Search results for: shape prediction

Authors: Ghufran Ahmed, Muhammad Osama Siddiqui, Shahbaz Siddiqui, Rauf Ahmad Shams Malick, Faisal Khan, Mubashir Khan

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In this paper, a machine learning-based approach for early prediction of diseases in cows is proposed. Different ML algos are applied to extract useful patterns from the available dataset. Technology has changed today’s world in every aspect of life. Similarly, advanced technologies have been developed in livestock and dairy farming to monitor dairy cows in various aspects. Dairy cattle monitoring is crucial as it plays a significant role in milk production around the globe. Moreover, it has become necessary for farmers to adopt the latest early prediction technologies as the food demand is increasing with population growth. This highlight the importance of state-ofthe-art technologies in analyzing how important technology is in analyzing dairy cows’ activities. It is not easy to predict the activities of a large number of cows on the farm, so, the system has made it very convenient for the farmers., as it provides all the solutions under one roof. The cattle industry’s productivity is boosted as the early diagnosis of any disease on a cattle farm is detected and hence it is treated early. It is done on behalf of the machine learning output received. The learning models are already set which interpret the data collected in a centralized system. Basically, we will run different algorithms on behalf of the data set received to analyze milk quality, and track cows’ health, location, and safety. This deep learning algorithm draws patterns from the data, which makes it easier for farmers to study any animal’s behavioral changes. With the emergence of machine learning algorithms and the Internet of Things, accurate tracking of animals is possible as the rate of error is minimized. As a result, milk productivity is increased. IoT with ML capability has given a new phase to the cattle farming industry by increasing the yield in the most cost-effective and time-saving manner.

Keywords: IoT, machine learning, health care, dairy cows

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Authors: Lana Damaj, Vincent Laur, Azar Maalouf, Alexis Chevalier

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Matched termination is an important part of the passive waveguide components. It is typically used at the end of a waveguide transmission line to prevent reflections and improve signal quality. Waveguide terminations (loads) are commonly used in microwave and RF applications. In traditional microwave architectures, usually, waveguide termination consists of a standard rectangular waveguide made by a lossy resistive material, and ended by shorting metallic plate. These types of terminations are used, to dissipate the energy as heat. However, these terminations may increase the size and the weight of the overall system. New alternative solution consists in developing terminations based on 3D-printing of materials. Designing such terminations is very challenging since it should meet the requirements imposed by the system. These requirements include many parameters such as the absorption, the power handling capability in addition to the cost, the size and the weight that have to be minimized. 3D-printing is a shaping process that enables the production of complex geometries. It allows to find best compromise between requirements. In this paper, a comparison study has been made between different existing and new shapes of waveguide terminations. Indeed, 3D printing of absorbers makes it possible to study not only standard shapes (wedge, pyramid, tongue) but also more complex topologies such as exponential ones. These shapes have been designed and simulated using CST MWS®. The loads have been printed using the carbon-filled PolyLactic Acid, conductive PLA from ProtoPasta. Since the terminations has been characterized in the X-band (from 8GHz to 12GHz), the rectangular waveguide standard WR-90 has been selected. The classical wedge shape has been used as a reference. First, all loads have been simulated with the same length and two parameters have been compared: the absorption level (level of |S11|) and the dissipated power density. This study shows that the concave exponential pyramidal shape has the better absorption level and the convex exponential pyramidal shape has the better dissipated power density level. These two loads have been printed in order to measure their properties. A good agreement between the simulated and measured reflection coefficient has been obtained. Furthermore, a study of material structuring based on the honeycomb hexagonal structure has been investigated in order to vary the effective properties. In the final paper, the detailed methodology and the simulated and measured results will be presented in order to show how 3D-printing can allow controlling mass, weight, absorption level and power behaviour.

Keywords: additive manufacturing, electromagnetic composite materials, microwave measurements, passive components, power handling capacity (PHC), 3D-printing

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Authors: Ashish Dhamaniya, Vineet Jain, Rajesh Chouhan

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Traffic management is one of the gigantic issue in most of the urban roads in al-most all metropolitan cities in India. Speed is one of the critical traffic parameters for effective Intelligent Transportation System (ITS) implementation as it decides the arrival rate of vehicles on an intersection which are majorly the point of con-gestions. The study aimed to leverage Machine Learning (ML) models to produce precise predictions of speed on urban roadway links. The research objective was to assess how categorized traffic volume and road width, serving as variables, in-fluence speed prediction. Four tree-based regression models namely: Decision Tree (DT), Random Forest (RF), Extra Tree (ET), and Extreme Gradient Boost (XGB)are employed for this purpose. The models' performances were validated using test data, and the results demonstrate that Random Forest surpasses other machine learning techniques and a conventional utility theory-based model in speed prediction. The study is useful for managing the urban roadway network performance under mixed traffic conditions and effective implementation of ITS.

