Search results for: food composition data

Authors: K. Ghosh, S. N. Tripathi, Manish Joshi, Y. S. Mayya, Arshad Khan, B. K. Sapra

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We have developed a CFD coupled aerosol microphysics model in the context of aerosol generation from a glowing wire. The governing equations can be solved implicitly for mass, momentum, energy transfer along with aerosol dynamics. The computationally efficient framework can simulate temporal behavior of total number concentration and number size distribution. This formulation uniquely couples standard K-Epsilon scheme with boundary layer model with detailed aerosol dynamics through residence time. This model uses measured temperatures (wire surface and axial/radial surroundings) and wire compositional data apart from other usual inputs for simulations. The model predictions show that bulk fluid motion and local heat distribution can significantly affect the aerosol behavior when the buoyancy effect in momentum transfer is considered. Buoyancy generated turbulence was found to be affecting parameters related to aerosol dynamics and transport as well. The model was validated by comparing simulated predictions with results obtained from six controlled experiments performed with a laboratory-made hot wire nanoparticle generator. Condensation particle counter (CPC) and scanning mobility particle sizer (SMPS) were used for measurement of total number concentration and number size distribution at the outlet of reactor cell during these experiments. Our model-predicted results were found to be in reasonable agreement with observed values. The developed model is fast (fully implicit) and numerically stable. It can be used specifically for applications in the context of the behavior of aerosol particles generated from glowing wire technique and in general for other similar large scale domains. Incorporation of CFD in aerosol microphysics framework provides a realistic platform to study natural convection driven systems/ applications. Aerosol dynamics sub-modules (nucleation, coagulation, wall deposition) have been coupled with Navier Stokes equations modified to include buoyancy coupled K-Epsilon turbulence model. Coupled flow-aerosol dynamics equation was solved numerically and in the implicit scheme. Wire composition and temperature (wire surface and cell domain) were obtained/measured, to be used as input for the model simulations. Model simulations showed a significant effect of fluid properties on the dynamics of aerosol particles. The role of buoyancy was highlighted by observation and interpretation of nucleation zones in the planes above the wire axis. The model was validated against measured temporal evolution, total number concentration and size distribution at the outlet of hot wire generator cell. Experimentally averaged and simulated total number concentrations were found to match closely, barring values at initial times. Steady-state number size distribution matched very well for sub 10 nm particle diameters while reasonable differences were noticed for higher size ranges. Although tuned specifically for the present context (i.e., aerosol generation from hotwire generator), the model can also be used for diverse applications, e.g., emission of particles from hot zones (chimneys, exhaust), fires and atmospheric cloud dynamics.

Keywords: nanoparticles, k-epsilon model, buoyancy, CFD, hot wire generator, aerosol dynamics

Procedia PDF Downloads 131

Authors: Maliheh Raji, Hossein Abolghasemi, Jaber Safdari, Ali Kargari

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Samarium as a rare-earth element is playing a growing important role in high technology. Traditional methods for extraction of rare earth metals such as ion exchange and solvent extraction have disadvantages of high investment and high energy consumption. Emulsion liquid membrane (ELM) as an improved solvent extraction technique is an effective transport method for separation of various compounds from aqueous solutions. In this work, the extraction of samarium from aqueous solutions by ELM was investigated using response surface methodology (RSM). The organic membrane phase of the ELM was a nanofluid consisted of multiwalled carbon nanotubes (MWCNT), Span80 as surfactant, Cyanex 272 as mobile carrier, and kerosene as base fluid. 1 M nitric acid solution was used as internal aqueous phase. The effects of the important process parameters on samarium extraction were investigated, and the values of these parameters were optimized using the Central Composition Design (CCD) of RSM. These parameters were the concentration of MWCNT in nanofluid, the carrier concentration, and the volume ratio of organic membrane phase to internal phase (Roi). The three-dimensional (3D) response surfaces of samarium extraction efficiency were obtained to visualize the individual and interactive effects of the process variables. A regression model for % extraction was developed, and its adequacy was evaluated. The result shows that % extraction improves by using MWCNT nanofluid in organic membrane phase and extraction efficiency of 98.92% can be achieved under the optimum conditions. In addition, demulsification was successfully performed and the recycled membrane phase was proved to be effective in the optimum condition.

Keywords: Cyanex 272, emulsion liquid membrane, MWCNT nanofluid, response surface methology, Samarium

Procedia PDF Downloads 409

Authors: Andreas Walmsley, Shobana Partington, Rebecca Armstrong, Harold Goodwin

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In the UK, more than 1 in 5 workers earn less than a living wage. The hospitality sector is particularly affected where it has been claimed two thirds of workers earn less than the living wage. The UK Government is set to introduce (April 2016) a national living wage (NLW) which is therefore likely to have a significant impact on the hospitality sector. To date limited data exists that focus on how hotels are tackling the issue, what stakeholder perceptions are towards the change in legislation, and how the NLW may affect working patterns in the sector. This study draws on interviews with a range of key stakeholders such as hotel HR and general managers as well as industry representatives to explore these issues within the broader context of responsible tourism. Data collection is still ongoing and is scheduled to be completed by the end of June 2016.

Keywords: hospitality, living wage, responsible tourism, tourism employment

Procedia PDF Downloads 369

Authors: Aafaf El Jazouli, Ahmed Barakat, Rida Khellouk

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Mapping of soil degradation is derived from field observations, laboratory measurements, and remote sensing data, integrated quantitative methods to map the spatial characteristics of soil properties at different spatial and temporal scales to provide up-to-date information on the field. Since soil salinity, texture and organic matter play a vital role in assessing topsoil characteristics and soil quality, remote sensing can be considered an effective method for studying these properties. The main objective of this research is to asses soil degradation by combining remote sensing data and laboratory analysis. In order to achieve this goal, the required study of soil samples was taken at 50 locations in the upper basin of Oum Er Rbia in the Middle Atlas in Morocco. These samples were dried, sieved to 2 mm and analyzed in the laboratory. Landsat 8 OLI imagery was analyzed using physical or empirical methods to derive soil properties. In addition, remote sensing can serve as a supporting data source. Deterministic potential (Spline and Inverse Distance weighting) and probabilistic interpolation methods (ordinary kriging and universal kriging) were used to produce maps of each grain size class and soil properties using GIS software. As a result, a correlation was found between soil texture and soil organic matter content. This approach developed in ongoing research will improve the prospects for the use of remote sensing data for mapping soil degradation in arid and semi-arid environments.

Keywords: Soil degradation, GIS, interpolation methods (spline, IDW, kriging), Landsat 8 OLI, Oum Er Rbia high basin

Procedia PDF Downloads 149

Authors: Ammar Alali, Mahmoud Abughaban

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

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

Procedia PDF Downloads 206

Authors: Muhammad Nauman Ahmad, Patrick Büker, Sofia Khalid, Leon Van Den Berg, Hamid Ullah Shah, Abdul Wahid, Lisa Emberson, Sally A. Power, Mike Ashmore

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Although ozone is well-documented to affect crop yields in the densely populated Indo-Gangetic Plain, there is little knowledge of its effects around cities in more remote areas of South Asia. We surveyed crops around the city of Peshawar, Pakistan for visible injury, linking this to passive measurements of ozone concentrations. Foliar injury was found in the field on potato, onion and cotton when the mean monthly ozone concentration reached 35-55ppb. The symptoms on onion were reproduced in ozone fumigation experiments, which also showed that daytime ozone concentrations of 60ppb and above significantly reduce the growth of Pakistani varieties of both spinach (Beta vulgaris) and onion. Aphid infestation on spinach was also reduced at these elevated ozone concentrations. The ozone concentrations in Peshawar are comparable to those through many parts of northern south Asia, where ozone may therefore be a significant threat to sensitive vegetable crops in peri-urban regions.

Keywords: ozone, air pollution, vegetable crops, peshawar, south asia

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Authors: Frederic Henry-Couannier

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Dark Gravity is a natural extension of general relativity in presence of a flat non dynamical background. Matter and radiation fields from its dark sector, as soon as their gravity dominates over our side fields gravity, produce a constant acceleration law of the scale factor. After a brief reminder of the Dark Gravity theory foundations, the confrontation with the main cosmological probes is carried out. We show that, amazingly, the sudden transition between the usual matter dominated decelerated expansion law a(t) ∝ t²/³ and this accelerated expansion law a(t) ∝ t² predicted by the theory should be able to fit the main cosmological probes (SN, BAO, CMB and age of the oldest stars data) but also direct H₀ measurements with two free parameters only: H₀ and the transition redshift.

Keywords: anti-gravity, negative energies, time reversal, field discontinuities, dark energy theory

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Authors: D. Serero, L. Couton, J. D. Parisse, R. Leroy

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In urban planning, an increasing number of cities require wind analysis to verify comfort of public spaces and around buildings. These studies are made using computer fluid dynamic simulation (CFD). However, this technique is often based on wind information taken from meteorological stations located at several kilometers of the spot of analysis. The approximated input data on project surroundings produces unprecise results for this type of analysis. They can only be used to get general behavior of wind in a zone but not to evaluate precise wind speed. This paper presents another approach to this problem, based on collecting wind data and generating an urban wind cartography using connected ultrasound anemometers. They are wireless devices that send immediate data on wind to a remote server. Assembled in array, these devices generate geo-localized data on wind such as speed, temperature, pressure and allow us to compare wind behavior on a specific site or building. These Netatmo-type anemometers communicate by wifi with central equipment, which shares data acquired by a wide variety of devices such as wind speed, indoor and outdoor temperature, rainfall, and sunshine. Beside its precision, this method extracts geo-localized data on any type of site that can be feedback looped in the architectural design of a building or a public place. Furthermore, this method allows a precise calibration of a virtual wind tunnel using numerical aeraulic simulations (like STAR CCM + software) and then to develop the complete volumetric model of wind behavior over a roof area or an entire city block. The paper showcases connected ultrasonic anemometers, which were implanted for an 18 months survey on four study sites in the Grand Paris region. This case study focuses on Paris as an urban environment with multiple historical layers whose diversity of typology and buildings allows considering different ways of capturing wind energy. The objective of this approach is to categorize the different types of wind in urban areas. This, particularly the identification of the minimum and maximum wind spectrum, helps define the choice and performance of wind energy capturing devices that could be implanted there. The localization on the roof of a building, the type of wind, the altimetry of the device in relation to the levels of the roofs, the potential nuisances generated. The method allows identifying the characteristics of wind turbines in order to maximize their performance in an urban site with turbulent wind.

Keywords: computer fluid dynamic simulation in urban environment, wind energy harvesting devices, net-zero energy building, urban wind behavior simulation, advanced building skin design methodology

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Authors: Maximilian Winkens, Peter Nyhuis

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Components with sensory properties such as gentelligent components developed at the Collaborative Research Center 653 offer a new angle on the full utilization of the remaining service life in case of a preventive maintenance. The developed methodology of component status driven maintenance analyses the stress data obtained during the component's useful life and on the basis of this knowledge assesses the type of maintenance called for in this case. The procedure is derived from the case-based reasoning method and will be elucidated in detail. The method's functionality is demonstrated with real-life data obtained during test runs of a racing car prototype.

Keywords: gentelligent component, preventive maintenance, case-based reasoning, sensory

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Authors: Ferdi Afian, Inne Yuliawati

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Background: The change in causes of death from infectious diseases to non-communicable diseases also occurs in the aviation community in Indonesia. Non-communicable diseases are influenced by several internal risk factors, such as age, lifestyle changes and the presence of other diseases. These risk factors will increase the incidence of heart diseases resulting in the incapacity of Indonesian aviators which will disrupt flight safety. Method: The study was conducted by collecting secondary data. The retrieval of primary data was obtained from medical records at the Indonesian Aviation Health Center in 2016-2017. The subjects in this study were all cases of incapacity in Indonesian aviators medical conditions. Results: In this study, there were 15 cases of aviators in Indonesia who experienced incapacity of medical conditions related to heart and lung diseases in 2016-2017. Based on the secondary data contained in the flight medical records at the Aviation Health Center Aviation, it was found that several factors related to aviators incapacity causing its inability to carried out flight duties. Conclusion: Incapacity of Indonesian aviators medical conditions are most affected by the high value of Body Mass Index (86%) and less affected by high of Uric Acid in the blood (26%) and Hyperglycemia (26%).

Keywords: incapacity, aviators, flight, Indonesia

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Authors: Mohammad H. Fattahi

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Chaotic analysis has been performed on the river flow time series before and after applying the wavelet based de-noising techniques in order to investigate the noise content effects on chaotic nature of flow series. In this study, 38 years of monthly runoff data of three gauging stations were used. Gauging stations were located in Ghar-e-Aghaj river basin, Fars province, Iran. The noise level of time series was estimated with the aid of Gaussian kernel algorithm. This step was found to be crucial in preventing removal of the vital data such as memory, correlation and trend from the time series in addition to the noise during de-noising process.

Keywords: chaotic behavior, wavelet, noise reduction, river flow

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Authors: Teguh Endah Saraswati, Kusumandari Kusumandari, Candra Purnawan, Annisa Dinan Ghaisani, Aufara Mahayum

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The present study aims to investigate the degradation of methylene blue (MB) using TiO₂-carbon (TiO₂-C) photocatalyst combined with dielectric discharge (DBD) plasma. The carbon materials used in the photocatalyst were activated carbon and graphite. The thin layer of TiO₂-C photocatalyst was prepared by ball milling method which was then deposited on the plastic sheet. The characteristic of TiO₂-C thin layer was analyzed using X-ray diffraction (XRD), scanning electron microscopy (SEM) with energy dispersive X-ray (EDX) spectroscopy, and UV-Vis diffuse reflectance spectrophotometer. The XRD diffractogram patterns of TiO₂-G thin layer in various weight compositions of 50:1, 50:3, and 50:5 show the 2θ peaks found around 25° and 27° are the main characteristic of TiO₂ and carbon. SEM analysis shows spherical and regular morphology of the photocatalyst. Analysis using UV-Vis diffuse reflectance shows TiO₂-C has narrower band gap energy. The DBD plasma reactor was generated using two electrodes of Cu tape connected with stainless steel mesh and Fe wire separated by a glass dielectric insulator, supplied by a high voltage 5 kV with an air flow rate of 1 L/min. The optimization of the weight composition of TiO₂-C thin layer was studied based on the highest reduction of the MB concentration achieved, examined by UV-Vis spectrophotometer. The changes in pH values and color of MB indicated the success of MB degradation. Moreover, the degradation efficiency of MB was also studied in various higher concentrations of 50, 100, 200, 300 ppm treated for 0, 2, 4, 6, 8, 10 min. The degradation efficiency of MB treated in combination system of photocatalysis and DBD plasma reached more than 99% in 6 min, in which the greater concentration of methylene blue dye, the lower degradation rate of methylene blue dye would be achieved.

Keywords: activated carbon, DBD plasma, graphite, methylene blue, photocatalysis

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Authors: Tanvi P. Patel, Warish D. Patel

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Association rule mining forms the core of data mining and it is termed as one of the well-known methodologies of data mining. Objectives of mining is to find interesting correlations, frequent patterns, associations or casual structures among sets of items in the transaction databases or other data repositories. Hence, association rule mining is imperative to mine patterns and then generate rules from these obtained patterns. For efficient targeted query processing, finding frequent patterns and itemset mining, there is an efficient way to generate an itemset tree structure named Memory Efficient Itemset Tree. Memory efficient IT is efficient for storing itemsets, but takes more time as compare to traditional IT. The proposed strategy generates maximal frequent itemsets from memory efficient itemset tree by using levelwise pruning. For that firstly pre-pruning of items based on minimum support count is carried out followed by itemset tree reconstruction. By having maximal frequent itemsets, less number of patterns are generated as well as tree size is also reduced as compared to MEIT. Therefore, an enhanced approach of memory efficient IT proposed here, helps to optimize main memory overhead as well as reduce processing time.

Keywords: association rule mining, itemset mining, itemset tree, meit, maximal frequent pattern

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Authors: Ashkan Ebadi, Adam Krzyzak

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Recommender systems, also known as recommender engines, have become an important research area and are now being applied in various fields. In addition, the techniques behind the recommender systems have been improved over the time. In general, such systems help users to find their required products or services (e.g. books, music) through analyzing and aggregating other users’ activities and behavior, mainly in form of reviews, and making the best recommendations. The recommendations can facilitate user’s decision making process. Despite the wide literature on the topic, using multiple data sources of different types as the input has not been widely studied. Recommender systems can benefit from the high availability of digital data to collect the input data of different types which implicitly or explicitly help the system to improve its accuracy. Moreover, most of the existing research in this area is based on single rating measures in which a single rating is used to link users to items. This paper proposes a highly accurate hotel recommender system, implemented in various layers. Using multi-aspect rating system and benefitting from large-scale data of different types, the recommender system suggests hotels that are personalized and tailored for the given user. The system employs natural language processing and topic modelling techniques to assess the sentiment of the users’ reviews and extract implicit features. The entire recommender engine contains multiple sub-systems, namely users clustering, matrix factorization module, and hybrid recommender system. Each sub-system contributes to the final composite set of recommendations through covering a specific aspect of the problem. The accuracy of the proposed recommender system has been tested intensively where the results confirm the high performance of the system.

Keywords: tourism, hotel recommender system, hybrid, implicit features

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Authors: Fatma Zuhrotun Nisa, Aprilina Ratriany, Agus Wijanarka

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Background: Cassava leaves are widely used by the people of Indonesia as a vegetable and treat various diseases, including anticancer believed as food. However, not much research on the anticancer activity of cassava leaves, especially in colon cancer. Objectives: the aim of this study is to investigate anti-cancer activity of cassava leaves (Manihot esculanta C.) against colon cancer (WiDr) cells in vitro. Methods: effect of crude aqueous extract of leaves of cassava and cassava leaves boiled tested in colon cancer cells widr. Determination of Anticancer uses the MTT method with parameters such as the percentage of deaths. Results: raw cassava leaf water extract gave IC50 of 63.1 mg / ml. While the water extract of boiled cassava leaves gave IC50 of 79.4 mg/ml. However, there is no difference anticancer activity of raw cassava leaves or cancer (p> 0.05). Conclusion: Cassava leaves contain a variety of compounds that have previously been reported to have anticancer activity. Linamarin, β-carotene, vitamin C, and fiber were thought to affect the IC50 cassava leaf extract against colon cancer cells WiDr.

Keywords: boiled cassava leaves, cassava leaves raw, anticancer activity, colon cancer, IC50

Procedia PDF Downloads 526

Authors: Ayadi Aya, Ghorbel Oussama, M. Obeid Abdulfattah, Abid Mohamed

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Nowadays, many wireless sensor networks have been distributed in the real world to collect valuable raw sensed data. The challenge is to extract high-level knowledge from this huge amount of data. However, the identification of outliers can lead to the discovery of useful and meaningful knowledge. In the field of wireless sensor networks, an outlier is defined as a measurement that deviates from the normal behavior of sensed data. Many detection techniques of outliers in WSNs have been extensively studied in the past decade and have focused on classic based algorithms. These techniques identify outlier in the real transaction dataset. This survey aims at providing a structured and comprehensive overview of the existing researches on classification based outlier detection techniques as applicable to WSNs. Thus, we have identified key hypotheses, which are used by these approaches to differentiate between normal and outlier behavior. In addition, this paper tries to provide an easier and a succinct understanding of the classification based techniques. Furthermore, we identified the advantages and disadvantages of different classification based techniques and we presented a comparative guide with useful paradigms for promoting outliers detection research in various WSN applications and suggested further opportunities for future research.

Keywords: bayesian networks, classification-based approaches, KPCA, neural networks, one-class SVM, outlier detection, wireless sensor networks

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Authors: Ebrahim Ghasemi Ardi, Ai Bing Yu, Run Yu Yang

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Current study is aimed to develop an available in-house discrete element method (DEM) code and link it with direct breakage event. So, it became possible to determine the particle breakage and then its fragments size distribution, simultaneous with DEM simulation. It directly applies the particle breakage inside the DEM computation algorithm and if any breakage happens the original particle is replaced with daughters. In this way, the calculation will be followed based on a new updated particles list which is very similar to the real grinding environment. To validate developed model, a grinding ball impacting an unconfined particle bed was simulated. Since considering an entire ball mill would be too computationally demanding, this method provided a simplified environment to test the model. Accordingly, a representative volume of the ball mill was simulated inside a box, which could emulate media (ball)–powder bed impacts in a ball mill and during particle bed impact tests. Mono, binary and ternary particle beds were simulated to determine the effects of granular composition on breakage kinetics. The results obtained from the DEM simulations showed a reduction in the specific breakage rate for coarse particles in binary mixtures. The origin of this phenomenon, commonly known as cushioning or decelerated breakage in dry milling processes, was explained by the DEM simulations. Fine particles in a particle bed increase mechanical energy loss, and reduce and distribute interparticle forces thereby inhibiting the breakage of the coarse component. On the other hand, the specific breakage rate of fine particles increased due to contacts associated with coarse particles. Such phenomenon, known as acceleration, was shown to be less significant, but should be considered in future attempts to accurately quantify non-linear breakage kinetics in the modeling of dry milling processes.

Keywords: particle bed, breakage models, breakage kinetic, discrete element method

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Authors: Zhaojun Zheng, Yuanfa Liu, Jiaxin Li, Jinwei Li, Yong-jiang Xu, Chen Cao

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Black bean is an excellent protein source for preparing hydrolysates, which attract much attention due to their biological activity. The objective of this study was to characterize the physicochemical and antioxidant properties of black bean protein, hydrolyzed by ficin, bromelain or alcalase until 300 min of hydrolysis. Results showed that bromelain and alcalase hydrolysates possessed a higher degree of hydrolysis (DH) than that of ficin, thereby presenting different ultraviolet absorption, fluorescence intensity, and circular dichroism. Moreover, all hydrolysates possessed the capacity to scavenge DPPH radical with the lowest IC₅₀ of 21.11 µg/mL, as well as to chelate ferrous ion (Fe²⁺) with the IC₅₀ values ranging from 6.82 to 30.68 µg/mL. Intriguingly, the oxidation of linoleic acid, sunflower oil, and sunflower oil-in-water emulsion was remarkedly retarded by the three selected protein hydrolysates, especially by bromelain-treated protein hydrolysate, which might attribute to their high hydrophobicity and emulsifying properties. These findings can provide strong support for black bean protein hydrolysates to be employed in food products acting as natural antioxidant alternatives.

Keywords: antioxidant activity, black bean protein hydrolysate, emulsion physicochemical properties, sunflower oil

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Authors: Francisca Margarita Ocran

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Underwear, and more particularly bras and panties, are defined as intimate clothing. Strictly speaking, they enhance the place of women in the public or private satchel. Therefore, women's lingerie is a complex garment with a high involvement profile, motivating consumers to buy it not only by its functional utility but also by the multisensory experience it provides them. Customer behavior models are generally based on customer data mining, and each model is designed to answer questions at a specific time. Predicting the customer experience is uncertain and difficult. Thus, knowledge of consumers' tastes in lingerie deserves to be treated as an experiential product, where the dimensions of the experience motivating consumers to buy a lingerie product and to remain faithful to it must be analyzed in detail by the manufacturers and retailers to engage and retain consumers, which is why this research aims to identify the variables that push consumers to choose their lingerie product, based on an in-depth analysis of the types of fabrics used to make lingerie. The data used in this study comes from online purchases. Machine learning approach with the use of Python programming language and Pycaret gives us a precision of 86.34%, 85.98%, and 84.55% for the three algorithms to use concerning the preference of a buyer in front of a range of lingerie. Gradient Boosting, random forest, and K Neighbors were used in this study; they are very promising and rich in the classification of preference in the textile industry.

Keywords: consumer behavior, data mining, lingerie, machine learning, preference

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Authors: Muhammad Habib, Lin Tang, Guoliang Xue, Attaur Rahman, Myong-Ho Kim, Soonil Lee, Xuefan Zhou, Yan Zhang, Dou Zhang

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Designing a high-performance, lead-free ceramic has become a cutting-edge research topic due to growing concerns about the toxic nature of lead-based materials. In this work, a convenient strategy of compositional design and domain engineering is applied to the lead-fee BiFeO₃-BaTiO₃ ceramics, which provides a flexible polarization-free-energy profile for domain switching. Here, simultaneously enhanced dynamic piezoelectric constant (d33* = 772 pm/V) and a good thermal-stability (d33* = 26% over the temperature of 20-180 ᵒC) are achieved with a high Curie temperature (TC) of 432 ᵒC. This high piezoelectric strain performance is collectively attributed to multiple effects such as thermal quenching, suppression of defect charges by donor doping, chemically induced local structure heterogeneity, and electric field-induced phase transition. Furthermore, the addition of BT content decreased octahedral tilting, reduced anisotropy for domain switching and increased tetragonality (cₜ/aₜ), providing a wider polar length for B-site cation displacement, leading to high piezoelectric strain performance. Atomic-resolution transmission electron microscopy and piezoelectric force microscopy combined with X-ray diffraction results strongly support the origin of high piezoelectricity. The high and temperature-stable piezoelectric strain response of this work is superior to those of other lead-free ceramics. The synergistic approach of composition design and the concept present here for the origin of high strain response provides a paradigm for the development of materials for high-temperature piezoelectric actuator applications.

Keywords: Piezoelectric, BiFeO3-BaTiO3, Quenching, Temperature-insensitive

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Authors: Gidado Lawal Likko

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The study examined the relationship of achievement goals, metacognitive awareness and academic success among students of colleges of education in North Western Nigeria. The study was guided by three objectives. The first two were to find out whether students’ achievement goals and metacognitive awareness correlate with their academic success. 358 students comprising 242 males (67.6%) and 116 females (32.4%) were studied. Correlation survey was employed in the conduct of the study. The instruments used to collect data were students’ bio data form, achievement goals inventory (Roedel, Schraw and Plake, 1994), metacognitive awareness inventory (Schraw & Dennison, 1994) and students’ CGPA (NCCE minimum standard, 2013) was used as the index of academic success. Pearson Product Moment and regression analysis were the statistical techniques used to analyze the data. Results of the analysis indicated that students’ achievement goals (r=0.554, p=0.004) and metacognitive awareness (r= 0.67, p=0.001) positively correlated with their academic success. Similarly, significant relationship exists between achievement goals and metacognitive awareness (r=0.77, p=0.000). Part of the recommendations is the need for the management of all colleges of education to have educational interventions aimed at developing students’ metacognitive awareness which will foster purposeful self-regulation of their learning. This could be achieved by periodic assessment of students’ metacognitive awareness which will serve as feedback as they move from one educational level to another.

Keywords: academic success, goal orientation, metacognitive awareness, prospective teachers

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Authors: Saleh Alotaibi, Mohammed Quddus, Craig Morton, Jobair Bin Alam

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This paper explores the role of large-scale investment in transport sectors and the impact of increased railway accessibility on the efficiency of the regional economic productivity in the Kingdom of Saudi Arabia (KSA). There are considerable differences among the KSA regions in terms of their levels of investment and productivity due to their geographical scale and location, which in turn greatly affect their relative efficiency. The study used a non-parametric linear programming technique - