Search results for: axial flux induction machine

Authors: Lamis Naddaf, Yuval Tabach

Abstract:

In this research, we identified the missense genetic variants that have the potential to enhance resistance against cancer. Such field has not been widely explored, as researchers tend to investigate mutations that cause diseases, in response to the suffering of patients, rather than those mutations that protect from them. In conjunction with the genomic revolution, and the advances in genetic engineering and synthetic biology, identifying the protective variants will increase the power of genotype-phenotype predictions and can have significant implications on improved risk estimation, diagnostics, prognosis and even for personalized therapy and drug discovery. To approach our goal, we systematically investigated the sites of the coding genomes and picked up the alleles that showed a correlation with the species’ cancer resistance. We predicted 250 protecting variants (PVs) with a 0.01 false discovery rate and more than 20 thousand PVs with a 0.25 false discovery rate. Cancer resistance in Mammals and reptiles was significantly predicted by the number of PVs a species has. Moreover, Genes enriched with the protecting variants are enriched in pathways relevant to tumor suppression like pathways of Hedgehog signaling and silencing, which its improper activation is associated with the most common form of cancer malignancy. We also showed that the PVs are more abundant in healthy people compared to cancer patients within different human races.

Keywords: comparative genomics, machine learning, cancer resistance, cancer-protecting alleles

Procedia PDF Downloads 97

Authors: Shota Nagata, Kazuya Okubo, Toru Fujii

Abstract:

The purpose of this study is to investigate the proper condition in extracting carbon fibers as the reinforcement of composite molded by injection method. Recycled carbon fibers were extracted from wasted CFRP by pyrolyzing epoxy matrix of CFRP under air atmosphere at different temperature conditions 400, 600 and 800°C in this study. Recycled carbon fiber reinforced polypropylene (RCF/PP) pellets were prepared using twin screw extruder. The RCF/PP specimens were molded into dumbbell shaped specimens using injection molding machine. The tensile strength of recycled carbon fiber was decreased with rising pyrolysis temperature from 400 to 800°C. However, superior mechanical properties of tensile strength, tensile modulus and fracture strain of RCF/PP specimen were obtained when the extracting temperature was 600°C. Almost fibers in RCF/PP specimens were aligned in the mold filling direction in this study when the extracting temperature was 600°C. To discuss the results, the failure mechanisms of RCF/PP specimens was shown schematically. Finally, it was concluded that the temperature condition at 600°C should be selected in extracting carbon fibers as the reinforcement of RCF/PP composite molded by injection method.

Keywords: CFRP, recycled carbon fiber, injection molding, mechanical properties, fiber orientation, failure mechanism

Procedia PDF Downloads 446

Authors: Justin Tang

Abstract:

Balance tests are frequently used to diagnose concussions on the sidelines of sporting events. Manual scoring, however, is labor intensive and subjective, and many concussions go undetected. This study institutes a novel approach to conducting the Balance Error Scoring System (BESS) more quantitatively using Microsoft’s gaming system Kinect, which uses a contactless sensor and several cameras to receive data and estimate body limb positions. Using a machine learning approach, Visual Gesture Builder, and a deterministic approach, MATLAB, we tested whether the Kinect can differentiate between “correct” and erroneous stances of the BESS. We created the two separate solutions by recording test videos to teach the Kinect correct stances and by developing a code using Java. Twenty-two subjects were asked to perform a series of BESS tests while the Kinect was collecting data. The Kinect recorded the subjects and mapped key joints onto their bodies to obtain angles and measurements that are interpreted by the software. Through VGB and MATLAB, the videos are analyzed to enumerate the number of errors committed during testing. The resulting statistics demonstrate a high correlation between manual scoring and the Kinect approaches, indicating the viability of the use of remote tracking devices in conducting concussion tests.

Keywords: automated, concussion detection, contactless sensors, microsoft kinect

Procedia PDF Downloads 318

Authors: U. Achutha Kini, S. S. Sharma, K. Jagannath, P. R. Prabhu, M. C. Gowri Shankar

Abstract:

Aluminium matrix composites with alumina reinforcements give superior mechanical & physical properties. Their applications in several fields like automobile, aerospace, defense, sports, electronics, bio-medical and other industrial purposes are becoming essential for the last several decades. In the present work, fabrication of hybrid composite was done by Stir casting technique using Al 6061 as a matrix with alumina and silicon carbide (SiC) as reinforcement materials. The weight percentage of alumina is varied from 2 to 4% and the silicon carbide weight percentage is maintained constant at 2%. Hardness and wear tests are performed in the as cast and heat treated conditions. Age hardening treatment was performed on the specimen with solutionizing at 550°C, aging at two temperatures (150 and 200°C) for different time durations. Hardness distribution curves are drawn and peak hardness values are recorded. Hardness increase was very sensitive with respect to the decrease in aging temperature. There was an improvement in wear resistance of the peak aged material when aged at lower temperature. Also increase in weight percent of alumina, increases wear resistance at lower temperature but opposite behavior was seen when aged at higher temperature.

Keywords: hybrid composite, hardness test, wear test, heat treatment, pin on disc wear testing machine

Procedia PDF Downloads 321

Authors: Thaveesha Dheerasekera, Dileeka Sandamali Alwis

Abstract:

The escalating prevalence of mental health issues, such as depression and suicidal ideation, is a matter of significant global concern. It is plausible that a variety of factors, such as life events, social isolation, and preexisting physiological or psychological health conditions, could instigate or exacerbate these conditions. Traditional approaches to diagnosing depression entail a considerable amount of time and necessitate the involvement of adept practitioners. This underscores the necessity for automated systems capable of promptly detecting and diagnosing symptoms of depression. The PsyVBot system employs sophisticated natural language processing and machine learning methodologies, including the use of the NLTK toolkit for dataset preprocessing and the utilization of a Long Short-Term Memory (LSTM) model. The PsyVBot exhibits a remarkable ability to diagnose depression with a 94% accuracy rate through the analysis of user input. Consequently, this resource proves to be efficacious for individuals, particularly those enrolled in academic institutions, who may encounter challenges pertaining to their psychological well-being. The PsyVBot employs a Long Short-Term Memory (LSTM) model that comprises a total of three layers, namely an embedding layer, an LSTM layer, and a dense layer. The stratification of these layers facilitates a precise examination of linguistic patterns that are associated with the condition of depression. The PsyVBot has the capability to accurately assess an individual's level of depression through the identification of linguistic and contextual cues. The task is achieved via a rigorous training regimen, which is executed by utilizing a dataset comprising information sourced from the subreddit r/SuicideWatch. The diverse data present in the dataset ensures precise and delicate identification of symptoms linked with depression, thereby guaranteeing accuracy. PsyVBot not only possesses diagnostic capabilities but also enhances the user experience through the utilization of audio outputs. This feature enables users to engage in more captivating and interactive interactions. The PsyVBot platform offers individuals the opportunity to conveniently diagnose mental health challenges through a confidential and user-friendly interface. Regarding the advancement of PsyVBot, maintaining user confidentiality and upholding ethical principles are of paramount significance. It is imperative to note that diligent efforts are undertaken to adhere to ethical standards, thereby safeguarding the confidentiality of user information and ensuring its security. Moreover, the chatbot fosters a conducive atmosphere that is supportive and compassionate, thereby promoting psychological welfare. In brief, PsyVBot is an automated conversational agent that utilizes an LSTM model to assess the level of depression in accordance with the input provided by the user. The demonstrated accuracy rate of 94% serves as a promising indication of the potential efficacy of employing natural language processing and machine learning techniques in tackling challenges associated with mental health. The reliability of PsyVBot is further improved by the fact that it makes use of the Reddit dataset and incorporates Natural Language Toolkit (NLTK) for preprocessing. PsyVBot represents a pioneering and user-centric solution that furnishes an easily accessible and confidential medium for seeking assistance. The present platform is offered as a modality to tackle the pervasive issue of depression and the contemplation of suicide.

