Search results for: fruit fly optimization algorithm

Authors: Adil Basuki Ahza, Michael Liong, Subarna Suryatman

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Healthy chips now dominating the snack market shelves. More than 80% processed snack foods in the market are chips. This research takes the advantages of twin extrusion technology to produce two types of product, i.e. directly expanded and intermediate ready-to-fry or microwavable chips. To improve the functional quality, the cereal-tuber based mix was enriched with antioxidant rich mix of temurui, celery, carrot and isolated soy protein (ISP) powder. Objectives of this research were to find best composite cassava-sorghum ratio, i.e. 60:40, 70:30 and 80:20, to optimize processing conditions of extrusion and study the microstructural, physical and sensorial characteristics of the final products. Optimization was firstly done by applying metering section of extruder barrel temperatures of 120, 130 and 140 °C with screw speeds of 150, 160 and 170 rpm to produce direct expanded product. The intermediate product was extruded in 100 °C and 100 rpm screw speed with feed moisture content of 35, 40 and 45%. The directly expanded products were analyzed for color, hardness, density, microstructure, and organoleptic properties. The results showed that interaction of ratio of cassava-sorghum and cooking methods affected the product's color, hardness, and bulk density (p<0.05). Extrusion processing conditions also significantly affected product's microstructure (p<0.05). The direct expanded snacks of 80:20 cassava-sorghum ratio and fried expanded one 70:30 and 80:20 ratio shown the best organoleptic score (slightly liked) while baking the intermediate product with microwave were resulted sensorial not acceptable quality chips.

Keywords: cassava-sorghum composite, extrusion, microstructure, physical characteristics

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Authors: Sofia M. Karadimitriou, Kostas Triantafyllopoulos, Timothy Heaton

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Reduced dimension Dynamic Spatio-Temporal Models (DSTMs) jointly describe the spatial and temporal evolution of a function observed subject to noise. A basic state space model is adopted for the discrete temporal variation, while a continuous autoregressive structure describes the continuous spatial evolution. Application of such a DSTM relies upon the pre-selection of a suitable reduced set of basic functions and this can present a challenge in practice. In this talk, we propose an online estimation method for high dimensional spatio-temporal data based upon DSTM and we attempt to resolve this issue by allowing the basis to adapt to the observed data. Specifically, we present a wavelet decomposition in order to obtain a parsimonious approximation of the spatial continuous process. This parsimony can be achieved by placing a Laplace prior distribution on the wavelet coefficients. The aim of using the Laplace prior, is to filter wavelet coefficients with low contribution, and thus achieve the dimension reduction with significant computation savings. We then propose a Hierarchical Bayesian State Space model, for the estimation of which we offer an appropriate particle filter algorithm. The proposed methodology is illustrated using real environmental data.

Keywords: multidimensional Laplace prior, particle filtering, spatio-temporal modelling, wavelets

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Authors: Mauricio Cruz, Andrés Díaz García, Martha Isabel Gómez, Juan Carlos Serrato Bermúdez

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The disadvantages of using natural substrates in SSF processes have been well recognized and mainly are associated to gradual decomposition of the substrate, formation of agglomerates and decrease of porosity bed generating limitations in the mass and heat transfer. Additionally, in several cases, materials with a high agricultural value such as sour milk, beets, rice, beans and corn have been used. Thus, the use of economic inert supports (natural or synthetic) in combination with a nutrient suspension for the production of biocontrol microorganisms is a good alternative in SSF processes, but requires further studies in the fields of modeling and optimization. Therefore, the aim of this work is to compare the performance of two inert supports, a synthetic (polyurethane foam) and a natural one (rice husk), identifying the factors that have the major effects on the productivity of T. asperellum Th204 and the maximum specific growth rate in a PROPHYTA L05® fixed bed bioreactor. For this, the six factors C:N ratio, temperature, inoculation rate, bed height, air moisture content and airflow were evaluated using a fractional design. The factors C:N and air flow were identified as significant on the productivity (expressed as conidia/dry substrate•h). The polyurethane foam showed higher maximum specific growth rate (0.1631 h-1) and productivities of 3.89 x107 conidia/dry substrate•h compared to rice husk (2.83x106) and natural substrate based on rice (8.87x106) used as control. Finally, a quadratic model was generated and validated, obtaining productivities higher than 3.0x107 conidia/dry substrate•h with air flow at 0.9 m3/h and C:N ratio at 18.1.

Keywords: bioprocess, scale up, fractional design, C:N ratio, air flow

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Authors: Bingchun Liu, Pei-Chann Chang, Natasha Huang, Dun Li

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Machine Learning and Data Mining are the two important tools for extracting useful information and knowledge from large datasets. In machine learning, classification is a wildly used technique to predict qualitative variables and is generally preferred over regression from an operational point of view. Due to the enormous increase in air pollution in various countries especially China, Air Quality Classification has become one of the most important topics in air quality research and modelling. This study aims at introducing a hybrid classification model based on information theory and Support Vector Machine (SVM) using the air quality data of four cities in China namely Beijing, Guangzhou, Shanghai and Tianjin from Jan 1, 2014 to April 30, 2016. China's Ministry of Environmental Protection has classified the daily air quality into 6 levels namely Serious Pollution, Severe Pollution, Moderate Pollution, Light Pollution, Good and Excellent based on their respective Air Quality Index (AQI) values. Using the information theory, information gain (IG) is calculated and feature selection is done for both categorical features and continuous numeric features. Then SVM Machine Learning algorithm is implemented on the selected features with cross-validation. The final evaluation reveals that the IG and SVM hybrid model performs better than SVM (alone), Artificial Neural Network (ANN) and K-Nearest Neighbours (KNN) models in terms of accuracy as well as complexity.

