Search results for: evolutionary optimization techniques

Authors: Y.Galerkin, O. Solovieva

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Flow parameters are calculated in vaneless diffusers with relative width 0,014 – 0,10 constant along radii. Inlet flow angles and similarity criteria were varied. Information about flow structure is presented – meridian streamlines configuration, information on flow full development, flow separation. Polytrophic efficiency, loss and recovery coefficient are used to compare diffusers’ effectiveness. The sample of narrow diffuser optimization by conical walls application is presented. Three tampered variants of a wide diffuser are compared too. The work is made in the R&D laboratory “Gas dynamics of turbo machines” of the TU SPb.

Keywords: vaneless diffuser, relative width, flow angle, flow separation, loss coefficient, similarity criteria

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Authors: H. Yousefnia, MS. Mousavi-Daramoroudi, S. Zolghadri, F. Abbasi-Davani

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High expression of somatostatin receptors on a wide range of human tumours makes them as potential targets for peptide receptor radionuclide tomography. A series of octreotide analogues were synthesized while [DOTA-DPhe1, Tyr3]octreotide (DOTATOC) indicated advantageous properties in tumour models. In this study, 177Lu-DOTATOC was prepared with the radiochemical purity of higher than 99% in 30 min at the optimized condition. Biological behavior of the complex was studied after intravenous injection into the Syrian rats. Major difference uptake was observed compared to 177LuCl3 solution especially in somatostatin receptor-positive tissues such as pancreas and adrenal.

Keywords: Biodistribution, 177Lu, Octreotide, Syrian rats

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Authors: Francis O. Nwawuru

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The convergence analysis of split monotone inclusion problems and fixed point problems of certain nonlinear mappings are investigated in the setting of real Hilbert spaces. Inertial extrapolation term in the spirit of Polyak is incorporated to speed up the rate of convergence. Under standard assumptions, a strong convergence of the proposed algorithm is established without computing the resolvent operator or involving Yosida approximation method. The stepsize involved in the algorithm does not depend on the spectral radius of the linear operator. Furthermore, applications of the proposed algorithm in solving some related optimization problems are also considered. Our result complements and extends numerous results in the literature.

Keywords: fixedpoint, hilbertspace, monotonemapping, resolventoperators

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Authors: Qinglin Shu

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Based on the analysis of the structural vibration comfort of a domestic bridge, this paper studies the vibration reduction control principle of TMD, the derivation process of design parameter optimization and how to simulate TMD in the finite element software ANSYS. The research shows that, in view of the problem that the comfort level of a bridge exceeds the limit in individual working conditions, the vibration reduction control design of the bridge can effectively reduce the vibration of the structure by using TMD. Calculations show that when the mass ratio of TMD is 0.01, the vibration reduction rate under different working conditions is more than 90%, and the dynamic displacement of the TMD mass block is within 0.01m, indicating that the design of TMD is reasonable and safe.

Keywords: pedestrian bridges, human-induced vibration, comfort, tuned mass dampers

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Authors: A. S. Walkey, N. P. Patidar

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It is not always economical to provide reactive power using synchronous alternators. The cost of reactive power can be minimized by optimal placing of FACTS devices in power systems. In this paper a Particle Swarm Optimization- Sequential Quadratic Programming (PSO-SQP) algorithm is applied to minimize the cost of reactive power generation along with real power generation to alleviate the bus voltage violations. The effectiveness of proposed approach tested on IEEE-14 bus systems. In this paper in addition to synchronous generators, an opportunity of FACTS devices are also proposed to procure the reactive power demands in the power system.

Keywords: reactive power, reactive power cost, voltage security margins, capability curve, FACTS devices

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Authors: Micheale Yifter Weldemichael, Hailay Mehari Gebremedhn, Teklehaimanot Hailesslasie Teklu

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Recent developments in targeted genome editing accelerated genetic research and opened new potentials to improve crops for better yields and quality. Given the significance of cereal crops as a primary source of food for the global population, the utilization of contemporary genome editing techniques like CRISPR/Cas9 is timely and crucial. CRISPR/Cas technology has enabled targeted genomic modifications, revolutionizing genetic research and exploration. Application of gene editing through CRISPR/Cas9 in enhancing sorghum is particularly vital given the current ecological, environmental, and agricultural challenges exacerbated by climate change. As sorghum is one of the main staple foods of our region and is known to be a resilient crop with a high potential to overcome the above challenges, the application of genome editing technology will enhance the investigation of gene functionality. CRISPR/Cas9 enables the improvement of desirable sorghum traits, including nutritional value, yield, resistance to pests and diseases, and tolerance to various abiotic stresses. Furthermore, CRISPR/Cas9 has the potential to perform intricate editing and reshape the existing elite sorghum varieties, and introduce new genetic variations. However, current research primarily focuses on improving the efficacy of the CRISPR/Cas9 system in successfully editing endogenous sorghum genes, making it a feasible and successful undertaking in sorghum improvement. Recent advancements and developments in CRISPR/Cas9 techniques have further empowered researchers to modify additional genes in sorghum with greater efficiency. Successful application and advancement of CRISPR techniques in sorghum will aid not only in gene discovery and the creation of novel traits that regulate gene expression and functional genomics but also in facilitating site-specific integration events. The purpose of this review is, therefore, to elucidate the current advances in sorghum genome editing and highlight its potential in addressing food security issues. It also assesses the efficiency of CRISPR-mediated improvement and its long-term effects on crop improvement and host resistance against parasites, including tissue-specific activity and the ability to induce resistance. This review ends by emphasizing the challenges and opportunities of CRISPR technology in combating parasitic plants and proposing directions for future research to safeguard global agricultural productivity.