Keywords: chatbot, depression diagnosis, LSTM model, natural language process

Procedia PDF Downloads 72

Authors: Cuneyt Yucelbas, Seral Ozsen, Sule Yucelbas, Gulay Tezel

Abstract:

Due to the fact that there exist only a small number of complex systems in artificial immune system (AIS) that work out nonlinear problems, nonlinear AIS approaches, among the well-known solution techniques, need to be developed. Gaussian function is usually used as similarity estimation in classification problems and pattern recognition. In this study, diagnosis of breast cancer, the second type of the most widespread cancer in women, was performed with different distance calculation functions that euclidean, gaussian and gaussian-euclidean hybrid function in the clonal selection model of classical AIS on Wisconsin Breast Cancer Dataset (WBCD), which was taken from the University of California, Irvine Machine-Learning Repository. We used 3-fold cross validation method to train and test the dataset. According to the results, the maximum test classification accuracy was reported as 97.35% by using of gaussian-euclidean hybrid function for fold-3. Also, mean of test classification accuracies for all of functions were obtained as 94.78%, 94.45% and 95.31% with use of euclidean, gaussian and gaussian-euclidean, respectively. With these results, gaussian-euclidean hybrid function seems to be a potential distance calculation method, and it may be considered as an alternative distance calculation method for hard nonlinear classification problems.

Keywords: artificial immune system, breast cancer diagnosis, Euclidean function, Gaussian function

Procedia PDF Downloads 436

Authors: Young Kwon Yang, Bo Rang Park, Hyo Eun Lee, Tea Won Kim, Eun Ji Choi, Jin Chul Park

Abstract:

To develop an artificial intelligence based automatic ventilation system, recent research trends were analyzed and analyzed. This research method is as follows. In the field of architecture and window technology, the use of artificial intelligence, the existing study of machine learning model and the theoretical review of the literature were carried out. This paper collected journals such as Journal of Energy and Buildings, Journal of Renewable and Sustainable Energy Reviews, and articles published on Web-sites. The following keywords were searched for articles from 2000 to 2016. We searched for the above keywords mainly in the title, keyword, and abstract. As a result, the global artificial intelligence market is expected to grow at a CAGR of 14.0% from USD127bn in 2015 to USD165bn in 2017. Start-up investments in artificial intelligence increased from the US $ 45 million in 2010 to the US $ 310 million in 2015, and the number of investments increased from 6 to 54. Although AI is making efforts to advance to advanced countries, the level of technology is still in its infant stage. Especially in the field of architecture, artificial intelligence (AI) is very rare. Based on the data of this study, it is expected that the application of artificial intelligence and the application of architectural field will be revitalized through the activation of artificial intelligence in the field of architecture and window.

Keywords: artificial intelligence, window, fine dust, thermal comfort, ventilation system

Procedia PDF Downloads 275

Authors: Ibrahim S. Seddiek, Nader R. Ammar

Abstract:

One of the most important equipment that affects the performance of passenger ships is the air conditioning system, which in turn consumes considerable electric loads. In this paper, the waste heat energies of exhaust gases and jacket cooling water of marine diesel engines for these ships are analyzed to be used as heat sources for absorption refrigeration unit (ARU). Economic and environmental analysis of the absorption refrigeration cycle operated with the two heat sources that use lithium bromide as absorbent is carried out. In addition, environmental and economic analysis for the absorption cycle is performed. As a case study, high-speed passenger vessel operating in the Red Sea area has been investigated. The results show that a considerable specific economic benefit could be achieved in case of applying absorption air condition that operates by water cooling system over that operates by main engine exhaust gases. Environmentally, applying ARU machine during cruise will reduce total ship’s fuel consumption by about 104 ton per year. This will result in reducing NOₓ, SOₓ, and CO₂ emissions with cost-effectiveness of 6.99 $/kg, 18.44 $/kg, and 0.117 $/kg, respectively.

Keywords: ship emissions, IMO, lithium bromide-water ARU, analysis, thermodynamic, economic and environmental analysis

Procedia PDF Downloads 286

Authors: Dhurgham Al-Karawi, Nisreen Polus, Shakir Al-Zaidi, Sabah Jassim

Abstract:

The management of COVID-19 patients based on chest imaging is emerging as an essential tool for evaluating the spread of the pandemic which has gripped the global community. It has already been used to monitor the situation of COVID-19 patients who have issues in respiratory status. There has been increase to use chest imaging for medical triage of patients who are showing moderate-severe clinical COVID-19 features, this is due to the fast dispersal of the pandemic to all continents and communities. This article demonstrates the development of machine learning techniques for the test of COVID-19 patients using Chest X-Ray (CXR) images in nearly real-time, to distinguish the COVID-19 infection with a significantly high level of accuracy. The testing performance has covered a combination of different datasets of CXR images of positive COVID-19 patients, patients with viral and bacterial infections, also, people with a clear chest. The proposed AI scheme successfully distinguishes CXR scans of COVID-19 infected patients from CXR scans of viral and bacterial based pneumonia as well as normal cases with an average accuracy of 94.43%, sensitivity 95%, and specificity 93.86%. Predicted decisions would be supported by visual evidence to help clinicians speed up the initial assessment process of new suspected cases, especially in a resource-constrained environment.

Keywords: COVID-19, chest x-ray scan, artificial intelligence, texture analysis, local binary pattern transform, Gabor filter

Procedia PDF Downloads 147

Authors: Zahra Hamedani

Abstract:

Many wonderful technological developments in recent years has opened up the possibility of using intelligent buildings for a number of important applications, ranging from minimizing resource usage as well as increasing building efficiency to maximizing comfort, adaption to inhabitants and responsiveness to environmental changes. The concept of an intelligent building refers to the highly embedded, interactive environment within which by exploiting the use of artificial intelligence provides the ability to know its configuration, anticipate the optimum dynamic response to prevailing environmental stimuli, and actuate the appropriate physical reaction to provide comfort and efficiency. This paper contains a general identification of the intelligence paradigm and its impacts on the architecture arena, that with examining the performance of artificial intelligence, a mechanism to analyze and finally for decision-making to control the environment will be described. This mechanism would be a hierarchy of the rational agents which includes decision-making, information, communication and physical layers. This multi-agent system relies upon machine learning techniques for automated discovery, prediction and decision-making. Then, the application of this mechanism regarding adaptation and responsiveness of intelligent building will be provided in two scales of environmental and user. Finally, we review the identifications of sustainability and evaluate the potentials of intelligent building systems in the creation of sustainable architecture and environment.

Keywords: artificial intelligence, intelligent building, responsiveness, adaption, sustainability

Procedia PDF Downloads 411

Authors: Bandar Alahmadi, Manohar Mareboyana, Lethia Jackson

Abstract:

Today, there are many applications that are using computer vision models, such as face recognition, image classification, and object detection. The accuracy of these models is very important for the performance of these applications. One challenge that facing the computer vision models is the adversarial examples attack. In computer vision, the adversarial example is an image that is intentionally designed to cause the machine learning model to misclassify it. One of very well-known method that is used to attack the Convolution Neural Network (CNN) is Fast Gradient Sign Method (FGSM). The goal of this method is to find the perturbation that can fool the CNN using the gradient of the cost function of CNN. In this paper, we introduce a novel model that can attack Regional-Convolution Neural Network (R-CNN) that use FGSM. We first extract the regions that are detected by R-CNN, and then we resize these regions into the size of regular images. Then, we find the best perturbation of the regions that can fool CNN using FGSM. Next, we add the resulted perturbation to the attacked region to get a new region image that looks similar to the original image to human eyes. Finally, we placed the regions back to the original image and test the R-CNN with the attacked images. Our model could drop the accuracy of the R-CNN when we tested with Pascal VOC 2012 dataset.

Keywords: adversarial examples, attack, computer vision, image processing

Procedia PDF Downloads 193

Authors: Torsten Hermanns, You Wang, Stefan Janssen, Markus Niessen, Christoph Schoeler, Ulrich Thombansen, Wolfgang Schulz

Abstract:

In long pulse laser drilling of metals, it can be demonstrated that the ablation shape approaches a so-called asymptotic shape such that it changes only slightly or not at all with further irradiation. These findings are already known from ultra short pulse (USP) ablation of dielectric and semiconducting materials. The explanation for the occurrence of an asymptotic shape in long pulse drilling of metals is identified, a model for the description of the asymptotic hole shape numerically implemented, tested and clearly confirmed by comparison with experimental data. The model assumes a robust process in that way that the characteristics of the melt flow inside the arising melt film does not change qualitatively by changing the laser or processing parameters. Only robust processes are technically controllable and thus of industrial interest. The condition for a robust process is identified by a threshold for the mass flow density of the assist gas at the hole entrance which has to be exceeded. Within a robust process regime the melt flow characteristics can be captured by only one model parameter, namely the intensity threshold. In analogy to USP ablation (where it is already known for a long time that the resulting hole shape results from a threshold for the absorbed laser fluency) it is demonstrated that in the case of robust long pulse ablation the asymptotic shape forms in that way that along the whole contour the absorbed heat flux density is equal to the intensity threshold. The intensity threshold depends on the special material and radiation properties and has to be calibrated be one reference experiment. The model is implemented in a numerical simulation which is called AsymptoticDrill and requires such a few amount of resources that it can run on common desktop PCs, laptops or even smart devices. Resulting hole shapes can be calculated within seconds what depicts a clear advantage over other simulations presented in literature in the context of industrial every day usage. Against this background the software additionally is equipped with a user-friendly GUI which allows an intuitive usage. Individual parameters can be adjusted using sliders while the simulation result appears immediately in an adjacent window. A platform independent development allow a flexible usage: the operator can use the tool to adjust the process in a very convenient manner on a tablet during the developer can execute the tool in his office in order to design new processes. Furthermore, at the best knowledge of the authors AsymptoticDrill is the first simulation which allows the import of measured real beam distributions and thus calculates the asymptotic hole shape on the basis of the real state of the specific manufacturing system. In this paper the emphasis is placed on the investigation of the effect of multiple reflections on the asymptotic hole shape which gain in importance when drilling holes with large aspect ratios.

Keywords: asymptotic hole shape, intensity threshold, long pulse laser drilling, robust process

Procedia PDF Downloads 214

Authors: Farhad Alizadeh, Alireza Faregh Gharamaleki, Mojtaba Jalilzadeh, Houshang Gholami, Ali Akhoundzadeh

Abstract:

This study presented hybrid pre-processing approach along with a conceptual model to enhance the accuracy of river discharge prediction. In order to achieve this goal, Ensemble Empirical Mode Decomposition algorithm (EEMD), Discrete Wavelet Transform (DWT) and Mutual Information (MI) were employed as a hybrid pre-processing approach conjugated to Least Square Support Vector Machine (LSSVM). A conceptual strategy namely multi-station model was developed to forecast the Souris River discharge more accurately. The strategy used herein was capable of covering uncertainties and complexities of river discharge modeling. DWT and EEMD was coupled, and the feature selection was performed for decomposed sub-series using MI to be employed in multi-station model. In the proposed feature selection method, some useless sub-series were omitted to achieve better performance. Results approved efficiency of the proposed DWT-EEMD-MI approach to improve accuracy of multi-station modeling strategies.