Keywords: machine learning, air quality classification, air quality index, information gain, support vector machine, cross-validation

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Authors: Morlu Stevens, Bareki Batlokwa

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The use of treated wastewater is widely employed to compensate for the scarcity of safe and uncontaminated freshwater. However, the existence of toxic heavy metal ions in the wastewater pose a health hazard to animals and the environment, hence, the importance for an effective technique to tackle the challenge. A multi-templated ion imprinted sorbent (Fe,Ni,Cu-IIP) for the simultaneous removal of heavy metal ions from waste water was synthesised employing molecular imprinting technology (MIT) via thermal free radical bulk polymerization technique. Methacrylic acid (MAA) was employed as the functional monomer, and ethylene glycol dimethylacrylate (EGDMA) as cross-linking agent, azobisisobutyronitrile (AIBN) as the initiator, Fe, Ni, Cu ions as template ions, and 1,10-phenanthroline as the complexing agent. The template ions were exhaustively washed off the synthesized polymer by solvent extraction in several washing steps, while periodically increasing solvent (HCl) concentration from 1.0 M to 10.0 M. The physical and chemical properties of the sorbents were investigated using Fourier Transform Infrared Spectroscopy (FT-IR), X-ray Diffraction (XRD) and Atomic Force Microscopy (AFM) were employed. Optimization of operational parameters such as time, pH and sorbent dosage to evaluate the effectiveness of sorbents were investigated and found to be 15 min, 7.5 and 666.7 mg/L respectively. Selectivity of ion-imprinted polymers and competitive sorption studies between the template and similar ions were carried out and showed good selectivity towards the targeted metal ion by removing 90% - 98% of the templated ions as compared to 58% - 62% of similar ions. The sorbents were further applied for the selective removal of Fe, Ni and Cu from real wastewater samples and recoveries of 92.14 ± 0.16% - 106.09 ± 0.17% and linearities of R2 = 0.9993 - R2 = 0.9997 were achieved.

Keywords: ion imprinting, ion imprinted polymers, heavy metals, wastewater

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Authors: Marcello De Falco, Gianluca Natrella, Mauro Capocelli

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CO₂ capture and utilization (CCU) is a promising approach to reduce GHG(greenhouse gas) emissions. Many technologies in this field are recently attracting attention. However, since CO₂ is a very stable compound, its utilization as a reagent is energetic intensive. As a consequence, it is unclear whether CCU processes allow for a net reduction of environmental impacts from a life cycle perspective and whether these solutions are sustainable. Among the tools to apply for the quantification of the real environmental benefits of CCU technologies, exergy analysis is the most rigorous from a scientific point of view. The exergy of a system is the maximum obtainable work during a process that brings the system into equilibrium with its reference environment through a series of reversible processes in which the system can only interact with such an environment. In other words, exergy is an “opportunity for doing work” and, in real processes, it is destroyed by entropy generation. The exergy-based analysis is useful to evaluate the thermodynamic inefficiencies of processes, to understand and locate the main consumption of fuels or primary energy, to provide an instrument for comparison among different process configurations and to detect solutions to reduce the energy penalties of a process. In this work, the exergy analysis of a process for the production of Dimethyl Ether (DME) from green hydrogen generated through an electrolysis unit and pure CO₂ captured from flue gas is performed. The model simulates the behavior of all units composing the plant (electrolyzer, carbon capture section, DME synthesis reactor, purification step), with the scope to quantify the performance indices based on the II Law of Thermodynamics and to identify the entropy generation points. Then, a plant optimization strategy is proposed to maximize the exergy efficiency.

Keywords: green DME production, exergy analysis, energy penalties, exergy efficiency

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Authors: Yujie Chen , Zhen Li

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In the context of planning transformation, how to promote public participation in the formulation and implementation of collaborative planning has been the focused issue of discussion. However, existing studies have often been case-specific or focused on a specific design field, leaving the role of participatory social media (PSM) in urban collaborative planning generally questioned. A systematic database search was conducted in December 2019. Articles and projects were eligible if they reported a quantitative empirical study applying participatory social media in the collaborative planning process (a prospective, retrospective, experimental, longitudinal research, or collective actions in planning practices). Twenty studies and seven projects were included in the review. Findings showed that social media are generally applied in public spatial behavior, transportation behavior, and community planning fields, with new technologies and new datasets. PSM has provided a new platform for participatory design, decision analysis, and collaborative negotiation most widely used in participatory design. Findings extracted several existing forms of PSM. PSM mainly act as three roles: the language of decision-making for communication, study mode for spatial evaluation, and decision agenda for interactive decision support. Three optimization content of PSM were recognized, including improving participatory scale, improvement of the grass-root organization, and promotion of politics. However, basically, participants only could provide information and comment through PSM in the future collaborative planning process, therefore the issues of low data response rate, poor spatial data quality, and participation sustainability issues worth more attention and solutions.

Keywords: participatory social media, collaborative planning, planning workshop, application mode

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Authors: Mohamed Al-Badrashiny, Abdelghani Bellaachia

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This paper presents a classifier ensemble approach for predicting the survivability of the breast cancer patients using the latest database version of the Surveillance, Epidemiology, and End Results (SEER) Program of the National Cancer Institute. The system consists of two main components; features selection and classifier ensemble components. The features selection component divides the features in SEER database into four groups. After that it tries to find the most important features among the four groups that maximizes the weighted average F-score of a certain classification algorithm. The ensemble component uses three different classifiers, each of which models different set of features from SEER through the features selection module. On top of them, another classifier is used to give the final decision based on the output decisions and confidence scores from each of the underlying classifiers. Different classification algorithms have been examined; the best setup found is by using the decision tree, Bayesian network, and Na¨ıve Bayes algorithms for the underlying classifiers and Na¨ıve Bayes for the classifier ensemble step. The system outperforms all published systems to date when evaluated against the exact same data of SEER (period of 1973-2002). It gives 87.39% weighted average F-score compared to 85.82% and 81.34% of the other published systems. By increasing the data size to cover the whole database (period of 1973-2014), the overall weighted average F-score jumps to 92.4% on the held out unseen test set.