Keywords: CRISPR/Cas9, genome editing, quality, sorghum, stress, yield

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Authors: Shaban Guma, Naim Bajcinca

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We propose an event-triggered algorithm for the solution of a distributed optimization problem by means of the projected subgradient method. Thereby, we invoke an OFDMA resource allocation scheme by applying an event-triggered sensitivity analysis at the access point. The optimal resource assignment of the subcarriers to the involved wireless nodes is carried out by considering the sensitivity analysis of the overall objective function as defined by the control of vehicle clusters with respect to the information exchange between the nodes.

Keywords: consensus, cross-layer, distributed, event-triggered, multi-vehicle, protocol, resource, OFDMA, wireless

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Authors: Bitewulign Mekonnen

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Context: This scientific paper focuses on the use of near-infrared (NIR) spectroscopy to determine glucose concentration in aqueous solutions accurately and rapidly. The study compares six different machine learning methods for predicting glucose concentration and also explores the development of a deep learning model for classifying NIR spectra. The objective is to optimize the detection model and improve the accuracy of glucose prediction. This research is important because it provides a comprehensive analysis of various machine-learning techniques for estimating aqueous glucose concentrations. Research Aim: The aim of this study is to compare and evaluate different machine-learning methods for predicting glucose concentration from NIR spectra. Additionally, the study aims to develop and assess a deep-learning model for classifying NIR spectra. Methodology: The research methodology involves the use of machine learning and deep learning techniques. Six machine learning regression models, including support vector machine regression, partial least squares regression, extra tree regression, random forest regression, extreme gradient boosting, and principal component analysis-neural network, are employed to predict glucose concentration. The NIR spectra data is randomly divided into train and test sets, and the process is repeated ten times to increase generalization ability. In addition, a convolutional neural network is developed for classifying NIR spectra. Findings: The study reveals that the SVMR, ETR, and PCA-NN models exhibit excellent performance in predicting glucose concentration, with correlation coefficients (R) > 0.99 and determination coefficients (R²)> 0.985. The deep learning model achieves high macro-averaging scores for precision, recall, and F1-measure. These findings demonstrate the effectiveness of machine learning and deep learning methods in optimizing the detection model and improving glucose prediction accuracy. Theoretical Importance: This research contributes to the field by providing a comprehensive analysis of various machine-learning techniques for estimating glucose concentrations from NIR spectra. It also explores the use of deep learning for the classification of indistinguishable NIR spectra. The findings highlight the potential of machine learning and deep learning in enhancing the prediction accuracy of glucose-relevant features. Data Collection and Analysis Procedures: The NIR spectra and corresponding references for glucose concentration are measured in increments of 20 mg/dl. The data is randomly divided into train and test sets, and the models are evaluated using regression analysis and classification metrics. The performance of each model is assessed based on correlation coefficients, determination coefficients, precision, recall, and F1-measure. Question Addressed: The study addresses the question of whether machine learning and deep learning methods can optimize the detection model and improve the accuracy of glucose prediction from NIR spectra. Conclusion: The research demonstrates that machine learning and deep learning methods can effectively predict glucose concentration from NIR spectra. The SVMR, ETR, and PCA-NN models exhibit superior performance, while the deep learning model achieves high classification scores. These findings suggest that machine learning and deep learning techniques can be used to improve the prediction accuracy of glucose-relevant features. Further research is needed to explore their clinical utility in analyzing complex matrices, such as blood glucose levels.

Keywords: machine learning, signal processing, near-infrared spectroscopy, support vector machine, neural network

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Authors: Mustafa Alhamdi

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Industrial application to classify gamma rays and neutron events is investigated in this study using deep machine learning. The identification using a convolutional neural network and recursive neural network showed a significant improvement in predication accuracy in a variety of applications. The ability to identify the isotope type and activity from spectral information depends on feature extraction methods, followed by classification. The features extracted from the spectrum profiles try to find patterns and relationships to present the actual spectrum energy in low dimensional space. Increasing the level of separation between classes in feature space improves the possibility to enhance classification accuracy. The nonlinear nature to extract features by neural network contains a variety of transformation and mathematical optimization, while principal component analysis depends on linear transformations to extract features and subsequently improve the classification accuracy. In this paper, the isotope spectrum information has been preprocessed by finding the frequencies components relative to time and using them as a training dataset. Fourier transform implementation to extract frequencies component has been optimized by a suitable windowing function. Training and validation samples of different isotope profiles interacted with CdTe crystal have been simulated using Geant4. The readout electronic noise has been simulated by optimizing the mean and variance of normal distribution. Ensemble learning by combing voting of many models managed to improve the classification accuracy of neural networks. The ability to discriminate gamma and neutron events in a single predication approach using deep machine learning has shown high accuracy using deep learning. The paper findings show the ability to improve the classification accuracy by applying the spectrogram preprocessing stage to the gamma and neutron spectrums of different isotopes. Tuning deep machine learning models by hyperparameter optimization of neural network models enhanced the separation in the latent space and provided the ability to extend the number of detected isotopes in the training database. Ensemble learning contributed significantly to improve the final prediction.

Keywords: machine learning, nuclear physics, Monte Carlo simulation, noise estimation, feature extraction, classification

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Authors: Boris Melnikov, Ye Zhang, Dmitrii Chaikovskii

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This study explores the pseudo-geometric version of the extensively researched Traveling Salesman Problem (TSP), proposing a novel generalization of existing algorithms which are traditionally confined to the geometric version. By adapting the "onion husk" method and introducing auxiliary algorithms, this research fills a notable gap in the existing literature. Through computational experiments using randomly generated data, several metrics were analyzed to validate the proposed approach's efficacy. Preliminary results align with expected outcomes, indicating a promising advancement in TSP solutions.

Keywords: optimization problems, traveling salesman problem, heuristic algorithms, “onion husk” algorithm, pseudo-geometric version

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Authors: Saikiran Subbagari, Avinash Malladhi

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Anti Money Laundering (AML) regulations are designed to prevent money laundering and terrorist financing activities worldwide. Financial institutions around the world are legally obligated to identify, assess and mitigate the risks associated with money laundering and report any suspicious transactions to governing authorities. With increasing volumes of data to analyze, financial institutions seek to automate their AML processes. In the rise of financial crimes, optical character recognition (OCR), in combination with machine learning (ML) algorithms, serves as a crucial tool for automating AML processes by extracting the data from documents and identifying suspicious transactions. In this paper, we examine the utilization of OCR for AML and delve into various OCR techniques employed in AML processes. These techniques encompass template-based, feature-based, neural network-based, natural language processing (NLP), hidden markov models (HMMs), conditional random fields (CRFs), binarizations, pattern matching and stroke width transform (SWT). We evaluate each technique, discussing their strengths and constraints. Also, we emphasize on how OCR can improve the accuracy of customer identity verification by comparing the extracted text with the office of foreign assets control (OFAC) watchlist. We will also discuss how OCR helps to overcome language barriers in AML compliance. We also address the implementation challenges that OCR-based AML systems may face and offer recommendations for financial institutions based on the data from previous research studies, which illustrate the effectiveness of OCR-based AML.

Keywords: anti-money laundering, compliance, financial crimes, fraud detection, machine learning, optical character recognition

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Authors: Raana Babadi Fathipour

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Tea, a beverage widely cherished across the globe, has captured the interest of scholars with its recent acknowledgement for possessing noteworthy health advantages. Of particular significance is its proven ability to ward off ailments such as cancer and cardiovascular afflictions. Moreover, within the realm of culinary creations, lipid oxidation poses a significant challenge for food product development. In light of these aforementioned concerns, this present discourse turns its attention towards exploring diverse methodologies employed in extracting polyphenols from various types of tea leaves and examining their utility within the vast landscape of the ever-evolving food industry. Based on the discoveries unearthed in this comprehensive investigation, it has been determined that the fundamental constituents of tea are polyphenols possessed of intrinsic health-enhancing properties. This includes an assortment of catechins, namely epicatechin, epigallocatechin, epicatechin gallate, and epigallocatechin gallate. Moreover, gallic acid, flavonoids, flavonols and theaphlavins have also been detected within this aromatic beverage. Of these myriad components examined vigorously in this study's analysis, catechin emerges as particularly beneficial. Multiple techniques have emerged over time to successfully extract key compounds from tea plants, including solvent-based extraction methodologies, microwave-assisted water extraction approaches and ultrasound-assisted extraction techniques. In particular, consideration is given to microwave-assisted water extraction method as a viable scheme which effectively procures valuable polyphenols from tea extracts. This methodology appears adaptable for implementation within sectors such as dairy production along with meat and oil industries alike.

Keywords: camellia sinensis, extraction, food application, shelf life, tea

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Authors: Chanhyuck Jeon, Jongho Park, Jinwoong Choi, Sungnam Hong, Sun-Kyu Park

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Many industries put emphasis on environmentally-friendliness as environmental problems are on the rise all over the world. Among themselves, the Modular Bridge research is going on. Also performing cross-section optimization and duration reducing, this research aims at developing the modular bridge with Environment-Friendliness and economic feasibility. However, the difficulty lies in verifying environmental effectiveness because there are no field applications of the modular bridge until now. Therefore, this thesis is categorized according to the form of the modular bridge superstructure and assessed CO₂ emission quantification per work types and materials according to each form to verify the environmental effectiveness of the modular bridge.

Keywords: modular bridge, CO2 emission, environmentally friendly, quantification, carbon emission factor, LCA (Life Cycle Assessment)

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Authors: Mandlenkosi George Robert Mahlobo, Tumelo Seadira, Major Melusi Mabuza, Peter Apata Olubambi

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Cathodic protection (CP) has been widely considered a suitable technique for mitigating corrosion of buried metal structures. Plenty of efforts have been made in developing techniques, in particular non-destructive techniques, for monitoring and quantifying the effectiveness of CP to ensure the sustainability and performance of buried steel structures. The aim of this study was to investigate the evolution of the electrochemical processes at the steel/soil interface during the application of CP on steel in simulated soil. Carbon steel was subjected to electrochemical tests with NS4 solution used as simulated soil conditions for 4 days before applying CP for a further 11 days. A previously modified non-destructive voltammetry technique was applied before and after the application of CP to measure the corrosion rate. Electrochemical impedance spectroscopy (EIS), in combination with mathematical modeling through equivalent electric circuits, was applied to determine the electrochemical behavior at the steel/soil interface. The measured corrosion rate was found to have decreased from 410 µm/yr to 8 µm/yr between days 5 and 14 because of the applied CP. Equivalent electrical circuits were successfully constructed and used to adequately model the EIS results. The modeling of the obtained EIS results revealed the formation of corrosion products via a mixed activation-diffusion mechanism during the first 4 days, while the activation mechanism prevailed in the presence of CP, resulting in a protective film. The x-ray diffraction analysis confirmed the presence of corrosion products and the predominant protective film corresponding to the calcareous deposit.

Keywords: carbon steel, cathodic protection, NS4 solution, voltammetry, EIS

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Authors: Nishi Bhuvandas, P. V. Timbadiya, P. L. Patel, P. D. Porey

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Recent perceived climate variability raises concerns with unprecedented hydrological phenomena and extremes. Distribution and circulation of the waters of the Earth become increasingly difficult to determine because of additional uncertainty related to anthropogenic emissions. According to the sixth Intergovernmental Panel on Climate Change (IPCC) Technical Paper on Climate Change and water, changes in the large-scale hydrological cycle have been related to an increase in the observed temperature over several decades. Although many previous research carried on effect of change in climate on hydrology provides a general picture of possible hydrological global change, new tools and frameworks for modelling hydrological series with nonstationary characteristics at finer scales, are required for assessing climate change impacts. Of the downscaling techniques, dynamic downscaling is usually based on the use of Regional Climate Models (RCMs), which generate finer resolution output based on atmospheric physics over a region using General Circulation Model (GCM) fields as boundary conditions. However, RCMs are not expected to capture the observed spatial precipitation extremes at a fine cell scale or at a basin scale. Statistical downscaling derives a statistical or empirical relationship between the variables simulated by the GCMs, called predictors, and station-scale hydrologic variables, called predictands. The main focus of the paper is on the need for using statistical downscaling techniques for projection of local hydrometeorological variables under climate change scenarios. The projections can be then served as a means of input source to various hydrologic models to obtain streamflow, evapotranspiration, soil moisture and other hydrological variables of interest.

Keywords: climate change, downscaling, GCM, RCM

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Authors: Hira Jabbar

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Education is vital for inclusive growth of an economy and a critical contributor for investment in human capital. Like other developing countries, Pakistan is facing enormous challenges regarding the provision of public facilities, improper infrastructure planning, accelerating rate of population and poor accessibility. The influence of the rapid advancement and innovations in GIS and RS techniques have proved to be a useful tool for better planning and decision making to encounter these challenges. Therefore present study incorporates GIS and RS techniques to investigate the spatial distribution of school facilities, identifies settlements with served and unserved population, finds potential areas for new schools based on population and develops an accessibility index to evaluate the higher accessibility for schools. For this purpose high-resolution worldview imagery was used to develop road network, settlements and school facilities and to generate school accessibility for each level. Landsat 8 imagery was utilized to extract built-up area by applying pre and post-processing models and Landscan 2015 was used to analyze population statistics. Service area analysis was performed using network analyst extension in ArcGIS 10.3v and results were evaluated for served and underserved areas and population. An accessibility tool was used to evaluate a set of potential destinations to determine which is the most accessible with the given population distribution. Findings of the study may contribute to facilitating the town planners and education authorities for understanding the existing patterns of school facilities. It is concluded that GIS and remote sensing can be effectively used in urban transport and facility planning.

Keywords: accessibility, geographic information system, landscan, worldview

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Authors: Salman Amur Alhajri

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Purpose: This paper explores the art of contemporary Arabic calligraphy in Oman. It explains the aesthetic features of Arabic calligraphy as a unique icon of Islamic art. This paper also explores the profile of one Omani artist, Salman Alhajri, as an example of Omani artists who have developed unique styles in this art stream. Methodology and approach: The paper is based on a theoretical study using a descriptive and case-study approach. Omani artists are fascinated by the art forms of Arabic calligraphy, which combine both spiritual meaning and aesthetic beauty. Artist Salman Alhajri is an example of a contemporary Arabic artist who uses Arabic calligraphy as the main theme in his art. Dr. Alhajri is trying to introduce the beauty of Arabic letters from a new aesthetic point of view. He also aims to create unusual visual effects that viewers can easily interact with. Even though words and phrases appear in Alhajri’s artwork, they are not conveying direct meanings: viewers can create their own meaning or expressions from them by appreciating the compositions of the artwork. Results: Arabic writing is directly related to the identity of Omani artists and their cultural background. This paper shows how the beauty of Arabic letters comes from its indefinite possibilities in designing calligraphic expressions, even within a single word, because letters can be stretched and transformed in various ways to create different compositions. Omani artists are interested in employing new media applications in this kind of practice to find new techniques for creating artwork based on Arabic writing. It is really important for all Omani artists to practice this art style because Arabic calligraphy and its flexibility introduce infinite possibilities that involve further exploration and investigation.

Keywords: Islamic art, contemporary Arabic calligraphy, new techniques, Omani artist

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Authors: Shahad Nagoor, Lucy Hederman, Kevin Koidl, Annalina Caputo

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Doctors’ notes reflect their impressions, attitudes, clinical sense, and opinions about patients’ conditions and progress, and other information that is essential for doctors’ daily clinical decisions. Despite their value, clinical notes are insufficiently researched within the language processing community. Automatically extracting information from unstructured text data is known to be a difficult task as opposed to dealing with structured information such as vital physiological signs, images, and laboratory results. The aim of this research is to investigate how Natural Language Processing (NLP) techniques and machine learning techniques applied to clinician notes can assist in doctors’ decision-making in Intensive Care Unit (ICU) for coronavirus disease 2019 (COVID-19) patients. The hypothesis is that clinical outcomes like survival or mortality can be useful in influencing the judgement of clinical sentiment in ICU clinical notes. This paper introduces two contributions: first, we introduce COVID_ICU_BERT, a fine-tuned version of clinical transformer models that can reliably predict clinical sentiment for notes of COVID patients in the ICU. We train the model on clinical notes for COVID-19 patients, a type of notes that were not previously seen by clinicalBERT, and Bio_Discharge_Summary_BERT. The model, which was based on clinicalBERT achieves higher predictive accuracy (Acc 93.33%, AUC 0.98, and precision 0.96 ). Second, we perform data augmentation using clinical contextual word embedding that is based on a pre-trained clinical model to balance the samples in each class in the data (survived vs. deceased patients). Data augmentation improves the accuracy of prediction slightly (Acc 96.67%, AUC 0.98, and precision 0.92 ).

Keywords: BERT fine-tuning, clinical sentiment, COVID-19, data augmentation

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Authors: Josef Brychta, Jiri Kratochvil, Marek Pagac

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The group of progressive cutting materials can include non-traditional, emerging and less-used materials that can be an efficient use of cutting their lead to a quantum leap in the field of machining. This is essentially a “superhard” materials (STM) based on polycrystalline diamond (PCD) and polycrystalline cubic boron nitride (PCBN) cutting performance ceramics and development is constantly "perfecting" fine coated cemented carbides. The latter cutting materials are broken down by two parameters, toughness and hardness. A variation of alloying elements is always possible to improve only one of each parameter. Reducing the size of the core on the other hand doing achieves "contradictory" properties, namely to increase both hardness and toughness.

Keywords: grained cutting materials difficult to machine materials, optimum utilization, mechanic, manufacturing

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Authors: Addisu Tadesse Sahile, Tennyson Mgutshini

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Background: Periodontal disease is a common, complex, inflammatory disease characterized by the destruction of tooth-supporting soft and hard tissues of the periodontium and a major public health problem across developed and developing countries. Objectives: The study was aimed at assessing the prevalence of periodontal disease and associated factors among diabetes patients in Addis Ababa, Ethiopia, 2018. Methods: Institutional based cross-sectional study was conducted on 388 diabetes patients selected by systematic random sampling method from March to May 2018. The study was conducted at two conveniently selected public hospitals in Addis Ababa. Data were collected with pre-tested, structured and translated questionnaire then entered to SPSS version 23 software for analysis. Descriptive statistics as a summary, in line with chi-square and binary logistics regression to identify factors associated with periodontal disease, were applied. A 95% CI with a p-value less than 5% was used as a level of significance. Results: Ninety-one percent (n=353) of participants had periodontal disease while oral examination was done in six regions. While only 9% (n=35) of participants were free of periodontal disease. The number of tooth brushings per day, correct techniques of brushing, malocclusion, and fillings that are defective were associated with periodontal disease at p < 0.05. Conclusion and recommendation: A higher prevalence of periodontal disease among diabetes patient was observed. The frequency of tooth brushing, correct techniques of brushing, malocclusion and defective fillings were associated with periodontal disease. Emphasis has to be given to oral health of diabetes patients by every concerned body so as to control the current higher burden of periodontal disease in diabetes.

Keywords: periodontal disease, risk factors, diabetes mellitus, Addis Ababa

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Authors: Famuwagun A. A., Abiona O. O., Gbadamosi S.O., Adeboye O. A., Adebooye O. C.

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The need to provide more information on the perceived bioactive status of the peel of plantain fruit informed the design of this research. Matured Plantain fruits were harvested, and fruits were allowed to ripen spontaneously. Samples of plantain fruit were taken every fortnight, and the peels were removed. The peels were dried using two different drying techniques (Oven drying and sun drying) and milled into powdery forms. Other samples were picked and processed in a similar manner on the first, third, seventh and tenth day until the peels of the fruits were fully ripped, resulting in eight different samples. The anti-oxidative properties of the samples using different assays (DPPH, FRAP, MCA, HRSA, SRSA, ABTS, ORAC), inhibitory activities against enzymes related to diabetes (alpha-amylase and glucosidase) and inhibition against angiotensin-converting enzymes (ACE) were evaluated. The result showed that peels of plantain fruits on the 7th day of ripening and sundried exhibited greater inhibitions against free radicals, which enhanced its antioxidant activities, resulting in greater inhibitions against alpha-amylase and alpha-glucosidase enzymes. Also, oven oven-dried sample of the peel of plantain fruit on the 7th day of ripening had greater phenolic contents than the other samples, which also resulted in higher inhibition against angiotensin converting enzymes when compared with other samples. The results showed that even though the unripe peel of plantain fruit is assumed to contain excellent bioactive activities, consumption of the peel should be allowed to ripen for seven days after maturity and harvesting so as to derive maximum benefit from the peel.

Keywords: functional ingredient, diabetics, hypertension, functional foods

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Authors: Amar Khoukhi, Mohamed Shahab

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This paper presents the optimal control problem of mobile robot motion as a nonlinear programming problem (NLP) and solved using a direct method of numerical optimal control. The NLP is initialized with a B-Spline for which node locations are optimized using a genetic search. The system acceleration inputs and sampling periods are considered as optimization variables. Different scenarios with different objectives weights are implemented and investigated. Interesting results are found in terms of complying with the expected behavior of a mobile robot system and time-energy minimization.

Keywords: multi-objective control, non-holonomic systems, mobile robots, nonlinear programming, motion planning, B-spline, genetic algorithm

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Authors: Manuel Alejandro Taborda Ceballos, Martin Sommerfeld

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Transport phenomena and dispersion of non-spherical particle in turbulent flows are found everywhere in industrial application and processes. Powder handling, pollution control, pneumatic transport, particle separation are just some examples where the particle encountered are not only spherical. These types of multiphase flows are wall bounded and mostly highly turbulent. The particles found in these processes are rarely spherical but may have various shapes (e.g., fibers, and rods). Although research related to the behavior of regular non-spherical particles in turbulent flows has been carried out for many years, it is still necessary to refine models, especially near walls where the interaction fiber-wall changes completely its behavior. Imaging-based experimental studies on dispersed particle-laden flows have been applied for many decades for a detailed experimental analysis. These techniques have the advantages that they provide field information in two or three dimensions, but have a lower temporal resolution compared to point-wise techniques such as PDA (phase-Doppler anemometry) and derivations therefrom. The applied imaging techniques in dispersed two-phase flows are extensions from classical PIV (particle image velocimetry) and PTV (particle tracking velocimetry) and the main emphasis was simultaneous measurement of the velocity fields of both phases. In a similar way, such data should also provide adequate information for validating the proposed models. Available experimental studies on the behavior of non-spherical particles are uncommon and mostly based on planar light-sheet measurements. Especially for elongated non-spherical particles, however, three-dimensional measurements are needed to fully describe their motion and to provide sufficient information for validation of numerical computations. For further providing detailed experimental results allowing a validation of numerical calculations of non-spherical particle dispersion in turbulent flows, a water channel test facility was built around a horizontal closed water channel. Into this horizontal main flow, a small cross-jet laden with fiber-like particles was injected, which was also solely driven by gravity. The dispersion of the fibers was measured by applying imaging techniques based on a LED array for backlighting and high-speed cameras. For obtaining the fluid velocity fields, almost neutrally buoyant tracer was used. The discrimination between tracer and fibers was done based on image size which was also the basis to determine fiber orientation with respect to the inertial coordinate system. The synchronous measurement of fluid velocity and fiber properties also allow the collection of statistics of fiber orientation, velocity fields of tracer and fibers, the angular velocity of the fibers and the orientation between fiber and instantaneous relative velocity. Consequently, an experimental study the behavior of elongated non-spherical particles in wall bounded turbulent flows was achieved. The development of a comprehensive analysis was succeeded, especially near the wall region, where exists hydrodynamic wall interaction effects (e.g., collision or lubrication) and abrupt changes of particle rotational velocity. This allowed us to predict numerically afterwards the behavior of non-spherical particles within the frame of the Euler/Lagrange approach, where the particles are therein treated as “point-particles”.

Keywords: crossflow, non-spherical particles, particle tracking velocimetry, PIV

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Authors: Aleksander Ejsmont, Stefan Wuttke, Joanna Goscianska

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Gaining control over particle shape, size and crystallinity is an ongoing challenge for many materials. Especially metalorganic frameworks (MOFs) are recently widely studied. Besides their remarkable porosity and interesting topologies, morphology has proven to be a significant feature. It can affect the further material application. Thus seeking new approaches that enable MOF morphology modulation is important. MOFs are reticular structures, where building blocks are made up of organic linkers and metallic nodes. The most common strategy of ensuring metal source is using salts, which usually exhibit high solubility and hinder morphology control. However, there has been a growing interest in using metal oxides as structure-directing agents towards MOFs due to their very low solubility and shape preservation. Metal oxides can be treated as a metal reservoir during MOF synthesis. Up to now, reports in which receiving MOFs from metal oxides mostly present ZnO conversion to ZIF-8. However, there are other oxides, for instance, Co₃O₄, which often is overlooked due to their structural stability and insolubility in aqueous solutions. Cobalt-based materials are famed for catalytic activity. Therefore the development of their efficient synthesis is worth attention. In the presented work, an optimized Co₃O₄transition to Co-MOFviaa solvothermal approach was proposed. The starting point of the research was the synthesis of Co₃O₄ flower petals and needles under hydrothermal conditions using different cobalt salts (e.g., cobalt(II) chloride and cobalt(II) nitrate), in the presence of urea, and hexadecyltrimethylammonium bromide (CTAB) surfactant as a capping agent. After receiving cobalt hydroxide, the calcination process was performed at various temperatures (300–500 °C). Then cobalt oxides as a source of cobalt cations were subjected to reaction with trimesic acid in solvothermal environment and temperature of 120 °C leading to Co-MOF fabrication. The solution maintained in the system was a mixture of water, dimethylformamide, and ethanol, with the addition of strong acids (HF and HNO₃). To establish how solvents affect metal oxide conversion, several different solvent ratios were also applied. The materials received were characterized with analytical techniques, including X-ray powder diffraction, energy dispersive spectroscopy,low-temperature nitrogen adsorption/desorption, scanning, and transmission electron microscopy. It was confirmed that the synthetic routes have led to the formation of Co₃O₄ and Co-based MOF varied in shape and size of particles. The diffractograms showed receiving crystalline phase for Co₃O₄, and also for Co-MOF. The Co₃O₄ obtained from nitrates and with using low-temperature calcination resulted in smaller particles. The study indicated that cobalt oxide particles of different size influence the efficiency of conversion and morphology of Co-MOF. The highest conversion was achieved using metal oxides with small crystallites.

Keywords: Co-MOF, solvothermal synthesis, morphology control, core-shell

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Authors: Saritdikhun Somasa

Abstract:

3D printing fabrication has empowered many artists in many fields. Artists who work in stop motion, 3D modeling, toy design, product design, sculpture, and fine arts become one-stop shop operations–where they can design, prototype, and distribute their designs for commercial or fine art purposes. The author has developed a digital sculpting course that fosters digital software, peripheral hardware, and 3D printing with traditional sculpting concept techniques to address the complexities of this multifaceted process, allowing the students to produce complex 3d-printed work. The author will detail the preparation and planning for pre- to post-process 3D printing elements, including software, materials, space, equipment, tools, and schedule consideration for small to medium figurine design statues in a semester-long class. In addition, the author provides insight into teaching challenges in the non-studio space that requires students to work intensively on post-printed models to assemble parts, finish, and refine the 3D printed surface. Even though this paper focuses on the 3D printing processes and techniques for small to medium design statue projects for the Digital Media program, the author hopes the paper will benefit other fields of study such as craft practices, product design, and fine-arts programs. Other schools that might implement 3D printing and fabrication in their programs will find helpful information in this paper, such as a teaching plan, choices of equipment and materials, adaptation for non-studio spaces, and putting together a complete and well-resolved project for students.

Keywords: 3D digital modeling, 3D digital sculpting, 3D modeling, 3D printing, 3D digital fabrication

Procedia PDF Downloads 99

Authors: Majid Abdolrazzagh-Nezhad, Salwani Abdullah

Abstract:

The job-shop scheduling problem (JSSP) is an important decision facing those involved in the fields of industry, economics and management. This problem is a class of combinational optimization problem known as the NP-hard problem. JSSPs deal with a set of machines and a set of jobs with various predetermined routes through the machines, where the objective is to assemble a schedule of jobs that minimizes certain criteria such as makespan, maximum lateness, and total weighted tardiness. Over the past several decades, interest in meta-heuristic approaches to address JSSPs has increased due to the ability of these approaches to generate solutions which are better than those generated from heuristics alone. This article provides the classification, constraints and objective functions imposed on JSSPs that are available in the literature.

Keywords: job-shop scheduling, classification, constraints, objective functions

Procedia PDF Downloads 439

Authors: Eyup Dogan

Abstract:

One criticism of the energy-growth-environment literature, to the best of our knowledge, is that only a few studies analyze the influence of tourism on CO₂ emissions even though tourism sector is closely related to the environment. The other criticism is the selection of methodology. Panel estimation techniques that fail to consider both heterogeneity and cross-sectional dependence across countries can cause forecasting errors. To fulfill the mentioned gaps in the literature, this study analyzes the impacts of real GDP, renewable energy and tourism on the levels of carbon dioxide (CO₂) emissions for the top 10 most-visited countries around the world. This study focuses on the top 10 touristic (most-visited) countries because they receive about the half of the worldwide tourist arrivals in late years and are among the top ones in 'Renewables Energy Country Attractiveness Index (RECAI)'. By looking at Pesaran’s CD test and average growth rates of variables for each country, we detect the presence of cross-sectional dependence and heterogeneity. Hence, this study uses second generation econometric techniques (cross-sectionally augmented Dickey-Fuller (CADF), and cross-sectionally augmented IPS (CIPS) unit root test, the LM bootstrap cointegration test, and the DOLS and the FMOLS estimators) which are robust to the mentioned issues. Therefore, the reported results become accurate and reliable. It is found that renewable energy mitigates the pollution whereas real GDP and tourism contribute to carbon emissions. Thus, regulatory policies are necessary to increase the awareness of sustainable tourism. In addition, the use of renewable energy and the adoption of clean technologies in tourism sector as well as in producing goods and services play significant roles in reducing the levels of emissions.

Keywords: air pollution, tourism, renewable energy, income, panel data

Procedia PDF Downloads 177

Authors: John Morales, Eduardo Orduña

Abstract:

Currently the Principal Component Analysis (PCA) theory has been used to develop algorithms regarding to Electric Power Systems (EPS). In this context, this paper presents a practical tutorial of this technique detailed their concept, on-line and off-line mathematical foundations, which are necessary and desirables in EPS algorithms. Thus, features of their eigenvectors which are very useful to real-time process are explained, showing how it is possible to select these parameters through a direct optimization. On the other hand, in this work in order to show the application of PCA to off-line and on-line signals, an example step to step using Matlab commands is presented. Finally, a list of different approaches using PCA is presented, and some works which could be analyzed using this tutorial are presented.

Keywords: practical guide; on-line; off-line, algorithms, faults

Procedia PDF Downloads 557

Authors: D. P. Lawrence, T. M. M. D. Ariyarathna, W. N. K. De Silva, M. D. S. C. De Silva, Lasantha Abeysiri, Pradeep Abeygunawardhna

Abstract:

Safari traveling is one of the most famous hobbies all over the world. In Sri Lanka, 'Yala' is the second-largest national park, which is a better place to go for a safari. Many number of local and foreign travelers are coming to go for a safari in 'Yala'. But 'Yala' does not have a mobile application that is made to facilitate the traveler with some important features that the traveler wants to achieve in the safari experience. To overcome these difficulties, the proposed mobile application by adding those identified features to make travelers, guiders, and administration's works easier. The proposed safari traveling guidance mobile application is called 'SMART SAFARI' for the 'Yala' National Park in Sri Lanka. There are four facilities in this mobile application that provide for travelers as well as the guiders. As the first facility, the guider and traveler can view the created map of the park, and the guider can add temporary locations of animals and special locations on the map. This is a Geographic Information System (GIS) to capture, analyze, and display geographical data. And as the second facility is to generate optimal paths according to the travelers' requirements through the park by using machine learning techniques. In the third part, the traveler can get information about animals using an animal identification system by capturing the animal. As in the other facility, the traveler will be facilitated to add reviews and a rate and view those comments under categorized sections and pre-defined score range. With those facilities, this user-friendly mobile application provides the user to get a better experience in safari traveling, and it will probably help to develop tourism culture in Sri Lanka.

Keywords: animal identification system, geographic information system, machine learning techniques, pre defined score range

Procedia PDF Downloads 126

Authors: Sou Watanabe, Hiromichi Ogi, Atsuhiro Shibata, Kazunori Nomura

Abstract:

As a part of STRAD project conducted by JAEA, condensation of radioactive liquid waste containing various chemical compounds using reverse osmosis (RO) membrane filter was examined for efficient and safety treatment of the liquid wastes accumulated inside hot laboratories. NH₄⁺ ion in the feed solution was successfully concentrated, and NH₄⁺ ion involved in the effluents became lower than target value; 100 ppm. Solidification of simulated aqueous and organic liquid wastes was also tested. Those liquids were successfully solidified by adding cement or coagulants. Nevertheless, optimization in materials for confinement of chemicals is required for long time storage of the final solidified wastes.

Keywords: condensation, radioactive liquid waste, solidification, STRAD project

Procedia PDF Downloads 151