekakou, Stephen Belcher, Alessandro Sordon

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The focus in the automotive industry is to reduce human operator and machine interaction, so manufacturing becomes more automated and safer. The aim is to lower part cost and construction time as well as defects in the parts, sometimes occurring due to the physical limitations of human operators. A move to automate the layup of reinforcement material in composites manufacturing has resulted in the use of tapes that are placed in position by a robotic deposition head, also described as Automated Fibre Placement (AFP). The process of AFP is limited with respect to the finite amount of material that can be loaded into the machine at any one time. Joining two batches of tape material together involves a splice to secure the ends of the finishing tape to the starting edge of the new tape. The splicing method of choice for the majority of prepreg applications is a hand stich method, and as the name suggests requires human input to achieve. This investigation explores three methods for automated splicing, namely, adhesive, binding and stitching. The adhesive technique uses an additional adhesive placed on the tape ends to be joined. Binding uses the binding agent that is already impregnated onto the tape through the application of heat. The stitching method is used as a baseline to compare the new splicing methods to the traditional technique currently in use. As the methods will be used within a High Speed Automated Fibre Placement (HSAFP) process, this meant the parameters of the splices have to meet certain specifications: (a) the splice must be able to endure a load of 50 N in tension applied at a rate of 1 mm/s; (b) the splice must be created in less than 6 seconds, dictated by the capacity of the tape accumulator within the system. The samples for experimentation were manufactured with controlled overlaps, alignment and splicing parameters, these were then tested in tension using a tensile testing machine. Initial analysis explored the use of the impregnated binding agent present on the tape, as in the binding splicing technique. It analysed the effect of temperature and overlap on the strength of the splice. It was found that the optimum splicing temperature was at the higher end of the activation range of the binding agent, 100 °C. The optimum overlap was found to be 25 mm; it was found that there was no improvement in bond strength from 25 mm to 30 mm overlap. The final analysis compared the different splicing methods to the baseline of a stitched bond. It was found that the addition of an adhesive was the best splicing method, achieving a maximum load of over 500 N compared to the 26 N load achieved by a stitching splice and 94 N by the binding method.

Keywords: analysis, automated fibre placement, high speed, splicing

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Authors: Muhammad Umair, Yuri Nikolaev, Denis Artemov, Ighor Uzhinsky

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A flexible manufacturing system is an integral part of a smart factory of industry 4.0 in which every machine is interconnected and works autonomously. Robots are in the process of replacing humans in every industrial sector. As the cyber-physical-system (CPS) and artificial intelligence (AI) are advancing, the manufacturing industry is getting more dependent on computers than human brains. This modernization has boosted the production with high quality and accuracy and shifted from classic production to smart manufacturing systems. However, material handling for such automated productions is a challenge and needs to be addressed with the best possible solution. Conventional clamping systems are designed for manual work and not suitable for highly automated production systems. Researchers and engineers are trying to find the most economical solution for loading/unloading and transportation workpieces from a warehouse to a machine shop for machining operations and back to the warehouse without human involvement. This work aims to propose an advanced multi-shape tooling solution for highly automated manufacturing systems. The currently obtained result shows that it could function well with automated guided vehicles (AGVs) and modern conveyor belts. The proposed solution is following requirements to be automation-friendly, universal for different part geometry and production operations. We used a bottom-up approach in this work, starting with studying different case scenarios and their limitations and finishing with the general solution.

Keywords: artificial intelligence, cyber physics system, Industry 4.0, material handling, smart factory, flexible manufacturing system

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Authors: Yingisani Chabalala, Elhadi Adam, Khalid Adem Ali