Keywords: stream speed, urban roads, machine learning, traffic flow

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Authors: Jeremy Jenrette, Z. Y. C. Liu, Pranav Chimote, Edward Fox, Trevor Hastie, Francesco Ferretti

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Suitable shark conservation depends on well-informed population assessments. Direct methods such as scientific surveys and fisheries monitoring are adequate for defining population statuses, but species-specific indices of abundance and distribution coming from these sources are rare for most shark species. We can rapidly fill these information gaps by boosting media-based remote monitoring efforts with machine learning and automation. We created a database of shark images by sourcing 24,546 images covering 219 species of sharks from the web application spark pulse and the social network Instagram. We used object detection to extract shark features and inflate this database to 53,345 images. We packaged object-detection and image classification models into a Shark Detector bundle. We developed the Shark Detector to recognize and classify sharks from videos and images using transfer learning and convolutional neural networks (CNNs). We applied these models to common data-generation approaches of sharks: boosting training datasets, processing baited remote camera footage and online videos, and data-mining Instagram. We examined the accuracy of each model and tested genus and species prediction correctness as a result of training data quantity. The Shark Detector located sharks in baited remote footage and YouTube videos with an average accuracy of 89\%, and classified located subjects to the species level with 69\% accuracy (n =\ eight species). The Shark Detector sorted heterogeneous datasets of images sourced from Instagram with 91\% accuracy and classified species with 70\% accuracy (n =\ 17 species). Data-mining Instagram can inflate training datasets and increase the Shark Detector’s accuracy as well as facilitate archiving of historical and novel shark observations. Base accuracy of genus prediction was 68\% across 25 genera. The average base accuracy of species prediction within each genus class was 85\%. The Shark Detector can classify 45 species. All data-generation methods were processed without manual interaction. As media-based remote monitoring strives to dominate methods for observing sharks in nature, we developed an open-source Shark Detector to facilitate common identification applications. Prediction accuracy of the software pipeline increases as more images are added to the training dataset. We provide public access to the software on our GitHub page.

Keywords: classification, data mining, Instagram, remote monitoring, sharks

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Authors: Fedda Y. Alzoubi, Ihsan A. Aljarrah

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Silver nanoparticles (AgNPs) are one of the most vital and fascinating nanomaterials among several metallic nanoparticles that are involved in different applications, especially in biomedical applications. Samples of different alkaline water were prepared in order to study the effect of alkalinity of water on the optical properties, size, and morphology of colloidal AgNPs prepared according to the chemical reduction method using the prepared water samples. Ultraviolet-Visible spectrophotometer, Zeta-sizer, and Scanning electron microscope (SEM) have been utilized to carry out this study. Absorption spectra AgNPs in different alkaline water show a surface Plasmon resonance (SPR) peak at the wavelength of 420 nm. The position of this peak is sensitive to the shape of the particles, and in our case, it indicates that the particles are spherical. As the alkalinity increases, the intensity of the SPR peak decreases, indicating the aggregation of particles. Zeta-sizer measurements show that the average diameter for AgNPs in pure water is found to be 53.51 nm, and this value increases as the alkalinity increases. Zeta potential values of samples show that the negatively coated particles are stable in the solution. SEM images insure the spherical shape of the prepared nanoparticles and show that as the alkalinity increases the particles aggregate into larger particles.

Keywords: aggregation, alkalinity, colloid, nanoparticle

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Authors: Franziska Moser, Martin Trautz, Anna-Lena Beger, Manuel Löwer, Jörg Feldhusen, Jürgen Prell, Alexandra Wormit, Björn Usadel, Christoph Kämpfer, Thomas-Benjamin Seiler, Henner Hollert

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The demand for bio-based materials and components in architecture has increased in recent years due to society’s heightened environmental awareness. Nowadays, most components are being developed via a substitution approach, which aims at replacing conventional components with natural alternatives who are then being processed, shaped and manufactured to fit the desired application. This contribution introduces a novel approach to the development of bio-based products that decreases resource consumption and increases recyclability. In this approach, natural organisms like plants or trees are not being used in a processed form, but grow into a near net shape before then being harvested and utilized as building components. By minimizing the conventional production steps, the amount of resources used in manufacturing decreases whereas the recyclability increases. This paper presents the approach of technical product harvesting, explains the theoretical basis as well as the matching process of product requirements and biological properties, and shows first results of the growth manipulation studies.

Keywords: design with nature, eco manufacturing, sustainable construction materials, technical product harvesting

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Authors: Dinda A. Utami, Muhammad N. Alfarizi

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The purpose of this study was to determine the ability of zeolite catalyst for the trans- esterification reaction in biodiesel production from animal fat. The ability of the zeolite as a catalyst is determined by the structure and composition of the zeolite. An important factor that determines the properties of zeolites in catalysis includes adsorption capability to the compound of the reactants. Zeolites with a pore size of specific properties selectively adsorbing molecules. A molecule can be adsorbed by either the zeolite cavities if the size and shape of the molecule in accordance with the size and shape of the cavity in the zeolite. At this time, it is common to use homogeneous catalysts for biodiesel. We know these catalysts have some disadvantages in its use. Such as the difficulty of separation of the product with the catalyst, the generation of waste that is harmful to the environment due to residual catalysts can’t be reused, and the difficulty of handling and storage. But nowadays, solid catalyst developed technically to improve the efficiency of biodiesel production. In this case of study, we used trans-esterification process wherein the triglyceride is reacted with an alcohol with zeolite as a solid catalyst and it will produce biodiesel and glycerol as a byproduct. Development of solid catalyst seems to be the perfect solution to address the problems associated with homogeneous catalysts.

Keywords: biodiesel, animal fat, trans esterification, zeolite catalyst

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Authors: Samah Said, Muhsin Elie Rahhal

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Due to the COVID-19 pandemic, disposable plastic-based face masks were excessively used worldwide. Therefore, the production and consumption rates of these masks were significantly brought up, which led to severe environmental problems. The main purpose of this research is to test the possibility of reinforcing soil deposits with mask fibers to reuse pandemic-generated waste materials. When testing the compaction properties, the sand was reinforced with a fiber content that increased from 0% to 0.5%, with successive small increments of 0.1%. The optimum content of 0.1% remarkably increased the maximum dry density of the soil and dropped its optimum moisture content. Add to that, it was noticed that 15 mm and rectangular chips were, respectively, the optimum fiber length and shape to maximize the improvement of the sand compaction properties. Regarding the shear strength, fiber contents of 0.1%, 0.25%, and 0.5% were adopted. The direct shear tests have shown that the highest enhancement was observed for the optimum fiber content of 0.25%. Similarly to compaction tests, 15 mm and rectangular chips were respectively the optimum fiber length and shape to extremely enhance the shear resistance of the tested sand.