Keywords: classifier ensemble, breast cancer survivability, data mining, SEER

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Authors: Seema Ahlawat, Parul Saxena, Malik Zainul Abdin

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Malaria is a major health problem in many developing countries. The parasite responsible for the vast majority of fatal malaria infections is Plasmodium falciparum. Unfortunately, most Plasmodium strains including P. falciparum have become resistant to most of the antimalarials including chloroquine, mefloquine, etc. To combat this problem, WHO has recommended the use of artemisinin and its derivatives in artemisinin based combination therapy (ACT). Due to its current use in artemisinin based-combination therapy (ACT), its global demand is increasing continuously. But, the relatively low yield of artemisinin in A. annua L. plants and unavailability of economically viable synthetic protocols are the major bottlenecks for its commercial production and clinical use. Chemical synthesis of artemisinin is also very complex and uneconomical. The hairy root system, using the Agrobacterium rhizogenes LBA 9402 strain to enhance the production of artemisinin in A. annua L., is developed in our laboratory. The transgenic nature of hairy root lines and the copy number of trans gene (rol B) were confirmed using PCR and Southern Blot analyses, respectively. The effect of different concentrations of Piriformospora indica on artemisinin production in hairy root cultures were evaluated. 3% P. indica has resulted 1.97 times increase in artemisinin production in comparison to control cultures. The effects of P. indica on artemisinin production was positively correlated with regulatory genes of MVA, MEP and artemisinin biosynthetic pathways, viz. hmgr, ads, cyp71av1, aldh1, dxs, dxr and dbr2 in hairy root cultures of A. annua L. Mass scale cultivation of A. annua L. hairy roots by plant tissue culture technology may be an alternative route for production of artemisinin. A comprehensive investigation of the hairy root system of A. annua L. would help in developing a viable process for the production of artemisinin. The efficiency of the scaling up systems still needs optimization before industrial exploitation becomes viable.

Keywords: A. annua L., artemisinin, hairy root cultures, malaria

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Authors: Jiajia Yao, GuanLin Wu, Fang liu, JunShuai Xue, JinCheng Zhang, Yue Hao

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Due to its outstanding material properties like high thermal conductivity and ultra-wide bandgap, Aluminum nitride (AlN) has the promising potential to provide high breakdown voltage and high output power among III-nitrides for various applications in electronics and optoelectronics. This work presents material growth and characterization of strained channel Aluminum nitride/Gallium nitride (AlN/GaN) heterostructures grown by plasma-assisted molecular beam epitaxy (PA-MBE) on AlN-on-sapphire templates. To improve the crystal quality and manifest the ability of the PA-MBE approach, a thick AlN buffer with a thickness of 180 nm is first grown on AlN template, which acts as a back-barrier to enhance the breakdown characteristic and isolates the leakage path existing in the interface between AlN epilayer and AlN template, as well as improve the heat dissipation. The grown AlN buffer features a root-mean-square roughness of 0.2 nm over a scanned area of 2×2 µm2 measured by atomic force microscopy (AFM), and exhibits full-width at half-maximum of 95 and 407 arcsec for the (002) and (102) plane the X-ray rocking curve, respectively, tested by high resolution x-ray diffraction (HR-XRD). With a thin and strained GaN channel, the electron mobility of 294 cm2 /Vs. with a carrier concentration of 2.82×1013 cm-2 at room temperature is achieved in AlN/GaN double-channel heterostructures, and the depletion capacitance is as low as 14 pF resolved by the capacitance-voltage, which indicates the promising opportunities for future applications in next-generation high temperature, high-frequency and high-power electronics with a further increased electron mobility by optimization of heterointerface quality.

Keywords: AlN/GaN, HEMT, MBE, homoepitaxy

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Authors: Luísa P. Cruz-Lopes, Hugo Costa e Silva, Idalina Domingos, José Ferreira, Luís Teixeira de Lemos, Bruno Esteves

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The liquefaction process of cork based tree barks has led to an increase of interest due to its potential innovation in the lumber and wood industries. In this particular study the bark of Quercus cerris (Turkish oak) is used due to its appreciable amount of cork tissue, although of inferior quality when compared to the cork provided by other Quercus trees. This study aims to optimize alkaline catalysis liquefaction conditions, regarding several parameters. To better comprehend the possible chemical characteristics of the bark of Quercus cerris, a complete chemical analysis was performed. The liquefaction process was performed in a double-jacket reactor heated with oil, using glycerol and a mixture of glycerol/ethylene glycol as solvents, potassium hydroxide as a catalyst, and varying the temperature, liquefaction time and granulometry. Due to low liquefaction efficiency resulting from the first experimental procedures a study was made regarding different washing techniques after the filtration process using methanol and methanol/water. The chemical analysis stated that the bark of Quercus cerris is mostly composed by suberin (ca. 30%) and lignin (ca. 24%) as well as insolvent hemicelluloses in hot water (ca. 23%). On the liquefaction stage, the results that led to higher yields were: using a mixture of methanol/ethylene glycol as reagents and a time and temperature of 120 minutes and 200 ºC, respectively. It is concluded that using a granulometry of <80 mesh leads to better results, even if this parameter barely influences the liquefaction efficiency. Regarding the filtration stage, washing the residue with methanol and then distilled water leads to a considerable increase on final liquefaction percentages, which proves that this procedure is effective at liquefying suberin content and lignocellulose fraction.