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Mapping smallholder fruit plantations using optical data is challenging due to morphological landscape heterogeneity and crop types having overlapped spectral signatures. Furthermore, cloud covers limit the use of optical sensing, especially in subtropical climates where they are persistent. This research assessed the effectiveness of Sentinel-1 (S1) and Sentinel-2 (S2) data for mapping fruit trees and co-existing land-use types by using support vector machine (SVM) and random forest (RF) classifiers independently. These classifiers were also applied to fused data from the two sensors. Feature ranks were extracted using the RF mean decrease accuracy (MDA) and forward variable selection (FVS) to identify optimal spectral windows to classify fruit trees. Based on RF MDA and FVS, the SVM classifier resulted in relatively high classification accuracy with overall accuracy (OA) = 0.91.6% and kappa coefficient = 0.91% when applied to the fused satellite data. Application of SVM to S1, S2, S2 selected variables and S1S2 fusion independently produced OA = 27.64, Kappa coefficient = 0.13%; OA= 87%, Kappa coefficient = 86.89%; OA = 69.33, Kappa coefficient = 69. %; OA = 87.01%, Kappa coefficient = 87%, respectively. Results also indicated that the optimal spectral bands for fruit tree mapping are green (B3) and SWIR_2 (B10) for S2, whereas for S1, the vertical-horizontal (VH) polarization band. Including the textural metrics from the VV channel improved crop discrimination and co-existing land use cover types. The fusion approach proved robust and well-suited for accurate smallholder fruit plantation mapping.

Keywords: smallholder agriculture, fruit trees, data fusion, precision agriculture

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Authors: Sergio Haram Sarmiento, Arcady Ponosov

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Based on appropriate multivariate statistical methodology, we suggest a generic framework for efficient parameter estimation for ordinary differential equations and the corresponding nonlinear models. In this framework classical linear regression strategies is refined into a nonlinear regression by a locally linear modelling technique (known as metamodelling). The approach identifies those latent variables of the given model that accumulate most information about it among all approximations of the same dimension. The method is applied to several benchmark problems, in particular, to the so-called ”power-law systems”, being non-linear differential equations typically used in Biochemical System Theory.

Keywords: principal component analysis, generalized law of mass action, parameter estimation, metamodels

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Authors: Gaurav Kumar Sinha

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The oil refining industry, significant in its energy consumption and carbon emissions, faces increasing pressure to reduce its environmental footprint. This article explores the application of energy efficiency and sustainability analytics as crucial tools for reducing carbon emissions in oil refineries. Through a comprehensive review of current practices and technologies, this study highlights innovative analytical approaches that can significantly enhance energy efficiency. We focus on the integration of advanced data analytics, including machine learning and predictive modeling, to optimize process controls and energy use. These technologies are examined for their potential to not only lower energy consumption but also reduce greenhouse gas emissions. Additionally, the article discusses the implementation of sustainability analytics to monitor and improve environmental performance across various operational facets of oil refineries. We explore case studies where predictive analytics have successfully identified opportunities for reducing energy use and emissions, providing a template for industry-wide application. The challenges associated with deploying these analytics, such as data integration and the need for skilled personnel, are also addressed. The paper concludes with strategic recommendations for oil refineries aiming to enhance their sustainability practices through the adoption of targeted analytics. By implementing these measures, refineries can achieve significant reductions in carbon emissions, aligning with global environmental goals and regulatory requirements.

Keywords: energy efficiency, sustainability analytics, carbon emissions, oil refineries, data analytics, machine learning, predictive modeling, process optimization, greenhouse gas reduction, environmental performance

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Authors: Serge Tshibangu, Turgay Celik, Zenzo Ncube

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Road crashes kill nearly over a million people every year, and leave millions more injured or permanently disabled. Various annual reports reveal that the percentage of fatal crashes due to fatigue/driver falling asleep comes directly after the percentage of fatal crashes due to intoxicated drivers. This percentage is higher than the combined percentage of fatal crashes due to illegal/Un-Safe U-turn and illegal/Un-Safe reversing. Although a relatively small percentage of police reports on road accidents highlights drowsiness and fatigue, the importance of these factors is greater than we might think, hidden by the undercounting of their events. Some scenarios show that these factors are significant in accidents with killed and injured people. Thus the need for an automatic drivers fatigue detection system in order to considerably reduce the number of accidents owing to fatigue.This research approaches the drivers fatigue detection problem in an innovative way by combining cues collected from both temporal analysis of drivers’ faces and environment. Monotony in driving environment is inter-related with visual symptoms of fatigue on drivers’ faces to achieve fatigue detection. Optical and infrared (IR) sensors are used to analyse the monotony in driving environment and to detect the visual symptoms of fatigue on human face. Internal cues from drivers faces and external cues from environment are combined together using machine learning algorithms to automatically detect fatigue.

Keywords: intelligent transportation systems, bayesian networks, yawning computing, machine learning algorithms

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Authors: Ammar Alali, Mahmoud Abughaban

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Cost reduction and drilling optimization is the goal of many drilling operators. Historically, stuck pipe incidents were a major segment of non-productive time (NPT) associated costs. Traditionally, stuck pipe problems are part of the operations and solved post-sticking. However, the real key to savings and success is in predicting the stuck pipe incidents and avoiding the conditions leading to its occurrences. Previous attempts in stuck-pipe predictions have neglected the local geology of the problem. The proposed predictive tool utilizes geophysical data processing techniques and Machine Learning (ML) algorithms to predict drilling activities events in real-time using surface drilling data with minimum computational power. The method combines two types of analysis: (1) real-time prediction, and (2) cause analysis. Real-time prediction aggregates the input data, including historical drilling surface data, geological formation tops, and petrophysical data, from wells within the same field. The input data are then flattened per the geological formation and stacked per stuck-pipe incidents. The algorithm uses two physical methods (stacking and flattening) to filter any noise in the signature and create a robust pre-determined pilot that adheres to the local geology. Once the drilling operation starts, the Wellsite Information Transfer Standard Markup Language (WITSML) live surface data are fed into a matrix and aggregated in a similar frequency as the pre-determined signature. Then, the matrix is correlated with the pre-determined stuck-pipe signature for this field, in real-time. The correlation used is a machine learning Correlation-based Feature Selection (CFS) algorithm, which selects relevant features from the class and identifying redundant features. The correlation output is interpreted as a probability curve of stuck pipe incidents prediction in real-time. Once this probability passes a fixed-threshold defined by the user, the other component, cause analysis, alerts the user of the expected incident based on set pre-determined signatures. A set of recommendations will be provided to reduce the associated risk. The validation process involved feeding of historical drilling data as live-stream, mimicking actual drilling conditions, of an onshore oil field. Pre-determined signatures were created for three problematic geological formations in this field prior. Three wells were processed as case studies, and the stuck-pipe incidents were predicted successfully, with an accuracy of 76%. This accuracy of detection could have resulted in around 50% reduction in NPT, equivalent to 9% cost saving in comparison with offset wells. The prediction of stuck pipe problem requires a method to capture geological, geophysical and drilling data, and recognize the indicators of this issue at a field and geological formation level. This paper illustrates the efficiency and the robustness of the proposed cross-disciplinary approach in its ability to produce such signatures and predicting this NPT event.

Keywords: drilling optimization, hazard prediction, machine learning, stuck pipe

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Authors: Sujesh F. Sujan, Steve W. Jones, Arto Kiviniemi

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The use of Building Information Modelling (BIM) in the Architectural-Engineering-Construction (AEC) industry is a form of systemic innovation. Unlike incremental innovation, (such as the technological development of CAD from hand based drawings to 2D electronically printed drawings) any form of systemic innovation in Project-Based Inter-Organisational Networks requires complete collaboration and results in numerous benefits if adopted and utilised properly. Proper use of BIM involves people collaborating with the use of interoperable BIM compliant tools. The AEC industry globally has been known for its adversarial and fragmented nature where firms take advantage of one another to increase their own profitability. Due to the industry’s nature, getting people to collaborate by unifying their goals is critical to successful BIM adoption. However, this form of innovation is often being forced artificially in the old ways of working which do not suit collaboration. This may be one of the reasons for its low global use even though the technology was developed more than 20 years ago. Therefore, there is a need to develop a metric/method to support and allow industry players to gain confidence in their investment into BIM software and workflow methods. This paper departs from defining systemic risk as a risk that affects all the project participants at a given stage of a project and defines categories of systemic risks. The need to generalise is to allow method applicability to any industry where the category will be the same, but the example of the risk will depend on the industry the study is done in. The method proposed seeks to use individual perception of an example of systemic risk as a key parameter. The significance of this study lies in relating the variance of individual perception of systemic risk to how much the team is collaborating. The method bases its notions on the claim that a more unified range of individual perceptions would mean a higher probability that the team is collaborating better. Since contracts and procurement devise how a project team operates, the method could also break the methodological barrier of highly subjective findings that case studies inflict, which has limited the possibility of generalising between global industries. Since human nature applies in all industries, the authors’ intuition is that perception can be a valuable parameter to study collaboration which is essential especially in projects that utilise systemic innovation such as BIM.

Keywords: building information modelling, perception of risk, systemic innovation, team collaboration

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Authors: M. Belén Prendes-Gero, Celestino González-Nicieza, M. Inmaculada Alvarez-Fernández

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Roof collapse is one of the problems with a higher frequency in most of the mines of all countries, even now. There are many reasons that may cause the roof to collapse, namely the mine stress activities in the mining process, the lack of vigilance and carelessness or the complexity of the geological structure and irregular operations. This work is the result of the analysis of one accident produced in the “Mary” coal exploitation located in northern Spain. In this accident, the roof of a crossroad of excavated galleries to exploit the “Morena” Layer, 700 m deep, collapsed. In the paper, the work done by the forensic team to determine the causes of the incident, its conclusions and recommendations are collected. Initially, the available documentation (geology, geotechnics, mining, etc.) and accident area were reviewed. After that, laboratory and on-site tests were carried out to characterize the behaviour of the rock materials and the support used (metal frames and shotcrete). With this information, different hypotheses of failure were simulated to find the one that best fits reality. For this work, the software of finite differences in three dimensions, FLAC 3D, was employed. The results of the study confirmed that the detachment was originated as a consequence of one sliding in the layer wall, due to the large roof span present in the place of the accident, and probably triggered as a consequence of the existence of a protection pillar insufficient. The results allowed to establish some corrective measures avoiding future risks. For example, the dimensions of the protection zones that must be remained unexploited and their interaction with the crossing areas between galleries, or the use of more adequate supports for these conditions, in which the significant deformations may discourage the use of rigid supports such as shotcrete. At last, a grid of seismic control was proposed as a predictive system. Its efficiency was tested along the investigation period employing three control equipment that detected new incidents (although smaller) in other similar areas of the mine. These new incidents show that the use of explosives produces vibrations which are a new risk factor to analyse in a next future.

Keywords: forensic analysis, hypothesis modelling, roof failure, seismic monitoring

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Authors: Matthew F. Gibson, Narasimha D. Rao, Raphael B. Slade, Joana Portugal Pereira, Joeri Rogelj

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Globally, India’s population is among the most severely impacted by nutrient deficiency, yet millions of tonnes of food are lost before reaching consumers. Across food groups, grains represent the largest share of daily calories and overall losses by mass in India. If current losses remain unresolved and follow projected population rates, we estimate, by 2030, losses from grains for human consumption could increase by 1.3-1.8 million tonnes (Mt) per year against current levels of ~10 Mt per year. This study quantifies energy input to minimise storage losses across India, responsible for a quarter of grain supply chain losses. In doing so, we identify and explore a Sustainable Development Goal (SDG) triplet between SDG₂, SDG₇, and SDG₁₂ and provide insight for development of joined up agriculture and health policy in the country. Analyzing rice, wheat, maize, bajra, and sorghum, we quantify one route to reduce losses in supply chains, by modelling the energy input to maintain favorable climatic conditions in modern silo storage. We quantify key nutrients (calories, protein, zinc, iron, vitamin A) contained within these losses and calculate roughly how much deficiency in these dietary components could be reduced if grain losses were eliminated. Our modelling indicates, with appropriate uncertainty, maize has the highest energy input intensity for storage, at 110 kWh per tonne of grain (kWh/t), and wheat the lowest (72 kWh/t). This energy trade-off represents 8%-16% of the energy input required in grain production. We estimate if grain losses across the supply chain were saved and targeted to India’s nutritionally deficient population, average protein deficiency could reduce by 46%, calorie by 27%, zinc by 26%, and iron by 11%. This study offers insight for development of Indian agriculture, food, and health policy by first quantifying and then presenting benefits and trade-offs of tackling food grain losses.

Keywords: energy, food loss, grain storage, hunger, India, sustainable development goal, SDG

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Authors: M. Aghili, S. Tabarestani, C. Freytes, M. Shojaie, M. Cabrerizo, A. Barreto, N. Rishe, R. E. Curiel, D. Loewenstein, R. Duara, M. Adjouadi

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A major challenge in medical studies, especially those that are longitudinal, is the problem of missing measurements which hinders the effective application of many machine learning algorithms. Furthermore, recent Alzheimer's Disease studies have focused on the delineation of Early Mild Cognitive Impairment (EMCI) and Late Mild Cognitive Impairment (LMCI) from cognitively normal controls (CN) which is essential for developing effective and early treatment methods. To address the aforementioned challenges, this paper explores the potential of using the eXtreme Gradient Boosting (XGBoost) algorithm in handling missing values in multiclass classification. We seek a generalized classification scheme where all prodromal stages of the disease are considered simultaneously in the classification and decision-making processes. Given the large number of subjects (1631) included in this study and in the presence of almost 28% missing values, we investigated the performance of XGBoost on the classification of the four classes of AD, NC, EMCI, and LMCI. Using 10-fold cross validation technique, XGBoost is shown to outperform other state-of-the-art classification algorithms by 3% in terms of accuracy and F-score. Our model achieved an accuracy of 80.52%, a precision of 80.62% and recall of 80.51%, supporting the more natural and promising multiclass classification.

Keywords: eXtreme gradient boosting, missing data, Alzheimer disease, early mild cognitive impairment, late mild cognitive impair, multiclass classification, ADNI, support vector machine, random forest

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Authors: Amnon Shirizly, Ohad Dolev

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In recent years, the implementation and use of Metal Additive Manufacturing (AM) parts increase. As a result, the demand for bigger parts rises along with the desire to reduce it’s the production cost. Generally, in powder bed Additive Manufacturing technology the part size is limited by the machine build volume. In order to overcome this limitation, the parts can be built in one or more machine operations and mechanically joint or weld them together. An alternative option could be a production of wrought part and built on it the AM structure (mainly to reduce costs). In both cases, the mechanical properties of the interface have to be defined and recognized. In the current study, the authors introduce guidelines on how to examine the interface between wrought alloy and powder-bed AM. The mechanical and metallurgical properties of the Ti6Al4V materials (wrought alloy and powder-bed AM) and their hybrid interface were examined. The mechanical properties gain from tensile test bars in the built direction and fracture toughness samples in various orientations. The hybrid specimens were built onto a wrought Ti6Al4V start-plate. The standard fracture toughness (CT25 samples) and hybrid tensile specimens' were heat treated and milled as a post process to final diminutions. In this Study, the mechanical tensile tests and fracture toughness properties supported by metallurgical observation will be introduced and discussed. It will show that the hybrid approach of utilizing powder bed AM onto wrought material expanding the current limitation of the future manufacturing technology.

Keywords: additive manufacturing, hybrid, fracture-toughness, powder bed

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Authors: Yash Jain

Abstract:

The US Center for Disease Control has recommended wearing masks to slow the spread of the virus. The research uses a video feed from a camera to conduct real-time classifications of whether or not a human is correctly wearing a mask, incorrectly wearing a mask, or not wearing a mask at all. Utilizing two distinct datasets from the open-source website Kaggle, a mask detection network had been trained. The first dataset that was used to train the model was titled 'Face Mask Detection' on Kaggle, where the dataset was retrieved from and the second dataset was titled 'Face Mask Dataset, which provided the data in a (YOLO Format)' so that the TinyYoloV3 model could be trained. Based on the data from Kaggle, two machine learning models were implemented and trained: a Tiny YoloV3 Real-time model and a two-stage neural network classifier. The two-stage neural network classifier had a first step of identifying distinct faces within the image, and the second step was a classifier to detect the state of the mask on the face and whether it was worn correctly, incorrectly, or no mask at all. The TinyYoloV3 was used for the live feed as well as for a comparison standpoint against the previous two-stage classifier and was trained using the darknet neural network framework. The two-stage classifier attained a mean average precision (MAP) of 80%, while the model trained using TinyYoloV3 real-time detection had a mean average precision (MAP) of 59%. Overall, both models were able to correctly classify stages/scenarios of no mask, mask, and incorrectly worn masks.