Keywords: river stage-discharge process, LSSVM, discrete wavelet transform, Ensemble Empirical Decomposition Mode, multi-station modeling

Procedia PDF Downloads 175

Authors: Manuel Chacon-Fuentes, Leonardo Bardehle, Marcelo Lizama, Claudio Reyes, Andres Quiroz

Abstract:

The insect-plant interaction is a complex process in which the plant is able to release chemical signaling that modifies the behavior of insects. Insect herbivory can trigger mechanisms that allow the increase in the production of secondary metabolites that allow coping against the herbivores. Monoterpenes are a kind of secondary metabolites involved in direct defense acting as repellents of herbivorous or even in indirect defense acting as attractants for insect predators. In addition, an increase of the monoterpenes concentration is an effect commonly associated with the herbivory. Hence, plants subjected to damage by herbivory increase the monoterpenes production in comparison to plants without herbivory. In this framework, co-evolutionary aspects play a fundamental role in the adaptation of the herbivorous to their host and in the counter-adaptive strategies of the plants to avoid the herbivorous. In this context, Ugni molinae 'murtilla' is a native shrub from Chile characterized by its antioxidant activity mainly related to the phenolic compounds presents in its fruits. The larval stage of the red grape caterpillar Chilesia rudis Butler (Lepidoptera: Arctiidae) has been reported as an important defoliator of U. molinae. This insect is native from Chile and probably has been involved in a co-evolutionary process with murtilla. Therefore, we hypothesized that herbivory by the red grape caterpillar increases the emission of monoterpenes in Ugni molinae. Ecotypes 19-1 and 22-1 of murtilla were established and maintained at 25° C in the Laboratorio de Química Ecológica at Universidad de La Frontera. Red grape caterpillars of ⁓40 mm were collected near to Temuco (Chile) from grasses, and they were deprived of food for 24 h before performing the assays. Ten caterpillars were placed on the foliage of the ecotypes 19-1 and 22-1 and allowed to feed during 48 h. After this time, caterpillars were removed from the ecotypes and monoterpenes were collected. A glass chamber was used to enclose the ecotypes and a Porapak-Q column was used to trap the monoterpenes. After 24 h of capturing, columns were desorbed with hexane. Then, samples were injected in a gas chromatograph coupled to mass spectrometer and monoterpenes were determined according to the NIST library. All the experiments were performed in triplicate. Results showed that α-pinene, β-phellandrene, limonene, and 1,8 cineole were the main monoterpenes released by murtilla ecotypes. For the ecotype 19-1, the abundance of α-pinene was significantly higher in plants subjected to herbivory (100%) in relation to control plants (54.58%). Moreover, β-phellandrene and 1,8 cineole were observed only in control plants. For ecotype 22-1, there was no significant difference in monoterpenes abundance. In conclusion, the results suggest a trade-off of β-phellandrene and 1,8 cineole in response to herbivory damage by red grape caterpillar generating an increase in α-pinene abundance.

Keywords: Chilesia rudis, gas chromatography, monoterpenes, Ugni molinae

Procedia PDF Downloads 152

Authors: Jia-Sin Li, Ying-Fang Wang, Cheing-Tong Yan

Abstract:

In this study, an integrated approach of Google Spreadsheet and QR code which is free internet resources was used to improve the inspection procedure. The mobile phone Application（App）was also designed to combine with a web page to create an automatic checklist in order to provide a new integrated information of inspection management system. By means of client-server model, the client App is developed for Android mobile OS and the back end is a web server. It can set up App accounts including authorized data and store some checklist documents in the website. The checklist document URL could generate QR code first and then print and paste on the machine. The user can scan the QR code by the app and filled the checklist in the factory. In the meanwhile, the checklist data will send to the server, it not only save the filled data but also executes the related functions and charts. On the other hand, it also enables auditors and supervisors to facilitate the prevention and response to hazards, as well as immediate report data checks. Finally, statistics and professional analysis are performed using inspection records and other relevant data to not only improve the reliability, integrity of inspection operations and equipment loss control, but also increase plant safety and personnel performance. Therefore, it suggested that the traditional paper-based inspection method could be replaced by the APP which promotes the promotion of industrial security and reduces human error.

Keywords: checklist, Google spreadsheet, APP, self-inspection

Procedia PDF Downloads 120

Authors: Hussin K. Ragb, Vijayan K. Asari

Abstract:

In this paper, we present a pedestrian detection descriptor called Fused Structure and Texture (FST) features based on the combination of the local phase information with the texture features. Since the phase of the signal conveys more structural information than the magnitude, the phase congruency concept is used to capture the structural features. On the other hand, the Center-Symmetric Local Binary Pattern (CSLBP) approach is used to capture the texture information of the image. The dimension less quantity of the phase congruency and the robustness of the CSLBP operator on the flat images, as well as the blur and illumination changes, lead the proposed descriptor to be more robust and less sensitive to the light variations. The proposed descriptor can be formed by extracting the phase congruency and the CSLBP values of each pixel of the image with respect to its neighborhood. The histogram of the oriented phase and the histogram of the CSLBP values for the local regions in the image are computed and concatenated to construct the FST descriptor. Several experiments were conducted on INRIA and the low resolution DaimlerChrysler datasets to evaluate the detection performance of the pedestrian detection system that is based on the FST descriptor. A linear Support Vector Machine (SVM) is used to train the pedestrian classifier. These experiments showed that the proposed FST descriptor has better detection performance over a set of state of the art feature extraction methodologies.

Keywords: pedestrian detection, phase congruency, local phase, LBP features, CSLBP features, FST descriptor

Procedia PDF Downloads 490

Authors: Abhishek Pandey

Abstract:

Capping and lamination are the most common tablet defects with poorly compressible drugs the common example of that Ibuprofen and Acetaminophen. Generally both these drugs are compressed by wet granulation method which is very time consuming process Ibuprofen and Acetaminophen is widely used as prescription & non-prescription medicine. Ibuprofen mainly used in the treatment of mild to moderate pain related to headache, migraine, postoperative condition and in the management of spondylitis, osteoarthritis Acetaminophen used as an analgesic and antipyretic drug. Ibuprofen having high tendency of sticking to punches of tablet punching machine while Acetaminophen is not ordinarily compressible to tablet formulation because Acetaminophen crystals are very hard and brittle in nature and fracture very easily when compressed producing capping and laminating tablet defects therefore wet granulation method is used to make them compressible. The aim of study was to prepare Ibuprofen and Acetaminophen tablets by direct compression technique and their evaluation. In this Investigation tablets were prepared by using directly compressible grade excipients. Dibasic calcium phosphate, lactose anhydrous (DCL21), microcrystalline cellulose (Avicel PH 101). In order to obtain best or optimize formulation nine different formulations were generated among them batch F5, F6, F7 shows good results and within the acceptable limit. Formulation (F7) selected as optimize product on the basis of evaluation parameters.

Keywords: capping, lamination, tablet defects, direct compression

Procedia PDF Downloads 438

Authors: Zhaozhi Liu, Feng Xu, Junhua Xu, Xiaolong Tang, Ying Liu, Dunwen Zuo

Abstract:

Chemical vapor deposition (CVD) diamond coated cutting tool has excellent cutting performance, it is the most ideal tool for the processing of nonferrous metals and alloys, composites, nonmetallic materials and other difficult-to-machine materials efficiently and accurately. Depositing CVD diamond coating on the cemented carbide with high cobalt content can improve its toughness and strength, therefore, it is very important to research on the preparation technology and cutting properties of CVD diamond coated cemented carbide cutting tool with high cobalt content. The preparation technology of boron-doped diamond (BDD) coating has been studied and the coated drills were prepared. BDD coating were deposited on the drills by using the optimized parameters and the SEM results show that there are no cracks or collapses in the coating. Cutting tests with the prepared drills against the silumin and aluminum base printed circuit board (PCB) have been studied. The results show that the wear amount of the coated drill is small and the machined surface has a better precision. The coating does not come off during the test, which shows good adhesion and cutting performance of the drill.

Keywords: cemented carbide with high cobalt content, CVD boron-doped diamond, cutting test, drill

Procedia PDF Downloads 420

Authors: Maria Hadjisoteriou, Yiorgos Hadjichristou

Abstract:

This paper aims to explore and provoke a debate, through the work of the design studio, “living where the immaterial matters” of the architecture department of the University of Nicosia, on the role that the “immaterial matter” can play in enhancing innovative sustainable architecture and viewing the cities as sustainable organisms that always grow and alter. The blurring, juxtaposing binary of immaterial and matter, as the theoretical backbone of the Unit is counterbalanced by the practicalities of the contested sites of the last divided capital Nicosia with its ambiguous green line and the ghost city of Famagusta in the island of Cyprus. Jonathan Hill argues that the ‘immaterial is as important to architecture as the material concluding that ‘Immaterial–Material’ weaves the two together, so that they are in conjunction not opposition’. This understanding of the relationship of the immaterial vs material set the premises and the departing point of our argument, and talks about new recipes for creating hybrid public space that can lead to the unpredictability of a complex and interactive, sustainable city. We hierarchized the human experience as a priority. We distinguish the notion of space and place referring to Heidegger’s ‘building dwelling thinking’: ‘a distinction between space and place, where spaces gain authority not from ‘space’ appreciated mathematically but ‘place’ appreciated through human experience’. Following the above, architecture and the city are seen as one organism. The notions of boundaries, porous borders, fluidity, mobility, and spaces of flows are the lenses of the investigation of the unit’s methodology, leading to the notion of a new hybrid urban environment, where the main constituent elements are in a flux relationship. The material and the immaterial flows of the town are seen interrelated and interwoven with the material buildings and their immaterial contents, yielding to new sustainable human built environments. The above premises consequently led to choices of controversial sites. Indisputably a provoking site was the ghost town of Famagusta where the time froze back in 1974. Inspired by the fact that the nature took over the a literally dormant, decaying city, a sustainable rebirthing was seen as an opportunity where both nature and built environment, material and immaterial are interwoven in a new emergent urban environment. Similarly, we saw the dividing ‘green line’ of Nicosia completely failing to prevent the trespassing of images, sounds and whispers, smells and symbols that define the two prevailing cultures and becoming a porous creative entity which tends to start reuniting instead of separating , generating sustainable cultures and built environments. The authors would like to contribute to the debate by introducing a question about a new recipe of cooking the built environment. Can we talk about a new ‘urban recipe’: ‘cooking architecture and city’ to deliver an ever changing urban sustainable organism, whose identity will mainly depend on the interrelationship of the immaterial and material constituents?

Keywords: blurring zones, porous borders, spaces of flow, urban recipe

Procedia PDF Downloads 421

Authors: Ming-Jong Tsai, T. M. Wu, R. C. Chu

Abstract:

The purpose of this study is to develop an Advanced linear calibration Technique for air flow sensing by using CTA-based Hot wire Anemometry. It contains a host PC with Human Machine Interface, a wind tunnel, a wind speed controller, an automatic data acquisition module, and nonlinear calibration model. To improve the fitting error by using single fitting polynomial, this study proposes a Multiple three-order Polynomial Fitting Method (MPFM) for fitting the non-linear output of a CTA-based Hot wire Anemometry. The CTA-based anemometer with built-in fitting parameters is installed in the wind tunnel, and the wind speed is controlled by the PC-based controller. The Hot-Wire anemometer's thermistor resistance change is converted into a voltage signal or temperature differences, and then sent to the PC through a DAQ card. After completion measurements of original signal, the Multiple polynomial mathematical coefficients can be automatically calculated, and then sent into the micro-processor in the Hot-Wire anemometer. Finally, the corrected Hot-Wire anemometer is verified for the linearity, the repeatability, error percentage, and the system outputs quality control reports.

Keywords: flow rate sensing, hot wire, constant temperature anemometry (CTA), linear calibration, multiple three-order polynomial fitting method (MPFM), temperature compensation

Procedia PDF Downloads 416

Authors: Mu-Yen Chen, Min-Hsuan Fan, Chia-Chen Chen, Siang-Yu Jhong

Abstract:

In recent years, real estate prediction or valuation has been a topic of discussion in many developed countries. Improper hype created by investors leads to fluctuating prices of real estate, affecting many consumers to purchase their own homes. Therefore, scholars from various countries have conducted research in real estate valuation and prediction. With the back-propagation neural network that has been popular in recent years and the orthogonal array in the Taguchi method, this study aimed to find the optimal parameter combination at different levels of orthogonal array after the system presented different parameter combinations, so that the artificial neural network obtained the most accurate results. The experimental results also demonstrated that the method presented in the study had a better result than traditional machine learning. Finally, it also showed that the model proposed in this study had the optimal predictive effect, and could significantly reduce the cost of time in simulation operation. The best predictive results could be found with a fewer number of experiments more efficiently. Thus users could predict a real estate transaction price that is not far from the current actual prices.

Keywords: artificial neural network, Taguchi method, real estate valuation model, investors

Procedia PDF Downloads 490

Authors: Patrícia T. Souza, Marina Ansolin, Eduardo A. C. Batista, Antonio J. A. Meirelles, Matthieu Tubino

Abstract:

Lipid oxidation is a major cause of the deterioration of the quality of the biodiesel, because the waste generated damages the engines. Among the main undesirable effects are the increase of viscosity and acidity, leading to the formation of insoluble gums and sediments which cause the blockage of fuel filters. The auto-oxidation is defined as the spontaneous reaction of atmospheric oxygen with lipids. Unsaturated fatty acids are usually the components affected by such reactions. They are present as free fatty acids, fatty esters and glycerides. To determine the oxidative stability of biodiesels, through the induction period, IP, the Rancimat method is used, which allows continuous monitoring of the induced oxidation process of the samples. During the oxidation of the lipids, volatile organic acids are produced as byproducts, in addition, other byproducts, including alcohols and carbonyl compounds, may be further oxidized to carboxylic acids. By the methodology developed in this work using ion chromatography, IC, analyzing the water contained in the conductimetric vessel, were quantified organic anions of carboxylic acids in samples subjected to oxidation induced by Rancimat. The optimized chromatographic conditions were: eluent water:acetone (80:20 v/v) with 0.5 mM sulfuric acid; flow rate 0.4 mL min-1; injection volume 20 µL; eluent suppressor 20 mM LiCl; analytical curve from 1 to 400 ppm. The samples studied were methyl biodiesel from soybean oil and unsaturated fatty acids standards: oleic, linoleic and linolenic. The induced oxidation kinetics curves were constructed by analyzing the water contained in the conductimetric vessels which were removed, each one, from the Rancimat apparatus at prefixed intervals of time. About 3 g of sample were used under the conditions of 110 °C and air flow rate of 10 L h-1. The water of each conductimetric Rancimat measuring vessel, where the volatile compounds were collected, was filtered through a 0.45 µm filter and analyzed by IC. Through the kinetic data of the formation of the organic anions of carboxylic acids, the formation rates of the same were calculated. The observed order of the rates of formation of the anions was: formate >>> acetate > hexanoate > valerate for the oleic acid; formate > hexanoate > acetate > valerate for the linoleic acid; formate >>> valerate > acetate > propionate > butyrate for the linolenic acid. It is possible to suppose that propionate and butyrate are obtained mainly from linolenic acid and that hexanoate is originated from oleic and linoleic acid. For the methyl biodiesel the order of formation of anions was: formate >>> acetate > valerate > hexanoate > propionate. According to the total rate of formation these anions produced during the induced degradation of the fatty acids can be assigned the order of reactivity: linolenic acid > linoleic acid >>> oleic acid.

Keywords: anions of carboxylic acids, biodiesel, ion chromatography, oxidation

Procedia PDF Downloads 475

Authors: R. Dangtungee, A. Rattanapan, S. Siengchin

Abstract:

Waste silicon carbide (waste SiC) filled high-density polyethylene (HDPE) with and without surface modifiers were studied. Two types of surface modifiers namely; high-density polyethylene-grafted-maleic anhydride (HDPE-g-MA) and 3-aminopropyltriethoxysilane have been used in this study. The composites were produced using a two roll mill, extruder and shaped in a hydraulic compression molding machine. The mechanical properties of polymer composites such as flexural strength and modulus, impact strength, tensile strength, stiffness and hardness were investigated over a range of compositions. It was found that, flexural strength and modulus, tensile modulus and hardness increased, whereas impact strength and tensile strength decreased with the increasing in filler contents, compared to the neat HDPE. At similar filler content, the effect of both surface modifiers increased flexural modulus, impact strength, tensile strength and stiffness but reduced the flexural strength. Morphological investigation using SEM revealed that the improvement in mechanical properties was due to enhancement of the interfacial adhesion between waste SiC and HDPE.

Keywords: high-density polyethylene, HDPE-g-MA, mechanical properties, morphological properties, silicon carbide, waste silicon carbide

Procedia PDF Downloads 365

Authors: Suresh Vidyasagar Chevuri, D. B. Karunakar Chevuri

Abstract:

The main advantages of 'Metal Matrix Nano Composites (MMNCs)' include excellent mechanical performance, good wear resistance, low creep rate, etc. The method of fabrication of MMNCs is quite a challenge, which includes processing techniques like Spark Plasma Sintering (SPS), etc. The objective of the present work is to fabricate aluminum based MMNCs with the addition of small amounts of yttrium using Spark Plasma Sintering and to evaluate their mechanical and microstructure properties. Samples of 2024 AA with yttrium ranging from 0.1% to 0.5 wt% keeping 1 wt% TiB2 constant are fabricated by Spark Plasma Sintering (SPS). The mechanical property like hardness is determined using Vickers hardness testing machine. The metallurgical characterization of the samples is evaluated by Optical Microscopy (OM), Field Emission Scanning Electron Microscopy (FE-SEM) and X-Ray Diffraction (XRD). Unreinforced 2024 AA sample is also fabricated as a benchmark to compare its properties with that of the composite developed. It is found that the yttrium addition increases the above-mentioned properties to some extent and then decreases gradually when yttrium wt% increases beyond a point between 0.3 and 0.4 wt%. High density is achieved in the samples fabricated by spark plasma sintering when compared to any other fabrication route, and uniform distribution of yttrium is observed.

Keywords: spark plasma sintering, 2024 AA, yttrium addition, microstructure characterization, mechanical properties

Procedia PDF Downloads 226

Authors: Laura Helena Caicedo Lopez, Luis Miguel Contreras Medina, Ramon Gerardo Guevara Gonzales, Juan E. Andrade

Abstract:

In this study, we evaluated the effect of three different hydric stress conditions: Low (LHS), medium (MHS), and high (HHS) on capsaicinoid content and enzyme regulation of C. annuum plants. Five main peaks were detected using a 2 Hz resolution vibrometer laser (Polytec-B&K). These peaks or “characteristic frequencies” were used as acoustic emissions (AEs) treatment, transforming these signals into audible sound with the frequency (Hz) content of each hydric stress. Capsaicinoids (CAPs) are the main, secondary metabolites of chili pepper plants and are known to increase during hydric stress conditions or short drought-periods. The AEs treatments were applied in two plant stages: the first one was in the pre-anthesis stage to evaluate the genes that encode the transcription of enzymes responsible for diverse metabolic activities of C. annuum plants. For example, the antioxidant responses such as peroxidase (POD), superoxide dismutase (Mn-SOD). Also, phenyl-alanine ammonia-lyase (PAL) involved in the biosynthesis of the phenylpropanoid compounds. The chalcone synthase (CHS) related to the natural defense mechanisms and species-specific aquaporin (CAPIP-1) that regulate the flow of water into and out of cells. The second stage was at 40 days after flowering (DAF) to evaluate the biochemical effect of AEs related to hydric stress on capsaicinoids production. These two experiments were conducted to identify the molecular responses of C. annuum plants to AE. Moreover, to define AEs could elicit any increase in the capsaicinoids content after a one-week exposition to AEs treatments. The results show that all AEs treatment signals (LHS, MHS, and HHS) were significantly different compared to the non-acoustic emission control (NAE). Also, the AEs induced the up-regulation of POD (~2.8, 2.9, and 3.6, respectively). The gene expression of another antioxidant response was particularly treatment-dependent. The HHS induced and overexpression of Mn-SOD (~0.23) and PAL (~0.33). As well, the MHS only induced an up-regulation of the CHs gene (~0.63). On the other hand, CAPIP-1 gene gas down-regulated by all AEs treatments LHS, MHS, and HHS ~ (-2.4, -0.43 and -6.4, respectively). Likewise, the down-regulation showed particularities depending on the treatment. LHS and MHS induced downregulation of the SOD gene ~ (-1.26 and -1.20 respectively) and PAL (-4.36 and 2.05, respectively). Correspondingly, the LHS and HHS showed the same tendency in the CHs gene, respectively ~ (-1.12 and -1.02, respectively). Regarding the elicitation effect of AE on the capsaicinoids content, additional treatment controls were included. A white noise treatment (WN) to prove the frequency-selectiveness of signals and a hydric stressed group (HS) to compare the CAPs content. Our findings suggest that WN and NAE did not present differences statically. Conversely, HS and all AEs treatments induced a significant increase of capsaicin (Cap) and dihydrocapsaicin (Dcap) after one-week of a treatment. Specifically, the HS plants showed an increase of 8.33 times compared to the NAE and WN treatments and 1.4 times higher than the MHS, which was the AEs treatment with a larger induction of Capsaicinoids among treatments (5.88) and compared to the controls.

Keywords: acoustic emission, capsaicinoids, elicitors, hydric stress, plant signaling

Procedia PDF Downloads 172

Authors: Justin Woulfe

Abstract:

Siloed logistics and supply chain management systems throughout the Department of Defense (DOD) has led to disparate approaches to modeling and simulation (M&S), a lack of understanding of how one system impacts the whole, and issues with “optimal” solutions that are good for one organization but have dramatic negative impacts on another. Many different systems have evolved to try to understand and account for uncertainty and try to reduce the consequences of the unknown. As the DoD undertakes expansive digital transformation initiatives, there is an opportunity to fuse and leverage traditionally disparate data into a centrally hosted source of truth. With a streamlined process incorporating machine learning (ML) and artificial intelligence (AI), advanced M&S will enable informed decisions guiding program success via optimized operational readiness and improved mission success. One of the current challenges is to leverage the terabytes of data generated by monitored systems to provide actionable information for all levels of users. The implementation of a cloud-based application analyzing data transactions, learning and predicting future states from current and past states in real-time, and communicating those anticipated states is an appropriate solution for the purposes of reduced latency and improved confidence in decisions. Decisions made from an ML and AI application combined with advanced optimization algorithms will improve the mission success and performance of systems, which will improve the overall cost and effectiveness of any program. The Systecon team constructs and employs model-based simulations, cutting across traditional silos of data, aggregating maintenance, and supply data, incorporating sensor information, and applying optimization and simulation methods to an as-maintained digital twin with the ability to aggregate results across a system’s lifecycle and across logical and operational groupings of systems. This coupling of data throughout the enterprise enables tactical, operational, and strategic decision support, detachable and deployable logistics services, and configuration-based automated distribution of digital technical and product data to enhance supply and logistics operations. As a complete solution, this approach significantly reduces program risk by allowing flexible configuration of data, data relationships, business process workflows, and early test and evaluation, especially budget trade-off analyses. A true capability to tie resources (dollars) to weapon system readiness in alignment with the real-world scenarios a warfighter may experience has been an objective yet to be realized to date. By developing and solidifying an organic capability to directly relate dollars to readiness and to inform the digital twin, the decision-maker is now empowered through valuable insight and traceability. This type of educated decision-making provides an advantage over the adversaries who struggle with maintaining system readiness at an affordable cost. The M&S capability developed allows program managers to independently evaluate system design and support decisions by quantifying their impact on operational availability and operations and support cost resulting in the ability to simultaneously optimize readiness and cost. This will allow the stakeholders to make data-driven decisions when trading cost and readiness throughout the life of the program. Finally, sponsors are available to validate product deliverables with efficiency and much higher accuracy than in previous years.

Keywords: artificial intelligence, digital transformation, machine learning, predictive analytics

Procedia PDF Downloads 162

Authors: Yohanna Panshak

Abstract:

The impact of macroeconomic instability on economic growth and prosperity has been at forefront in many discourses among researchers and policy makers and has generated a lot of controversies over the years. This has generated series of research efforts towards understanding the remote causes of this phenomenon; its nature, determinants and how it can be targeted and mitigated. While others have opined that the root cause of macroeconomic flux in Nigeria is attributed to Oil-Price volatility, others viewed the issue as resulting from some constellation of structural constraints both within and outside the shores of the country. Research works of scholars such as [Akpan (2009), Aliyu (2009), Olomola (2006), etc] argue that oil volatility can determine economic growth or has the potential of doing so. On the contrary, [Darby (1982), Cerralo (2005) etc] share the opinion that it can slow down growth. The earlier argument rest on the understanding that for a net balance of oil exporting economies, price upbeat directly increases real national income through higher export earnings, whereas, the latter allude to the case of net-oil importing countries (which experience price rises, increased input costs, reduced non-oil demand, low investment, fall in tax revenues and ultimately an increase in budget deficit which will further reduce welfare level). Therefore, assessing the precise impact of oil price volatility on virtually any economy is a function of whether it is an oil-exporting or importing nation. Research on oil price volatility and its outcome on the growth of the Nigerian economy are evolving and in a march towards resolving Nigeria’s macroeconomic instability as long as oil revenue still remain the mainstay and driver of socio-economic engineering. Recently, a major importer of Nigeria’s oil- United States made a historic breakthrough in more efficient source of energy for her economy with the capacity of serving significant part of the world. This undoubtedly suggests a threat to the exchange earnings of the country. The need to understand fluctuation in its major export commodity is critical. This paper leans on the Renaissance growth theory with greater focus on theoretical work of Lee (1998); a leading proponent of this school who makes a clear cut of difference between oil price changes and oil price volatility. Based on the above background, the research seeks to empirically examine the impact oil-price volatility on government expenditure using quarterly time series data spanning 1986:1 to 2014:4. Vector Auto Regression (VAR) econometric approach shall be used. The structural properties of the model shall be tested using Augmented Dickey-Fuller and Phillips-Perron. Relevant diagnostics tests of heteroscedasticity, serial correlation and normality shall also be carried out. Policy recommendation shall be offered on the empirical findings and believes it assist policy makers not only in Nigeria but the world-over.

Keywords: oil-price, volatility, prosperity, budget, expenditure

Procedia PDF Downloads 273

Authors: Sthephanie A. Colmenares, Fabio A. Rojas, Pablo A. Arbeláez, Johann F. Osma, Diana Narvaez

Abstract:

The loss of functional tissue is a ubiquitous and expensive health care problem, with very limited treatment options for these patients. The golden standard for large bone damage is a cadaveric bone as an allograft with stainless steel support; however, this solution only applies to bones with simple morphologies (long bones), has a limited material supply and presents long term problems regarding mechanical strength, integration, differentiation and induction of native bone tissue. Therefore, the fabrication of a scaffold with biological, physical and chemical properties similar to the human bone with a fabrication method for morphology manipulation is the focus of this investigation. Towards this goal, an alginate and coral matrix was created using two production techniques; the coral was chosen because of its chemical composition and the alginate due to its compatibility and mechanical properties. In order to construct the coral alginate scaffold the following methodology was employed; cleaning of the coral, its pulverization, scaffold fabrication and finally the mechanical and biological characterization. The experimental design had: mill method and proportion of alginate and coral, as the two factors, with two and three levels each, using 5 replicates. The coral was cleaned with sodium hypochlorite and hydrogen peroxide in an ultrasonic bath. Then, it was milled with both a horizontal and a ball mill in order to evaluate the morphology of the particles obtained. After this, using a combination of alginate and coral powder and water as a binder, scaffolds of 1cm3 were printed with a SpectrumTM Z510 3D printer. This resulted in solid cubes that were resistant to small compression stress. Then, using a ESQUIM DP-143 silicon mold, constructs used for the mechanical and biological assays were made. An INSTRON 2267® was implemented for the compression tests; the density and porosity were calculated with an analytical balance and the biological tests were performed using cell cultures with VERO fibroblast, and Scanning Electron Microscope (SEM) as visualization tool. The Young’s moduli were dependent of the pulverization method, the proportion of coral and alginate and the interaction between these factors. The maximum value was 5,4MPa for the 50/50 proportion of alginate and horizontally milled coral. The biological assay showed more extracellular matrix in the scaffolds consisting of more alginate and less coral. The density and porosity were proportional to the amount of coral in the powder mix. These results showed that this composite has potential as a biomaterial, but its behavior is elastic with a small Young’s Modulus, which leads to the conclusion that the application may not be for long bones but for tissues similar to cartilage.

Keywords: alginate, biomaterial, bone engineering, coral, Porites asteroids, SEM

Procedia PDF Downloads 254

Authors: Gordana Branković, Vesna Dragičević, Dejan Dodig, Desimir Knežević, Srbislav Denčić, Gordana Šurlan-Momirović

Abstract:

Phytic acid is a major pool in the flux of phosphorus through agroecosystems and represents a sum equivalent to > 50% of all phosphorus fertilizer used annually. Nutrition rich in phytic acid can substantially decrease micronutrients apsorption as calcium, zink, iron, manganese, copper due to phytate salts excretion by human and non-ruminant animals as poultry, swine and fish, having in common very scarce phytase activity, and consequently the ability to digest and utilize phytic acid, thus phytic acid derived phosphorus in animal waste contributes to water pollution. The tested accessions consisted of 15 genotypes of bread wheat (Triticum aestivum L. ssp. vulgare) and of 15 genotypes of durum wheat (Triticum durum Desf.). The trials were sown at the three test sites in Serbia: Rimski Šančevi (RS) (45º19´51´´N; 19º50´59´´E), Zemun Polje (ZP) (44º52´N; 20º19´E) and Padinska Skela (PS) (44º57´N 20º26´E) during two vegetation seasons 2010-2011 and 2011-2012. The experimental design was randomized complete block design with four replications. The elementary plot consisted of 3 internal rows of 0.6 m2 area (3 × 0.2 m × 1 m). Grains were grinded with Laboratory Mill 120 Perten (“Perten”, Sweden) (particles size < 500 μm) and flour was used for the analysis. Phytic acid grain content was determined spectrophotometrically with the Shimadzu UV-1601 spectrophotometer (Shimadzu Corporation, Japan). Objectives of this study were to determine: i) variability and stability of the phytic acid content among selected genotypes of bread and durum wheat, ii) predominant source of variation regarding genotype (G), environment (E) and genotype × environment interaction (GEI) from the multi-environment trial, iii) influence of climatic variables on the GEI for the phytic acid content. Based on the analysis of variance it had been determined that the variation of phytic acid content was predominantly influenced by environment in durum wheat, while the GEI prevailed for the variation of the phytic acid content in bread wheat. Phytic acid content expressed on the dry mass basis was in the range 14.21-17.86 mg g-1 with the average of 16.05 mg g-1 for bread wheat and 14.63-16.78 mg g-1 with the average of 15.91 mg g-1 for durum wheat. Average-environment coordination view of the genotype by environment (GGE) biplot was used for the selection of the most desirable genotypes for breeding for low phytic acid content in the sense of good stability and lower level of phytic acid content. The most desirable genotypes of bread and durum wheat for breeding for phytic acid were Apache and 37EDUYT /07 No. 7849. Models of climatic factors in the highest percentage (> 91%) were useful in interpreting GEI for phytic acid content, and included relative humidity in June, sunshine hours in April, mean temperature in April and winter moisture reserves for genotypes of bread wheat, as well as precipitation in June and April, maximum temperature in April and mean temperature in June for genotypes of durum wheat.

Keywords: genotype × environment interaction, phytic acid, stability, variability

Procedia PDF Downloads 395

Authors: Jiaqi Dong, Qing-Hua Qin, Yi Xiao

Abstract:

Some of the most fundamental challenges of elastic metamaterials (EMMs) optimization can be attributed to the high consumption of computational power resulted from finite element analysis (FEA) simulations that render the optimization process inefficient. Furthermore, due to the inherent mesh dependence of FEA, minuscule geometry features, which often emerge during the later stages of optimization, induce very fine elements, resulting in enormously high time consumption, particularly when repetitive solutions are needed for computing the objective function. In this study, a surrogate modelling algorithm is developed to reduce computational time in structural optimization of EMMs. The surrogate model is constructed based on a multilayer feedforward artificial neural network (ANN) architecture, trained with prepopulated eigenfrequency data prepopulated from FEA simulation and optimized through regime selection with genetic algorithm (GA) to improve its accuracy in predicting the location and width of the primary elastic band gap. With the optimized ANN surrogate at the core, a Nelder-Mead (NM) algorithm is established and its performance inspected in comparison to the FEA solution. The ANNNM model shows remarkable accuracy in predicting the band gap width and a reduction of time consumption by 47%.

Keywords: artificial neural network, machine learning, mechanical metamaterials, Nelder-Mead optimization

Procedia PDF Downloads 128