Keywords: liquefaction, Quercus cerris, polyalcohol liquefaction, temperature

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Authors: K. Karapetyan, F. Tkhruni, A. Aghajanyan, T. S. Balabekyan, L. Arstamyan

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Introduction: Globalization of the food supply has created the conditions favorable for the emergence and spread of food-borne and especially dangerous pathogens (EDP) in developing countries. The fresh-cut fruit and vegetable industry is searching for alternatives to replace chemical treatments with biopreservative approaches that ensure the safety of the processed foods product. Antimicrobial compounds of lactic acid bacteria (LAB) possess bactericidal or bacteriostatic activity against intestinal pathogens, spoilage organisms and food-borne pathogens such as Listeria monocytogenes, Staphylococcus aureus and Salmonella. Endemic strains of LAB were isolated. The strains, showing broad spectrum of antimicrobial activity against food spoiling microorganisms, were selected. The genotyping by 16S rRNA sequencing, GS-PCR, RAPD PCR methods showed that they were presented by Lactobacillus rhamnosus109, L.plantarum 65, L.plantarum 66 and Enterococcus faecium 64 species. LAB are deposited in "Microbial Depository Center" (MDC) SPC "Armbiotechnology". Methods: LAB strains were isolated from different dairy products from rural households from the highland regions of Armenia. Serially diluted samples were spread on MRS (Merck, Germany) and hydrolyzed milk agar (1,2 % w/v). Single colonies from each LAB were individually inoculated in liquid MRS medium and incubated at 37oC for 24 hours. Culture broth with biomass was centrifuged at 10,000 g during 20 min for obtaining of cell free culture broth (CFC). The antimicrobial substances from CFC broth were purified by the combination of adsorption-desorption and ion-exchange chromatography methods. Separation of bacteriocins was performed using a HPLC method on "Avex ODS" C18 column. Mass analysis of peptides recorded on the device API 4000 in the electron ionization mode. The spot-on-lawn method on the test culture plated in the solid medium was applied. The antimicrobial activity is expressed in arbitrary units (AU/ml). Results. Purification of CFC broth of LAB allowed to obtain partially purified antimicrobial preparations which contains bacteriocins with broad spectrum of antimicrobial activity. Investigation of their main biochemical properties shown, that inhibitory activity of preparations is partially reduced after treatment with proteinase K, trypsin, pepsin, suggesting a proteinaceous nature of bacteriocin-like substances containing in CFC broth. Preparations preserved their activity after heat treatment (50-121 oC, 20 min) and were stable in the pH range 3–8. The results of SDS PAAG electrophoresis show that L.plantarum 66 and Ent.faecium 64 strains have one bacteriocin (BCN) with maximal antimicrobial activity with approximate molecular weight 2.0-3.0 kDa. From L.rhamnosus 109 two BCNs were obtained. Mass spectral analysis indicates that these bacteriocins have peptide bonds and molecular weight of BCN 1 and BCN 2 are approximately 1.5 kDa and 700 Da. Discussion: Thus, our experimental data shown, that isolated endemic strains of LAB are able to produce bacteriocins with high and different inhibitory activity against broad spectrum of microorganisms of different taxonomic group, such as Salmonella sp., Esherichia coli, Bacillus sp., L.monocytogenes, Proteus mirabilis, Staph. aureus, Ps. aeruginosa. Obtained results proved the perspectives for use of endemic strains in the preservation of foodstuffs. Acknowledgments: This work was realized with financial support of the Project Global Initiatives for Preliferation Prevention (GIPP) T2- 298, ISTC A-1866.

Keywords: antimicrobial activity, bacteriocins, endemic strains, food safety

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Authors: Ya-Li Tsai

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Nitrogen is a vital element crucial for crop growth, significantly influencing crop yield. In rice cultivation, farmers often apply substantial nitrogen fertilizer to maximize yields. However, excessive nitrogen application increases the risk of lodging and pest infestation, leading to yield losses. Additionally, conventional flooded irrigation methods consume significant water resources, necessitating precise agricultural and intelligent water management systems. In this study, it leveraged physiological data and field images captured by unmanned aerial vehicles, considering fertilizer treatment and irrigation as key factors. Statistical models incorporating rice physiological data, yield, and vegetation indices from image data were developed. Missing physiological data were addressed using multiple imputation and regression methods, and regression models were established using principal component analysis and stepwise regression. Target nitrogen accumulation at key growth stages was identified to optimize fertilizer application, with the difference between actual and target nitrogen accumulation guiding recommendations for ear dressing dosage. Field experiments conducted in 2022 validated the recommended ear dressing dosage, demonstrating no significant difference in final yield compared to traditional fertilizer levels under alternate wetting and drying irrigation. These findings highlight the efficacy of applying recommended dosages based on fertilizer decision models, offering the potential for reduced fertilizer use while maintaining yield in rice cultivation.

Keywords: intelligent fertilizer management, nitrogen top and ear dressing fertilizer, rice, yield optimization

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Authors: Alex Kiselyov, Suehyun Cho, Darrell Harrington; Florent Cros, Olin Palmer, John Caputo, Michael Kardosh, Eran Oren, William Loudon, Michael Shpigelmacher

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Despite the most aggressive surgical and adjuvant therapeutic strategies, treatment of both pediatric and adult brainstem tumors remains problematic. Novel strategies, including targeted biologics, immunotherapy, and specialized delivery systems such as convection-enhanced delivery (CED), have been proposed. While some of these novel treatments are entering phase I trials, the field is still in need of treatment(s) that exhibits dramatically enhanced potency with optimal therapeutic ratio. Bionaut Labs has developed a modular microrobotic platform for performing localized delivery of diverse therapeutics in vivo. Our biocompatible particles (Bionauts™) are externally propelled and visualized in real-time. Bionauts™ are specifically designed to enhance the effect of radiation therapy via anatomically precise delivery of a radiosensitizing agent, as exemplified by temozolomide (TMZ) and Avastin™ to the brainstem gliomas of diverse origin. The treatment protocol is designed to furnish a better therapeutic outcome due to the localized (vs systemic) delivery of the drug to the neoplastic lesion(s) for use as a synergistic combination of radiation and radiosensitizing agent. In addition, the procedure is minimally invasive and is expected to be appropriate for both adult and pediatric patients. Current progress, including platform optimization, selection of the lead radiosensitizer as well as in vivo safety studies of the Bionauts™ in large animals, specifically the spine and the brain of porcine and ovine models, will be discussed.