Keywords: datasets, classifier, mask-detection, real-time, TinyYoloV3, two-stage neural network classifier

Procedia PDF Downloads 163

Authors: Lai Wenfang

Abstract:

Tthis study will describe how to use artificial intelligence (AI) technology to build the user-oriented platform for integrated archival service. The platform will be launched in 2020 by the National Archives Administration (NAA) in Taiwan. With the progression of information communication technology (ICT) the NAA has built many systems to provide archival service. In order to cope with new challenges, such as new ICT, artificial intelligence or blockchain etc. the NAA will try to use the natural language processing (NLP) and machine learning (ML) skill to build a training model and propose suggestions based on the data sent to the platform. NAA expects the platform not only can automatically inform the sending agencies’ staffs which records catalogues are against the transfer or destroy rules, but also can use the model to find the details hidden in the catalogues and suggest NAA’s staff whether the records should be or not to be, to shorten the auditing time. The platform keeps all the users’ browse trails; so that the platform can predict what kinds of archives user could be interested and recommend the search terms by visualization, moreover, inform them the new coming archives. In addition, according to the Archives Act, the NAA’s staff must spend a lot of time to mark or remove the personal data, classified data, etc. before archives provided. To upgrade the archives access service process, the platform will use some text recognition pattern to black out automatically, the staff only need to adjust the error and upload the correct one, when the platform has learned the accuracy will be getting higher. In short, the purpose of the platform is to deduct the government digital transformation and implement the vision of a service-oriented smart government.

Keywords: artificial intelligence, natural language processing, machine learning, visualization

Procedia PDF Downloads 174

Authors: Songul Cinaroglu

Abstract:

Regional planning in health has gained speed for developing countries in recent years. In Turkey, 89 different Public Hospital Unions (PHUs) were conducted based on provincial levels. In this study technical efficiencies of 89 PHUs were examined by using Data Envelopment Analysis (DEA) and machine learning techniques by dividing them into two clusters in terms of similarities of input and output indicators. Number of beds, physicians and nurses determined as input variables and number of outpatients, inpatients and surgical operations determined as output indicators. Before performing DEA, PHUs were grouped into two clusters. It is seen that the first cluster represents PHUs which have higher population, demand and service density than the others. The difference between clusters was statistically significant in terms of all study variables (p ˂ 0.001). After clustering, DEA was performed for general and for two clusters separately. It was found that 11% of PHUs were efficient in general, additionally 21% and 17% of them were efficient for the first and second clusters respectively. It is seen that PHUs, which are representing urban parts of the country and have higher population and service density, are more efficient than others. Random forest decision tree graph shows that number of inpatients is a determinative factor of efficiency of PHUs, which is a measure of service density. It is advisable for public health policy makers to use statistical learning methods in resource planning decisions to improve efficiency in health care.

Keywords: public hospital unions, efficiency, data envelopment analysis, random forest

Procedia PDF Downloads 126

Authors: Rabie Fadil, Parshuram Aarotale, Shubha Majumder, Bijay Guargain

Abstract:

Hemorrhage is the loss of blood from the circulatory system and leading cause of battlefield and postpartum related deaths. Early detection of hemorrhage remains the most effective strategy to reduce mortality rate caused by traumatic injuries. In this study, we investigated the physiological changes via non-invasive cardiac signals at rest and under different hemorrhage conditions simulated through graded lower-body negative pressure (LBNP). Simultaneous electrocardiogram (ECG), photoplethysmogram (PPG), blood pressure (BP), impedance cardiogram (ICG), and phonocardiogram (PCG) were acquired from 10 participants (age:28 ± 6 year, weight:73 ± 11 kg, height:172 ± 8 cm). The LBNP protocol consisted of applying -20, -30, -40, -50, and -60 mmHg pressure to the lower half of the body. Beat-to-beat heart rate (HR), systolic blood pressure (SBP), diastolic blood pressure (DBP), and mean aerial pressure (MAP) were extracted from ECG and blood pressure. Systolic amplitude (SA), systolic time (ST), diastolic time (DT), and left ventricle Ejection time (LVET) were extracted from PPG during each stage. Preliminary results showed that the application of -40 mmHg i.e. moderate stage simulated hemorrhage resulted significant changes in HR (85±4 bpm vs 68 ± 5bpm, p < 0.01), ST (191 ± 10 ms vs 253 ± 31 ms, p < 0.05), LVET (350 ± 14 ms vs 479 ± 47 ms, p < 0.05) and DT (551 ± 22 ms vs 683 ± 59 ms, p < 0.05) compared to rest, while no change was observed in SA (p > 0.05) as a consequence of LBNP application. These findings demonstrated the potential of cardiac signals in detecting moderate hemorrhage. In future, we will analyze all the LBNP stages and investigate the feasibility of other physiological signals to develop a predictive machine learning model for early detection of hemorrhage.

Keywords: blood pressure, hemorrhage, lower-body negative pressure, LBNP, machine learning

Procedia PDF Downloads 167

Authors: Mohammad Ehson Rangiha, Bill Karakostas

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Social Business Process Management (SBPM) promises to overcome limitations of traditional BPM by allowing flexible process design and enactment through the involvement of users from a social community. This paper proposes a meta-model and architecture for socially driven business process management systems. It discusses the main facets of the architecture such as goal-based role assignment that combines social recommendations with user profile, and process recommendation, through a real example of a charity organization.

Keywords: business process management, goal-based modelling, process recommendation social collaboration, social BPM

Procedia PDF Downloads 494

Authors: Hammad Majeed, Hanna Knuutila, Magne Hillestad, Hallvard F. Svendsen

Abstract:

Formation of aerosols can cause serious complications in industrial exhaust gas CO2 capture processes. SO3 present in the flue gas can cause aerosol formation in an absorption based capture process. Small mist droplets and fog formed can normally not be removed in conventional demisting equipment because their submicron size allows the particles or droplets to follow the gas flow. As a consequence of this aerosol based emissions in the order of grams per Nm3 have been identified from PCCC plants. In absorption processes aerosols are generated by spontaneous condensation or desublimation processes in supersaturated gas phases. Undesired aerosol development may lead to amine emissions many times larger than what would be encountered in a mist free gas phase in PCCC development. It is thus of crucial importance to understand the formation and build-up of these aerosols in order to mitigate the problem. Rigorous modelling of aerosol dynamics leads to a system of partial differential equations. In order to understand mechanics of a particle entering an absorber an implementation of the model is created in Matlab. The model predicts the droplet size, the droplet internal variable profiles and the mass transfer fluxes as function of position in the absorber. The Matlab model is based on a subclass method of weighted residuals for boundary value problems named, orthogonal collocation method. The model comprises a set of mass transfer equations for transferring components and the essential diffusion reaction equations to describe the droplet internal profiles for all relevant constituents. Also included is heat transfer across the interface and inside the droplet. This paper presents results describing the basic simulation tool for the characterization of aerosols formed in CO2 absorption columns and gives examples as to how various entering droplets grow or shrink through an absorber and how their composition changes with respect to time. Below are given some preliminary simulation results for an aerosol droplet composition and temperature profiles.