Keywords: COVID-19, mask fibers, compaction properties, soil reinforcement, shear resistance

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Authors: Thulodin Mat Lazim, Aminuddin Saat, Ammar Fakhir Abdulwahid, Zaid Sattar Kareem

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Film cooling is one of the cooling systems investigated for the application to gas turbine blades. Gas turbines use film cooling in addition to turbulence internal cooling to protect the blades outer surface from hot gases. The present study concentrates on the numerical investigation of film cooling performance for a row of twisted cylindrical holes in modern turbine blade. The adiabatic film effectiveness and the heat transfer coefficient are determined numerical on a flat plate downstream of a row of inclined different cross section area hole exit by using Computational Fluid Dynamics (CFD). The swirling motion of the film coolant was induced the twisted angle of film cooling holes, which inclined an angle of α toward the vertical direction and surface of blade turbine. The holes angle α of the impingement mainstream was changed from 90°, 65°, 45°, 30° and 20°. The film cooling effectiveness on surface of blade turbine wall was measured by using 3D Computational Fluid Dynamics (CFD). Results showed that the effectiveness of rectangular twisted hole has the effectiveness among other cross section area of the hole at blowing ratio (0.5, 1, 1.5 and 2).

Keywords: turbine blade cooling, film cooling, geometry shape of hole, turbulent flow

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Authors: Andreas Livera, Spyros Theocharides, Michalis Florides, Charalambos Anastassiou

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A main challenge in the quest for ensuring quality of operation, especially for photovoltaic (PV) systems, is to safeguard the reliability and optimal performance by detecting and diagnosing potential failures and performance losses at early stages or before the occurrence through real-time monitoring, supervision, fault detection, and predictive maintenance. The purpose of this work is to present the functionalities and results related to the development and validation of a software platform for PV assets diagnosis and maintenance. The platform brings together proprietary hardware sensors and software algorithms to enable the early detection and prediction of the most common and critical faults in PV systems. It was validated using field measurements from operating PV systems. The results showed the effectiveness of the platform for detecting faults and losses (e.g., inverter failures, string disconnections, and potential induced degradation) at early stages, forecasting PV power production while also providing recommendations for maintenance actions. Increased PV energy yield production and revenue can be thus achieved while also minimizing operation and maintenance (O&M) costs.

Keywords: failure detection and prediction, operation and maintenance, performance monitoring, photovoltaic, platform, recommendations, predictive maintenance

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Authors: Angga S. Fajar, Y. Takahashi, J. Kiyono, S. Sawada

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The structural system concept of RC pier accompanied with multi sliding friction damping mechanism was developed based on numerical analysis approach. However in the implementation, to make design for such kind of this structural system consumes a lot of effort in case high of complexity. During making design, the special behaviors of this structural system should be considered including flexible small deformation, sufficient elastic deformation capacity, sufficient lateral force resistance, and sufficient energy dissipation. The confinement distribution of friction devices has significant influence to its. Optimization and prediction with multi function regression of this structural system expected capable of providing easier and simpler design method. The confinement distribution of friction devices is optimized with SNOPT in Opensees, while some design variables of the structure are predicted using multi function regression of ANN. Based on the optimization and prediction this structural system is able to be designed easily and simply.

Keywords: RC Pier, multi sliding friction device, optimal design, flexible small deformation

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Authors: Jay J. Vora, Vishvesh J. Badheka

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This work attempts to investigate the effect of oxide fluxes on 6mm thick Reduced Activation ferritic/martensitic steels (RAFM) during Activated TIG (A-TIG) welding. Six different fluxes Al₂O₃, Co₃O₄, CuO, HgO, MoO₃, and NiO were mixed with methanol for conversion into paste and bead-on-plate experiments were then carried out. This study, systematically investigates the influence of oxide-based flux powder and carrier solvent composition on the weld bead shape, geometric shape of weld bead and dominant depth enhancing mechanism in tungsten inert gas (TIG) welding of reduced activation ferritic/martensitic (RAFM) steel. It was inferred from the study that flux Co₃O₄ and MoO₃ imparted full and secure (more than 6mm) penetration with methanol owing to dual mechanism of reversed Marangoni and arc construction. The use of methanol imparted good spreadabilty and coverability and ultimately higher peak temperatures were observed with its use owing to stronger depth enhancing mechanisms than use of acetone with same oxide fluxes and welding conditions.

Keywords: A-TIG, flux, oxides, penetration, RAFM, temperature, welding

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Authors: Mohsen Bahrami, Banafsheh Nikmehr, Yueqiang Song, Anuradha Koduru, Ayse K. Vuruskan, Hongkun Lu, Tamer M. Yalcinkaya