Keywords: Bionaut, brainstem, glioma, local delivery, micro-robot, radiosensitizer

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Authors: Ajai Singh

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Simulation of hydrological processes at the watershed outlet through modelling approach is essential for proper planning and implementation of appropriate soil conservation measures in Damodar Barakar catchment, Hazaribagh, India where soil erosion is a dominant problem. This study quantifies the parametric uncertainty involved in simulation of stream flow using Soil and Water Assessment Tool (SWAT), a watershed scale model and Radial Basis Neural Network (RBNN), an artificial neural network model. Both the models were calibrated and validated based on measured stream flow and quantification of the uncertainty in SWAT model output was assessed using ‘‘Sequential Uncertainty Fitting Algorithm’’ (SUFI-2). Though both the model predicted satisfactorily, but RBNN model performed better than SWAT with R2 and NSE values of 0.92 and 0.92 during training, and 0.71 and 0.70 during validation period, respectively. Comparison of the results of the two models also indicates a wider prediction interval for the results of the SWAT model. The values of P-factor related to each model shows that the percentage of observed stream flow values bracketed by the 95PPU in the RBNN model as 91% is higher than the P-factor in SWAT as 87%. In other words the RBNN model estimates the stream flow values more accurately and with less uncertainty. It could be stated that RBNN model based on simple input could be used for estimation of monthly stream flow, missing data, and testing the accuracy and performance of other models.

Keywords: SWAT, RBNN, SUFI 2, bootstrap technique, stream flow, simulation

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Authors: Adrian Ionita, Ana-Maria Ghimes

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The lack of features, design and the lack of promoting an integrated booking application are some of the reasons why most online travel platforms only offer automation of old booking processes, being limited to the integration of a smaller number of services without addressing the user experience. This paper represents a practical study on how to improve travel applications creating user-profiles through data-mining based on neural networks and genetic algorithms. Choices made by users and their ‘friends’ in the ‘social’ network context can be considered input data for a recommendation engine. The purpose of using these algorithms and this design is to improve user experience and to deliver more features to the users. The paper aims to highlight a broader range of improvements that could be applied to travel applications in terms of design and service integration, while the main scientific approach remains the technical implementation of the neural network solution. The motivation of the technologies used is also related to the initiative of some online booking providers that have made the fact that they use some ‘neural network’ related designs public. These companies use similar Big-Data technologies to provide recommendations for hotels, restaurants, and cinemas with a neural network based recommendation engine for building a user ‘DNA profile’. This implementation of the ‘profile’ a collection of neural networks trained from previous user choices, can improve the usability and design of any type of application.

Keywords: artificial intelligence, big data, cloud computing, DNA profile, genetic algorithms, machine learning, neural networks, optimization, recommendation system, user profiling

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Authors: Rhea Kapoor

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Fractal dimension provides a way to characterize non-geometric shapes like those found in nature. The purpose of this research is to estimate Minkowski fractal dimension of human lung images for early detection of lung cancer. Lung cancer is the leading cause of death among all types of cancer and an early histopathological analysis will help reduce deaths primarily due to late diagnosis. A Python application program, PathoPy2.0, was developed for analyzing medical images in pixelated format and estimating Minkowski fractal dimension using a new box-counting algorithm that allows windowing of images for more accurate calculation in the suspected areas of cancerous growth. Benchmark geometric fractals were used to validate the accuracy of the program and changes in fractal dimension of lung images to indicate the presence of issues in the lung. The accuracy of the program for the benchmark examples was between 93-99% of known values of the fractal dimensions. Fractal dimension values were then calculated for lung images, from National Cancer Institute, taken over time to correctly detect the presence of cancerous growth. For example, as the fractal dimension for a given lung increased from 1.19 to 1.27 due to cancerous growth, it represents a significant change in fractal dimension which lies between 1 and 2 for 2-D images. Based on the results obtained on many lung test cases, it was concluded that fractal dimension of human lungs can be used to diagnose lung cancer early. The ideas behind PathoPy2.0 can also be applied to study patterns in the electrical activity of the human brain and DNA matching.

Keywords: fractals, histopathological analysis, image processing, lung cancer, Minkowski dimension

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Authors: Kayode A. Olaniyi, Adeola A. Ogunleye, Tola M. Osifeko

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Electric Vehicles (EV) appear to be gaining increasing patronage as a feasible alternative to Internal Combustion Engine Vehicles (ICEVs) for having low emission and high operation efficiency. The EV energy storage systems are required to handle high energy and power density capacity constrained by limited space, operating temperature, weight and cost. The choice of strategies for energy storage evaluation, monitoring and control remains a challenging task. This paper presents review of various energy storage technologies and recent researches in battery evaluation techniques used in EV applications. It also underscores strategies for the hybrid energy storage management and control schemes for the improvement of EV stability and reliability. The study reveals that despite the advances recorded in battery technologies there is still no cell which possess both the optimum power and energy densities among other requirements, for EV application. However combination of two or more energy storages as hybrid and allowing the advantageous attributes from each device to be utilized is a promising solution. The review also reveals that State-of-Charge (SoC) is the most crucial method for battery estimation. The conventional method of SoC measurement is however questioned in the literature and adaptive algorithms that include all model of disturbances are being proposed. The review further suggests that heuristic-based approach is commonly adopted in the development of strategies for hybrid energy storage system management. The alternative approach which is optimization-based is found to be more accurate but is memory and computational intensive and as such not recommended in most real-time applications.

Keywords: battery state estimation, hybrid electric vehicle, hybrid energy storage, state of charge, state of health

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Authors: Simon Arunga, Teddy Kwaga, Rita Kageni, Michael Gichangi, Nyawira Mwangi, Fred Kagwa, Rogers Mwavu, Amos Baryashaba, Luis F. Nakayama, Katharine Morley, Michael Morley, Leo A. Celi, Jessica Haberer, Celestino Obua

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Purpose: The main aim for creating the Multimodal Database of Retinal Images for Africa (MoDRIA) was to provide a publicly available repository of retinal images for responsible researchers to conduct algorithm development in a bid to curb the challenges of ophthalmic artificial intelligence (AI) in Africa. Methods: Data and retina images were ethically sourced from sites in Uganda and Kenya. Data on medical history, visual acuity, ocular examination, blood pressure, and blood sugar were collected. Retina images were captured using fundus cameras (Foru3-nethra and Canon CR-Mark-1). Images were stored on a secure online database. Results: The database consists of 7,859 retinal images in portable network graphics format from 1,988 participants. Images from patients with human immunodeficiency virus were 18.9%, 18.2% of images were from hypertensive patients, 12.8% from diabetic patients, and the rest from normal’ participants. Conclusion: Publicly available data repositories are a valuable asset in the development of AI technology. Therefore, is a need for the expansion of MoDRIA so as to provide larger datasets that are more representative of Sub-Saharan data.

Keywords: retina images, MoDRIA, image repository, African database

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Authors: Syed Tahir Shah, Fazal Muhammad, Syed Kashif Shah, Maleeha Gul

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In recent days, natural gas has become a relatively clean and quality source of energy, which is recovered from deep wells by expensive drilling activities. The recovered substance is purified by processing in multiple stages to remove the unwanted/containments like dust, dirt, crude oil and other particles. Mostly, gas utilities are concerned with essential objectives of quantity/quality of natural gas delivery, financial outcome and safe natural gas volumetric inventory in the transmission gas pipeline. Gas quantity and quality are primarily related to standards / advanced metering procedures in processing units/transmission systems, and the financial outcome is defined by purchasing and selling gas also the operational cost of the transmission pipeline. SNGPL (Sui Northern Gas Pipelines Limited) Pakistan has a wide range of diameters of natural gas transmission pipelines network of over 9125 km. This research results in answer a few of the issues in accuracy/metering procedures via multiple advanced gadgets for gas flow attributes after being utilized in the transmission system and research. The effects of good pressure management in transmission gas pipeline network in contemplation to boost the gas volume deposited in the existing network and finally curbing gas losses UFG (Unaccounted for gas) for financial benefits. Furthermore, depending on the results and their observation, it is directed to enhance the maximum allowable working/operating pressure (MAOP) of the system to 1235 PSIG from the current round about 900 PSIG, such that the capacity of the network could be entirely utilized. In gross, the results depict that the current model is very efficient and provides excellent results in the minimum possible time.

Keywords: natural gas, pipeline network, UFG, transmission pack, AGA

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Authors: Byung Wook Yang, Sae Kyul Kim, Seung Il Ahn, Jae Hee Choi, Heejung Jung, Yejin Choi, Byung Yong Kim, Young Tae Hahm

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Opuntia ficus-indica is a member of the Cactaceae family that is widely grown in all the semiarid countries throughout the world. Opuntia ficus-indica var. Saboten (OFS), commonly known as prickly pear cactus, is commercially cultivated as a dietary foodstuffs and medicinal stuffs in Jeju Island, Korea. Owing to high viscosity of OFS’ pad, its application to the commercial field has been limited. When the low viscosity of OFS’s pad is obtained, it is useful for the manufacture of healthy food in the related field. This study was performed to obtain the optimal digestion conditions of food-grade enzymes (Pectinex, Viscozyme and Celluclast) with the powder of OFS stem. And also, the contents of water-soluble dietary fiber (WSDF) of the dried powder prepared by the extraction of OFS stem were monitored and optimized using the response surface methodology (RSM), which included 20 experimental points with 3 replicates for two independent variables (fermentation temperature and time). A central composite design was used to monitor the effect of fermentation temperature (30-90 °C, X1) and fermentation time (1-10h, X2) on dependent variables, such as viscosity (Y1), water-soluble dietary fiber (Y2) and dietary fiber yield (Y3). Estimated maximum values at predicted optimum conditions were in agreement with experimental values. Optimum temperature and duration were 50°C and 12 hours, respectively. Viscosity value reached 3.4 poise. Yield of water-soluble dietary fiber is determined in progress.

Keywords: Opuntia ficus-indica var. saboten, enzymatic fermentation, response surface methodology, water-soluble dietary fiber, viscosity

Procedia PDF Downloads 325

Authors: Xiaoying Guo, Xiangyun Wang, Chunhua Jia

Abstract:

Web page has been one of the most important way of knowing the world. Humans catch a lot of information from it everyday. Thus, understanding where human looks when they surfing the web pages is rather important. In normal scenes, the down-top features and top-down tasks significantly affect humans’ eye movement. In this paper, we investigated if the conventional visual salience algorithm can properly predict humans’ visual attractive region when they viewing the web pages. First, we obtained the eye movement data when the participants viewing the web pages using an eye tracker. By the analysis of eye movement data, we studied the influence of visual saliency and thinking way on eye-movement pattern. The analysis result showed that thinking way affect human’ eye-movement pattern much more than visual saliency. Second, we compared the results of web page visual salience region extracted by Itti model and Spectral Residual (SR) model. The results showed that Spectral Residual (SR) model performs superior than Itti model by comparison with the heat map from eye movements. Considering the influence of mind habit on humans’ visual region of interest, we introduced one of the most important cue in mind habit-fixation position to improved the SR model. The result showed that the improved SR model can better predict the human visual region of interest in web pages.

Keywords: web page salience region, eye-tracker, spectral residual, visual salience

Procedia PDF Downloads 257

Authors: Hankyu Lee, Seoung Bum Kim

Abstract:

The issue with imbalance and overlapping in the class distribution becomes important in various applications of data mining. The imbalanced dataset is a special case in classification problems in which the number of observations of one class (i.e., major class) heavily exceeds the number of observations of the other class (i.e., minor class). Overlapped dataset is the case where many observations are shared together between the two classes. Imbalanced and overlapped data can be frequently found in many real examples including fraud and abuse patients in healthcare, quality prediction in manufacturing, text classification, oil spill detection, remote sensing, and so on. The class imbalance and overlap problem is the challenging issue because this situation degrades the performance of most of the standard classification algorithms. In this study, we propose a classification procedure that can effectively handle imbalanced and overlapped datasets by splitting data space into three parts: nonoverlapping, light overlapping, and severe overlapping and applying the classification algorithm in each part. These three parts were determined based on the Hausdorff distance and the margin of the modified support vector machine. An experiments study was conducted to examine the properties of the proposed method and compared it with other classification algorithms. The results showed that the proposed method outperformed the competitors under various imbalanced and overlapped situations. Moreover, the applicability of the proposed method was demonstrated through the experiment with real data.

Keywords: classification, imbalanced data with class overlap, split data space, support vector machine

Procedia PDF Downloads 282

Authors: Jalil Qoulizadeh, Hasan Sadeghi

Abstract:

Brain waves are electrical wave patterns that are produced in the human brain. Knowing these waves and activating them can have a positive effect on brain function and ultimately create an ideal life. The brain has the ability to produce waves from 0.1 to above 65 Hz. (The Beta One device produces exactly these waves) This is because it is said that the waves produced by the Beta One device exactly match the waves produced by the brain. The function and method of this device is based on the magnetic stimulation of the brain. The technology used in the design and producƟon of this device works in a way to strengthen and improve the frequencies of brain waves with a pre-defined algorithm according to the type of requested function, so that the person can access the expected functions in life activities. to perform better. The effect of this field on neurons and their stimulation: In order to evaluate the effect of this field created by the device, on the neurons, the main tests are by conducting electroencephalography before and after stimulation and comparing these two baselines by qEEG or quantitative electroencephalography method using paired t-test in 39 subjects. It confirms the significant effect of this field on the change of electrical activity recorded after 30 minutes of stimulation in all subjects. The Beta One device is able to induce the appropriate pattern of the expected functions in a soft and effective way to the brain in a healthy and effective way (exactly in accordance with the harmony of brain waves), the process of brain activities first to a normal state and then to a powerful one. Production of inexpensive neuroscience equipment (compared to existing rTMS equipment) Magnetic brain stimulation for clinics - homes - factories and companies - professional sports clubs.

Keywords: stimulation, brain, waves, betaOne

Procedia PDF Downloads 60

Authors: Tim Nikolsky

Abstract:

This paper seeks to explore the different ways in which music producers choose to embrace various levels of technology based on musical values, objectives, affordability, access and workflow benefits. Current digital audio production workflow is questioned. Engineers and music producers of today are increasingly divorced from the tangibility of music production. Making music no longer requires you to reach over and turn a knob. Ideas of authenticity in music production are being redefined. Calculations from the mathematical algorithm with the pretty pictures are increasingly being chosen over hardware containing transformers and tubes. Are mouse clicks and movements equivalent or inferior to the master brush strokes we are seeking to conjure? We are making audio production decisions visually by constantly looking at a screen rather than listening. Have we compromised our music objectives and values by removing the ‘hands-on’ nature of music making? DAW interfaces are making our musical decisions for us not necessarily in our best interests. Technological innovation has presented opportunities as well as challenges for education. What do music production students actually need to learn in a formalised education environment, and to what extent do they need to know it? In this brave new world of omnipresent music creation tools, do we still need tangibility in music production? Interviews with prominent Australian music producers that work in a variety of fields will be featured in this paper, and will provide insight in answering these questions and move towards developing an understanding how tangibility can be rediscovered in the next generation of music production.

Keywords: analogue, digital, digital audio workstation, music production, plugins, tangibility, technology, workflow

Procedia PDF Downloads 254

Authors: M. Asl. Dehghan, M. H. Daemi, S. Radmard, S. H. Nabavi

Abstract:

Thin disk lasers are engineered for efficient thermal cooling and exhibit superior performance for this task. However the disk thickness and large pumped area make the use of this gain format in a resonator difficult when constructing a single-mode laser. Choosing an unstable resonator design is beneficial for this purpose. On the other hand, the low gain medium restricts the application of unstable resonators to low magnifications and therefore to a poor beam quality. A promising idea to enable the application of unstable resonators to wide aperture, low gain lasers is to couple a fraction of the out coupled radiation back into the resonator. The output coupling gets dependent on the ratio of the back reflection and can be adjusted independently from the magnification. The excitation of the converging wave can be done by the use of an external reflector. The resonator performance is numerically predicted. First of all the threshold condition of linear, V and 2V shape resonator is investigated. Results show that the maximum magnification is 1.066 that is very low for high quality purposes. Inserting an additional reflector covers the low gain. The reflectivity and the related magnification of a 350 micron Yb:YAG disk are calculated. The theoretical model was based on the coupled Kirchhoff integrals and solved numerically by the Fox and Li algorithm. Results show that with back reflection mechanism in combination with increasing the number of beam incidents on disk, high gain and high magnification can occur.

Keywords: unstable resonators, thin disk lasers, gain, external reflector

Procedia PDF Downloads 391

Authors: Torsten Schwan, Rene Unger

Abstract:

Simulation of thermal and electrical building performance more and more becomes part of an integrative planning process. Increasing requirements on energy efficiency, the integration of volatile renewable energy, smart control and storage management often cause tremendous challenges for building engineers and architects. This mainly affects commercial or non-residential buildings. Their energy consumption characteristics significantly distinguish from residential ones. This work focuses on the many-objective optimization problem indoor air quality and comfort, especially in non-residential buildings. Based on a brief description of intermediate dependencies between different requirements on indoor air treatment it extends existing Modelica-based building physics models with additional system states to adequately represent indoor air conditions. Interfaces to corresponding HVAC (heating, ventilation, and air conditioning) system and control models enable closed-loop analyzes of occupants' requirements and energy efficiency as well as profitableness aspects. A complex application scenario of a nearly-zero-energy school building shows advantages of presented evaluation process for engineers and architects. This way, clear identification of air quality requirements in individual rooms together with realistic model-based description of occupants' behavior helps to optimize HVAC system already in early design stages. Building planning processes can be highly improved and accelerated by increasing integration of advanced simulation methods. Those methods mainly provide suitable answers on engineers' and architects' questions regarding more exuberant and complex variety of suitable energy supply solutions.

Keywords: indoor air quality, dynamic simulation, energy efficient control, non-residential buildings

Procedia PDF Downloads 206

Authors: Julius Onyancha, Valentina Plekhanova

Abstract:

One of the significant issues facing web users is the amount of noise in web data which hinders the process of finding useful information in relation to their dynamic interests. Current research works consider noise as any data that does not form part of the main web page and propose noise web data reduction tools which mainly focus on eliminating noise in relation to the content and layout of web data. This paper argues that not all data that form part of the main web page is of a user interest and not all noise data is actually noise to a given user. Therefore, learning of noise web data allocated to the user requests ensures not only reduction of noisiness level in a web user profile, but also a decrease in the loss of useful information hence improves the quality of a web user profile. Noise Web Data Learning (NWDL) tool/algorithm capable of learning noise web data in web user profile is proposed. The proposed work considers elimination of noise data in relation to dynamic user interest. In order to validate the performance of the proposed work, an experimental design setup is presented. The results obtained are compared with the current algorithms applied in noise web data reduction process. The experimental results show that the proposed work considers the dynamic change of user interest prior to elimination of noise data. The proposed work contributes towards improving the quality of a web user profile by reducing the amount of useful information eliminated as noise.

Keywords: web log data, web user profile, user interest, noise web data learning, machine learning

Procedia PDF Downloads 242

Authors: Manop Aorpimai, Ponthep Navakitkanok

Abstract:

In this paper, the development of the ephemeris generation module used for the Thaichote satellite operations is presented. It is a vital part of the flight dynamics system, which comprises, the orbit determination, orbit propagation, event prediction and station-keeping maneuver modules. In the generation of the spacecraft ephemeris data, the estimated orbital state vector from the orbit determination module is used as an initial condition. The equations of motion are then integrated forward in time to predict the satellite states. The higher geopotential harmonics, as well as other disturbing forces, are taken into account to resemble the environment in low-earth orbit. Using a highly accurate numerical integrator based on the Burlish-Stoer algorithm the ephemeris data can be generated for long-term predictions, by using a relatively small computation burden and short calculation time. Some events occurring during the prediction course that are related to the mission operations, such as the satellite’s rise/set viewed from the ground station, Earth and Moon eclipses, the drift in ground track as well as the drift in the local solar time of the orbital plane are all detected and reported. When combined with other modules to form a flight dynamics system, this application is aimed to be applied for the Thaichote satellite and successive Thailand’s Earth-observation missions.

Keywords: flight dynamics system, orbit propagation, satellite ephemeris, Thailand’s Earth Observation Satellite

Procedia PDF Downloads 353

Authors: Umme Hani, H. G. Shivakumar, M. D. Younus Pasha

Abstract:

The purpose of this work was to design and optimize a novel vaginal drug delivery system for more effective treatment against vaginal candidiasis. To achieve a better therapeutic efficacy and patient compliance in the treatment for vaginal candidiasis, herbal antifungal agent Curcumin which is 2.5 fold more potent than fluconazole at inhibiting the adhesion of candida albicans has been formulated in a bio-adhesive vaginal film. Curcumin was formulated in bio-adhesive film formulations that could be retained in the vagina for prolonged intervals. The polymeric films were prepared by solvent evaporation and optimized for various physicodynamic and aesthetic properties. Curcumin HPβCD (Hydroxypropyl β Cyclodextrin) was first developed to increase the solubility of curcumin. The formation of the Curcumin HPβCD complex was characterized by scanning electron microscopy (SEM), differential scanning calorimetry (DSC), and FT-IR and evaluated for its solubility. Curcumin HPβCD complex was formulated in a bio-adhesive film using hydroxypropyl methyl cellulose (HPMC) and Carbopol 934P and characterized. DSC and FT-IR data of Curcumin HPβCD indicate there was complex formation between the drug and HPβCD. The little moisture content (8.02±0.34% w/w) was present in the film, which helps them to remain stable and kept them from being completely dry and brittle. The mechanical properties, tensile strength, and percentage elongation at break reveal that the formulations were found to be soft and tough. The films showed good peelability, relatively good swelling index, and moderate tensile strength and retained vaginal mucosa up to 8 h. The developed Curcumin vaginal film could be a promising safe herbal medication and can ensure longer residence at the vagina and provide an efficient therapy for vaginal candidiasis.

Keywords: curcumin, curcumin-HPβCD complex, bio-adhesive vaginal film, vaginal candidiasis, 23 factorial design

Procedia PDF Downloads 359