Keywords: absorption columns, aerosol formation, amine emissions, internal droplet profiles, monoethanolamine (MEA), post combustion CO2 capture, simulation

Procedia PDF Downloads 246

Authors: Faiz Khan Mohammad, Saumya Ray Chaudhari, Raghunathan Rengaswamy, Swagatika Sahoo

Abstract:

Cholera caused by pathogenic gut bacteria Vibrio Cholerae (VC), is a major health problem in developing countries. Different strains of VC exhibit variable responses subject to different extracellular medium (Nag et al, Infect Immun, 2018). In this study, we present a new approach to model the variable VC responses in mono- and co-cultures, subject to continuously changing growth medium, which is otherwise difficult via simple FBA model. Nine VC strain and seven E. coli (EC) models were assembled and considered. A continuously changing medium is modelled using a new iterative-based controlled medium technique (ITC). The medium is appropriately prefixed with the VC model secretome. As the flux through the bacteria biomass increases secretes certain by-products. These products shall add-on to the medium, either deviating the nutrient potential or block certain metabolic components of the model, effectively forming a controlled feed-back loop. Different VC models were setup as monoculture of VC in glucose enriched medium, and in co-culture with VC strains and EC. Constrained to glucose enriched medium, (i) VC_Classical model resulted in higher flux through acidic secretome suggesting a pH change of the medium, leading to lowering of its biomass. This is in consonance with the literature reports. (ii) When compared for neutral secretome, flux through acetoin exchange was higher in VC_El tor than the classical models, suggesting El tor requires an acidic partner to lower its biomass. (iii) Seven of nine VC models predicted 3-methyl-2-Oxovaleric acid, mysirtic acid, folic acid, and acetate significantly affect corresponding biomass reactions. (iv) V. parhemolyticus and vulnificus were found to be phenotypically similar to VC Classical strain, across the nine VC strains. The work addresses the advantage of the ITC over regular flux balance analysis for modelling varying growth medium. Future expansion to co-cultures, potentiates the identification of novel interacting partners as effective cholera therapeutics.

Keywords: cholera, vibrio cholera El. tor, vibrio cholera classical, acetate

Procedia PDF Downloads 162

Authors: Olusola Omolola Olafuyi, Michael Coleman, Raj Kumar Singh Badhan

Abstract:

Introduction: Malaria in pregnancy has morbidity and mortality implication on both mother and unborn child. Piperaquine (PQ) based antimalarial treatment is emerging as a choice antimalarial for pregnant women in the face of resistance to current antimalarial treatment recommendation in pregnancy. Physiological and biochemical changes in pregnant women may affect the pharmacokinetics of the antimalarial drug in these. In malaria endemic regions other infectious diseases like HIV/AIDs are prevalent. Pregnant women who are co-infected with malaria and HIV/AID are at even more greater risk of death not only due to complications of the diseases but also due to drug-drug interactions (DDIs) between antimalarials (AMT) and antiretroviral (ARVs). In this study, physiologically based pharmacokinetic (PBPK) modelling was used to investigate the effect of physiological and biochemical changes on the impact of ARV mediated DDIs in pregnant women in three countries. Method: A PBPK model for PQ was developed on SimCYP® using published physicochemical and pharmacokinetic data of PQ from literature, this was validated in three customized population groups from Thailand, Sudan and Papua New Guinea with clinical data. Validation of PQ model was also done in presence of interaction with efavirenz (pre-validated on SimCYP®). Different albumin levels and pregnancy stages was simulated in the presence of interaction with standard doses of efavirenz and ritonavir. PQ day 7 concentration of 30ng/ml was used as the efficacy endpoint for PQ treatment.. Results: The median day 7 concentration of PQ remained virtually consistent throughout pregnancy and were satisfactory across the three population groups ranging from 26-34.1ng/ml; this implied the efficacy of PQ throughout pregnancy. DDI interaction with ritonavir and efavirenz resulted in modest effect on the day 7 concentrations of PQ with AUCratio ranging from 0.56-0.8 and 1.64-1.79 for efavirenz and ritonavir respectively over 10-40 gestational weeks, however, a reduction in human serum albumin level reflective of severe malaria resulted in significantly reduced the number of subjects attaining the PQ day 7 concentration in the presence of both DDIs. The model demonstrated that the DDI between PQ and ARV in pregnant women with different malaria severities can alter the pharmacokinetic of PQ.

Keywords: antiretroviral, malaria, piperaquine, pregnancy, physiologically-based pharmacokinetics

Procedia PDF Downloads 185