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In recent years, we have witnessed an explosion of studies aimed at using a combination of artificial intelligence (AI) and time-lapse imaging data on embryos to improve IVF outcomes. However, despite promising results, no study has used a matched cohort of transferred embryos which only differ in pregnancy outcome, i.e., embryos from a single clinic which are similar in parameters, such as: morphokinetic condition, patient age, and overall clinic and lab performance. Here, we used time-lapse data on embryos with known pregnancy outcomes to see if the rich spatiotemporal information embedded in this data would allow the prediction of the pregnancy outcome regardless of such critical parameters. Methodology—We did a retrospective analysis of time-lapse data from our IVF clinic utilizing Embryoscope 100% of the time for embryo culture to blastocyst stage with known clinical outcomes, including live birth vs nonpregnant (embryos with spontaneous abortion outcomes were excluded). We used time-lapse data from 200 elective single transfer embryos randomly selected from January 2019 to June 2021. Our sample included 100 embryos in each group with no significant difference in patient age (P=0.9550) and morphokinetic scores (P=0.4032). Data from all patients were combined to make a 4th order tensor, and feature extraction were subsequently carried out by a tensor decomposition methodology. The features were then used in a machine learning classifier to classify the two groups. Major Findings—The performance of the model was evaluated using 100 random subsampling cross validation (train (80%) - test (20%)). The prediction accuracy, averaged across 100 permutations, exceeded 80%. We also did a random grouping analysis, in which labels (live birth, nonpregnant) were randomly assigned to embryos, which yielded 50% accuracy. Conclusion—The high accuracy in the main analysis and the low accuracy in random grouping analysis suggest a consistent spatiotemporal pattern which is associated with pregnancy outcomes, regardless of patient age and embryo morphokinetic condition, and beyond already known parameters, such as: early cleavage or early blastulation. Despite small samples size, this ongoing analysis is the first to show the potential of AI methods in capturing the complex morphokinetic changes embedded in embryo time-lapse data, which contribute to successful pregnancy outcomes, regardless of already known parameters. The results on a larger sample size with complementary analysis on prediction of other key outcomes, such as: euploidy and aneuploidy of embryos will be presented at the meeting.

Keywords: IVF, embryo, machine learning, time-lapse imaging data

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Authors: Felix Eyben, Simon Schaffrath, Markus Feldmann

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A building should maximize the potential for use through its design. Therefore, flexible use is always important when designing a steel structure. To create flexibility, steel beams with web openings are increasingly used, because these offer the advantage that cables, pipes and other technical equipment can easily be routed through without detours, allowing for more space-saving and aesthetically pleasing construction. This can also significantly reduce the height of ceiling systems. Until now, beams with web openings were not explicitly considered in the European standard. However, this is to be done with the new EN 1993-1-13, in which design rules for different opening forms are defined. In order to further develop the design concepts, beams with web openings under bending are therefore to be investigated in terms of damage mechanics as part of a German national research project aiming to optimize the verifications for steel structures based on a wider database and a validated damage prediction. For this purpose, first, fundamental factors influencing the load-bearing behavior of girders with web openings under bending load were investigated numerically without taking material damage into account. Various parameter studies were carried out for this purpose. For example, the factors under study were the opening shape, size and position as well as structural aspects as the span length, arrangement of stiffeners and loading situation. The load-bearing behavior is evaluated using resulting load-deformation curves. These results are compared with the design rules and critically analyzed. Experimental tests are also planned based on these results. Moreover, the implementation of damage mechanics in the form of the modified Bai-Wierzbicki model was examined. After the experimental tests will have been carried out, the numerical models are validated and further influencing factors will be investigated on the basis of parametric studies.

Keywords: damage mechanics, finite element, steel structures, web openings

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Authors: Monireh Ahmadi Bani, Adel Khorramrouz, Lalenoor Morvarid, Bagheri Mahtab

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Background:- Foot disorders are common in musculoskeletal problems. Plantar pressure distribution measurement is one the most important part of foot disorders diagnosis for quantitative analysis. However, the association of plantar pressure and foot disorders is not clear. With the growth of dataset and machine learning methods, the relationship between foot disorders and plantar pressures can be detected. Significance of the study:- The purpose of this study was to predict the probability of common foot disorders based on peak plantar pressure distribution and center of pressure during walking. Methodologies:- 2323 participants were assessed in a foot therapy clinic between 2015 and 2021. Foot disorders were diagnosed by an experienced physician and then they were asked to walk on a force plate scanner. After the data preprocessing, due to the difference in walking time and foot size, we normalized the samples based on time and foot size. Some of force plate variables were selected as input to a deep neural network (DNN), and the probability of any each foot disorder was measured. In next step, we used support vector machine (SVM) and run dataset for each foot disorder (classification of yes or no). We compared DNN and SVM for foot disorders prediction based on plantar pressure distributions and center of pressure. Findings:- The results demonstrated that the accuracy of deep learning architecture is sufficient for most clinical and research applications in the study population. In addition, the SVM approach has more accuracy for predictions, enabling applications for foot disorders diagnosis. The detection accuracy was 71% by the deep learning algorithm and 78% by the SVM algorithm. Moreover, when we worked with peak plantar pressure distribution, it was more accurate than center of pressure dataset. Conclusion:- Both algorithms- deep learning and SVM will help therapist and patients to improve the data pool and enhance foot disorders prediction with less expense and error after removing some restrictions properly.

Keywords: deep neural network, foot disorder, plantar pressure, support vector machine

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Authors: Elham Delzendeh, Song Wu, Mustafa Al-Adhami, Rima Alaaeddine

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Over the last 15 years, prediction of energy consumption has become a common practice and necessity at different stages of the building’s lifecycle, particularly, at the design and post-occupancy stages for planning and maintenance purposes. This is due to the ever-growing response of governments to address sustainability and reduction of CO₂ emission in the building sector. However, there is a level of uncertainty in the estimation of energy consumption in buildings. The accuracy of energy consumption predictions is directly related to the precision of the initial inputs used in the energy assessment process. In this study, multiple cases of large non-residential buildings at design, construction, and post-occupancy stages are investigated. The energy consumption process and inputs, and the actual and predicted energy consumption of the cases are analysed. The findings of this study have pointed out and evidenced various parameters that cause uncertainty in the prediction of energy consumption in buildings such as modelling, location data, and occupant behaviour. In addition, unavailability and insufficiency of energy-consumption-related inputs at different stages of the building’s lifecycle are classified and categorized. Understanding the roots of uncertainty in building energy analysis will help energy modellers and energy simulation software developers reach more accurate energy consumption predictions in buildings.

Keywords: building lifecycle, efficiency, energy analysis, energy performance, uncertainty

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Authors: Siham G. Farag

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The main objective of this paper is to provide a new methodology for road safety assessment in Oman through the development of suitable accident prediction models. GLM technique with Poisson or NBR using SAS package was carried out to develop these models. The paper utilized the accidents data of 31 un-signalized T-intersections during three years. Five goodness-of-fit measures were used to assess the overall quality of the developed models. Two types of models were developed separately; the flow-based models including only traffic exposure functions, and the full models containing both exposure functions and other significant geometry and traffic variables. The results show that, traffic exposure functions produced much better fit to the accident data. The most effective geometric variables were major-road mean speed, minor-road 85th percentile speed, major-road lane width, distance to the nearest junction, and right-turn curb radius. The developed models can be used for intersection treatment or upgrading and specify the appropriate design parameters of T- intersections. Finally, the models presented in this thesis reflect the intersection conditions in Oman and could represent the typical conditions in several countries in the middle east area, especially gulf countries.

Keywords: accidents prediction models (APMs), generalized linear model (GLM), T-intersections, Oman

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Authors: M. Ciminello, I. Dimino, S. Ameduri, A. Concilio

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Improving aircraft's efficiency is one of the key elements of Aeronautics. Modern aircraft possess many advanced functions, such as good transportation capability, high Mach number, high flight altitude, and increasing rate of climb. However, no aircraft has a possibility to reach all of this optimized performance in a single airframe configuration. The aircraft aerodynamic efficiency varies considerably depending on the specific mission and on environmental conditions within which the aircraft must operate. Structures that morph their shape in response to their surroundings may at first seem like the stuff of science fiction, but take a look at nature and lots of examples of plants and animals that adapt to their environment would arise. In order to ensure both the controllable and the static robustness of such complex structural systems, a monitoring network is aimed at verifying the effectiveness of the given control commands together with the elastic response. In order to achieve this kind of information, the use of FBG sensors network is, in this project, proposed. The sensor network is able to measure morphing structures shape which may show large, global displacements due to non-standard architectures and materials adopted. Chord -wise variations may allow setting and chasing the best layout as a function of the particular and transforming reference state, always targeting best aerodynamic performance. The reason why an optical sensor solution has been selected is that while keeping a few of the contraindication of the classical systems (like cabling, continuous deployment, and so on), fibre optic sensors may lead to a dramatic reduction of the wires mass and weight thanks to an extreme multiplexing capability. Furthermore, the use of the ‘light’ as ‘information carrier’, permits dealing with nimbler, non-shielded wires, and avoids any kind of interference with the on-board instrumentation. The FBG-based transducers, herein presented, aim at monitoring the actual shape of adaptive trailing edge. Compared to conventional systems, these transducers allow more fail-safe measurements, by taking advantage of a supporting structure, hosting FBG, whose properties may be tailored depending on the architectural requirements and structural constraints, acting as strain modulator. The direct strain may, in fact, be difficult because of the large deformations occurring in morphing elements. A modulation transducer is then necessary to keep the measured strain inside the allowed range. In this application, chord-wise transducer device is a cantilevered beam sliding trough the spars and copying the camber line of the ATE ribs. FBG sensors array position are dimensioned and integrated along the path. A theoretical model describing the system behavior is implemented. To validate the design, experiments are then carried out with the purpose of estimating the functions between rib rotation and measured strain.

Keywords: fiber optic sensor, morphing structures, strain sensor, shape reconstruction

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Authors: Saurabh Kumar

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In the fiercely competitive landscape of e-commerce, understanding and mitigating customer churn has become paramount for sustainable business growth. This paper presents a thorough investigation into the application of machine learning techniques for churn prediction in e-commerce, aiming to provide actionable insights for businesses seeking to enhance customer retention strategies. We conduct a comparative study of various machine learning algorithms, including traditional statistical methods and ensemble techniques, leveraging a rich dataset sourced from Kaggle. Through rigorous evaluation, we assess the predictive performance, interpretability, and scalability of each method, elucidating their respective strengths and limitations in capturing the intricate dynamics of customer churn. We identified the XGBoost classifier to be the best performing. Our findings not only offer practical guidelines for selecting suitable modeling approaches but also contribute to the broader understanding of customer behavior in the e-commerce domain. Ultimately, this research equips businesses with the knowledge and tools necessary to proactively identify and address churn, thereby fostering long-term customer relationships and sustaining competitive advantage.

Keywords: customer churn, e-commerce, machine learning techniques, predictive performance, sustainable business growth

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Authors: Ya Ting Tang

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Social learning theory has told us that the cognitions of gender roles come from learning. Thus, the images of gender roles which media describes will shape our cognitions. Taiwan and Japan are both in the East Asian cultural Sphere, and more or less influenced by the traditional Chinese culture. But our social structure and changes must be different. Others, the study also concerns that, with the rise of female consciousness in society, whether the female stereotype in drama of Taiwan and Japan are improved. This research first uses content analysis to analyze drama of Taiwan and Japan in 2003 and 2013 on how to shape female roles. Then use text analysis to conduct a qualitative analysis. Result of this study showed that drama on how to depict women indeed have changed, women are no longer just talk about love, but can serve as president or doctor, and show its mettle in the workplace. In Japanese drama, the female roles have diverse occupation than Taiwanese drama, and not just a background character set. But in most Taiwanese drama, female roles are given a career, but it always put emphasis on women emotionally. To include, although the stereotype in the drama of Taiwan and Japan are improved, female will still be derided due to their ages, love or marriage situations. Taiwanese drama must depict the occupation of female more diverse and let the female roles have more space to play, rather than focusing on romance which women of any occupation can have.

Keywords: female images, stereotype, TV drama, gender roles

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Authors: Niladri Sekhar Barma, Satish Dhandole

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Damage identification in the context of safety, nowadays, has become a fundamental research interest area in the field of mechanical, civil, and aerospace engineering structures. The following research is aimed to identify damage in a mechanical beam structure and quantify the severity or extent of damage in terms of loss of stiffness, and obtain an updated analytical Finite Element (FE) model. An FE model is used for analysis, and the location of damage for single and multiple damage cases is identified numerically using the modal strain energy method and mode shape curvature method. Experimental data has been acquired with the help of an accelerometer. Fast Fourier Transform (FFT) algorithm is applied to the measured signal, and subsequently, post-processing is done in MEscopeVes software. The two sets of data, the numerical FE model and experimental results, are compared to locate the damage accurately. The extent of the damage is identified via modal frequencies using a mixed numerical-experimental technique. Mode shape comparison is performed by Modal Assurance Criteria (MAC). The analytical FE model is adjusted by the direct method of model updating. The same study has been extended to some real-life structures such as plate and GARTEUR structures.

Keywords: damage identification, damage quantification, damage detection using modal analysis, structural damage identification

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Authors: Aditya, Ganapati Joshi, Vinod Kumar

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IN THE MODERN-WARFARE ERA, THE PRIME REQUIREMENT IS A HIGH SPEED AND MACH NUMBER. WHEN THE MISSILES STRIKE IN THE HYPERSONIC REGIME THE OPPONENT CAN DETECT IT WITH THE ANTI-DEFENSE SYSTEM BUT CAN NOT STOP IT FROM CAUSING DAMAGE. SO, TO ACHIEVE THE SPEEDS OF THIS LEVEL THERE ARE TWO ENGINES THAT ARE AVAILABLE WHICH CAN WORK IN THIS REGION ARE RAMJET AND SCRAMJET. THE PROBLEM WITH RAMJET STARTS TO OCCUR WHEN MACH NUMBER EXCEEDS 4 AS THE STATIC PRESSURE AT THE INLET BECOMES EQUAL TO THE EXIT PRESSURE. SO, SCRAMJET ENGINE DEALS WITH THIS PROBLEM AS IT NEARLY HAS THE SAME WORKING BUT HERE THE FLOW IS NOT MUCH SLOWED DOWN AS COMPARED TO RAMJET IN THE DIFFUSER BUT IT SUFFERS FROM THE PROBLEMS SUCH AS INLET BUZZ, THERMAL CHOCKING, MIXING OF FUEL AND OXIDIZER, THERMAL HEATING, AND MANY MORE. HERE THE NEW ENGINE IS DEVELOPED ON THE SAME PRINCIPLE AS THE SCRAMJET ENGINE BUT BURNING HAPPENS DUE TO DETONATION INSTEAD OF DEFLAGRATION. THE PROBLEM WITH THE ENGINE STARTS WHEN THE MACH NUMBER BECOMES VARIABLE AND THE INLET GEOMETRY IS FIXED AND THIS LEADS TO INLET SPILLAGE WHICH WILL AFFECT THE THRUST ADVERSELY. SO, HERE ADAPTIVE INLET IS MADE OF SHAPE MEMORY ALLOYS WHICH WILL ENHANCE THE INLET MASS FLOW RATE AS WELL AS THRUST.

Keywords: detonation, ramjet engine, shape memory alloy, ignition delay, shock-boundary layer interaction, eddy dissipation, asymmetric nozzle

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Authors: Meng H. Lean, Wei-Ping L. Chu

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The effects of ferroelectric nanofiller size, shape, loading, and polarization, on bipolar charge injection, transport, and recombination through amorphous and semicrystalline polymers are studied. A 3D particle-in-cell model extends the classical electrical double layer representation to treat ferroelectric nanoparticles. Metal-polymer charge injection assumes Schottky emission and Fowler-Nordheim tunneling, migration through field-dependent Poole-Frenkel mobility, and recombination with Monte Carlo selection based on collision probability. A boundary integral equation method is used for solution of the Poisson equation coupled with a second-order predictor-corrector scheme for robust time integration of the equations of motion. The stability criterion of the explicit algorithm conforms to the Courant-Friedrichs-Levy limit. Trajectories for charge that make it through the film are curvilinear paths that meander through the interspaces. Results indicate that charge transport behavior depends on nanoparticle polarization with anti-parallel orientation showing the highest leakage conduction and lowest level of charge trapping in the interaction zone. Simulation prediction of a size range of 80 to 100 nm to minimize attachment and maximize conduction is validated by theory. Attached charge fractions go from 2.2% to 97% as nanofiller size is decreased from 150 nm to 60 nm. Computed conductivity of 0.4 x 1014 S/cm is in agreement with published data for plastics. Charge attachment is increased with spheroids due to the increase in surface area, and especially so for oblate spheroids showing the influence of larger cross-sections. Charge attachment to nanofillers and nanocrystallites increase with vol.% loading or degree of crystallinity, and saturate at about 40 vol.%.

Keywords: nanocomposites, nanofillers, electrical double layer, bipolar charge transport

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Authors: Saeed Pourhassan, Nastaran Maghbouli

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Statement of the problem: The relationship between obesity and cardiovascular diseases has been studied widely(1). The distribution of fat tissue gained attention in relation to cardiovascular risk factors during lang-time research (2). American College of Cardiology/American Heart Association (ACC/AHA) is widely and the most reliable tool to be used as a cardiovascular risk (CVR) assessment tool(3). This study aimed to determine which anthropometric index is better in discrimination of high CVR patients from low risks using ACC/AHA score in addition to finding the best index as a CVR predictor among both genders in different races and countries. Methodology & theoretical orientation: The literature in PubMed, Scopus, Embase, Web of Science, and Google Scholar were searched by two independent investigators using the keywords "anthropometric indices," "cardiovascular risk," and "obesity." The search strategy was limited to studies published prior to Jan 2022 as full-texts in the English language. Studies using ACC/AHA risk assessment tool as CVR and those consisted at least 2 anthropometric indices (ancient ones and novel ones) are included. Study characteristics and data were extracted. The relative risks were pooled with the use of the random-effect model. Analysis was repeated in subgroups. Findings: Pooled relative risk for 7 studies with 16,348 participants were 1.56 (1.35-1.72) for BMI, 1.67(1.36-1.83) for WC [waist circumference], 1.72 (1.54-1.89) for WHR [waist-to-hip ratio], 1.60 (1.44-1.78) for WHtR [waist-to-height ratio], 1.61 (1.37-1.82) for ABSI [A body shape index] and 1.63 (1.32-1.89) for CI [Conicity index]. Considering gender, WC among females and WHR among men gained the highest RR. The heterogeneity of studies was moderate (α²: 56%), which was not decreased by subgroup analysis. Some indices such as VAI and LAP were evaluated just in one study. Conclusion & significance: This meta-analysis showed WHR could predict CVR better in comparison to BMI or WHtR. Some new indices like CI and ABSI are less accurate than WHR and WC. Among women, WC seems to be a better choice to predict cardiovascular disease risk.

Keywords: obesity, cardiovascular disease, risk assessment, anthropometric indices

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Authors: Hira Lal Gope, Hidekazu Fukai

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The aim of this study is to develop a system which can identify and sort peaberries automatically at low cost for coffee producers in developing countries. In this paper, the focus is on the classification of peaberries and normal coffee beans using image processing and machine learning techniques. The peaberry is not bad and not a normal bean. The peaberry is born in an only single seed, relatively round seed from a coffee cherry instead of the usual flat-sided pair of beans. It has another value and flavor. To make the taste of the coffee better, it is necessary to separate the peaberry and normal bean before green coffee beans roasting. Otherwise, the taste of total beans will be mixed, and it will be bad. In roaster procedure time, all the beans shape, size, and weight must be unique; otherwise, the larger bean will take more time for roasting inside. The peaberry has a different size and different shape even though they have the same weight as normal beans. The peaberry roasts slower than other normal beans. Therefore, neither technique provides a good option to select the peaberries. Defect beans, e.g., sour, broken, black, and fade bean, are easy to check and pick up manually by hand. On the other hand, the peaberry pick up is very difficult even for trained specialists because the shape and color of the peaberry are similar to normal beans. In this study, we use image processing and machine learning techniques to discriminate the normal and peaberry bean as a part of the sorting system. As the first step, we applied Deep Convolutional Neural Networks (CNN) and Support Vector Machine (SVM) as machine learning techniques to discriminate the peaberry and normal bean. As a result, better performance was obtained with CNN than with SVM for the discrimination of the peaberry. The trained artificial neural network with high performance CPU and GPU in this work will be simply installed into the inexpensive and low in calculation Raspberry Pi system. We assume that this system will be used in under developed countries. The study evaluates and compares the feasibility of the methods in terms of accuracy of classification and processing speed.

Keywords: convolutional neural networks, coffee bean, peaberry, sorting, support vector machine

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Authors: Bojan Najdenov, Danco Davcev

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Reduction of traffic congestions and the effects of pollution and waste of resources that come with them has been a big challenge in the past decades. Having reliable systems to facilitate the process of modeling and prediction of traffic conditions would not only reduce the environmental pollution, but will also save people time and money. Social networks play big role of people’s lives nowadays providing them means of communicating and sharing thoughts and ideas, that way generating huge knowledge bases by crowdsourcing. In addition to that, crowdsourcing as a concept provides mechanisms for fast and relatively reliable data generation and also many services are being used on regular basis because they are mainly powered by the public as main content providers. In this paper we present the Social-NETS-Traffic-Control System (SNTCS) that should serve as a facilitator in the process of modeling and prediction of traffic congestions. The main contribution of our system is to integrate data from social networks as Twitter and also implements a custom created crowdsourcing subsystem with which users report traffic conditions using an android application. Our first experience of the usage of the system confirms that the integrated approach allows easy extension of the system with other social networks and represents a very useful tool for traffic control.

Keywords: traffic, congestion reduction, crowdsource, social networks, twitter, android

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Authors: Amartya Hatua, Trung Nguyen, Andrew Sung

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In this paper, we study the information diffusion process on Twitter as a multivariate time series problem. Our model concerns three measures (volume, network influence, and sentiment of tweets) based on 10 features, and we collected 27 million tweets to build our information diffusion time series dataset for analysis. Then, different time series clustering techniques with Dynamic Time Warping (DTW) distance were used to identify different patterns of information diffusion. Finally, we built the information diffusion prediction models for new hashtags which comprise two phrases: The first phrase is recognizing the pattern using k-NN with DTW distance; the second phrase is building the forecasting model using the traditional Autoregressive Integrated Moving Average (ARIMA) model and the non-linear recurrent neural network of Long Short-Term Memory (LSTM). Preliminary results of performance evaluation between different forecasting models show that LSTM with clustering information notably outperforms other models. Therefore, our approach can be applied in real-world applications to analyze and predict the information diffusion characteristics of selected topics or memes (hashtags) in Twitter.

Keywords: ARIMA, DTW, information diffusion, LSTM, RNN, time series clustering, time series forecasting, Twitter

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Authors: Ravi Soni, Irfan Pathan, Manish Pande

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The current product development process needs simultaneous consideration of different physics. The performance of the product needs to be considered under both structural and fluid loads. Examples include ducts and valves where structural behavior affects fluid motion and vice versa. Simulation of fluid-structure interaction involves modeling interaction between moving components and the fluid flow. In these scenarios, it is difficult to calculate the damping provided by fluid flow because of dynamic motions of components and the transient nature of the flow. Abaqus Explicit offers general capabilities for modeling fluid-structure interaction with the Coupled Eulerian-Lagrangian (CEL) method. The Coupled Eulerian-Lagrangian technique has been used to simulate fluid spillage through fuel valves during dynamic closure events. The technique to simulate pressure drops across Eulerian domains has been developed using stagnation pressure. Also, the fluid flow is calculated considering material flow through elements at the outlet section of the valves. The methodology has been verified on Eaton products and shows a good correlation with the test results.

Keywords: Coupled Eulerian-Lagrangian Technique, fluid structure interaction, spillage prediction, stagnation pressure

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Authors: S. H. Chew, Z. X. Eng, K. E. Chuah, T. Y. Lim, H. M. A. Yim

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In recent years, geotextile tube has been widely used in the hydraulic engineering and dewatering industry. To construct a stable containment bund with geotextile tubes, the sand slurry is always the preference infilling material. However, the shortage of sand supply posts a problem in Singapore to adopt this construction method in the actual construction of long containment bund. Hence, utilizing the soft dredged clay or the excavated soft clay as the infilling material of geotextile tubes has a great economic benefit. There are any technical issues with using this soft clayey material as infilling material, especially on the excessive settlement and stability concerns. To minimize the shape deformation and settlement of geotextile tube associated with the use of this soft clay infilling material, a modified innovative infilling material is proposed – lightly cemented soft clay. The preliminary laboratory studies have shown that the dewatering mechanism via geotextile material of the tube skin, and the introduction of cementitious chemical action of the lightly cemented soft clay will accelerate the consolidation and improve the shear strength of infill material. This study aims to extend the study by conducting a pilot test of the geotextile tube filled with lightly cemented clay. This study consists of testing on a series of miniature geo-tubes and two full-size geotextile tube. In the miniature geo-tube tests, a number of small scaled-down size of geotextile tubes were filled with cemented clay (at water content of 150%) with cement content of 0% to 8% (by weight). The shear strength development of the lightly cemented clay under dewatering mechanism was evaluated using a modified in-situ Cone Penetration Test (CPT) at 0 days, 3 days, 7 days and 28 days after the infilling. The undisturbed soil samples of lightly cemented infilled clay were also extracted at 3-days and 7-days for triaxial tests and evaluation of final water content. The results suggested that the geotextile tubes filled with un-cemented soft clay experienced very significant shape change over the days (as control test). However, geotextile mini-tubes filled with lightly cemented clay experienced only marginal shape changed, even that the strength development of this lightly cemented clay inside the tube may not show significant strength gain at the early stage. The shape stability is believed to be due to the confinement effect of the geotextile tube with clay at non-slurry state. Subsequently, a full-scale instrumented geotextile tube filled with lightly cemented clay was performed. The extensive results of strain gauges and pressure transducers installed on this full-size geotextile tube demonstrated a substantial mobilization of tensile forces on the geotextile skin corresponding to the filling activity and the subsequent dewatering stage. Shape change and the in-fill material strength development was also monitored. In summary, the construction of containment bund with geotextile tube filled with lightly cemented clay is found to be technically feasible and stable with the use of the sufficiently strong (i.e. adequate tensile strength) geotextile tube, the adequate control on the dosage of cement content, and suitable water content of infilling soft clay material.

Keywords: cemented clay, containment bund, dewatering, geotextile tube

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Authors: Hasan Furkan Kurt, Tugba Nur Atabey, Onat Cavit Dereli, Ahmad Salmanogli, H. Selcuk Gecim

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In the article, the main goal is to study the effect of the plasmonic properties on the photocurrent generated by a photodetector. Fundamentally, a typical photodetector is designed and simulated using the finite element methods. To utilize the plasmonic effect, gold nanoparticles with different shape, size and morphology are buried into the intrinsic region. Plasmonic effect is arisen through the interaction of the incoming light with nanoparticles by which electrical properties of the photodetector are manipulated. In fact, using plasmonic nanoparticles not only increases the absorption bandwidth of the incoming light, but also generates a high intensity near-field close to the plasmonic nanoparticles. Those properties strongly affect the generated photocurrent. The simulation results show that using plasmonic nanoparticles significantly enhances the electrical properties of the photodetectors. More importantly, one can easily manipulate the plasmonic properties of the gold nanoparticles through engineering the nanoparticles' size, shape and morphology. Another important phenomenon is plasmon-plasmon interaction inside the photodetector. It is shown that plasmon-plasmon interaction improves the electron-hole generation rate by which the rate of the current generation is severely enhanced. This is the key factor that we want to focus on, to improve the photodetector electrical properties.

Keywords: plasmonic photodetector, plasmon-plasmon interaction, Gold nanoparticle, electrical properties

Procedia PDF Downloads 139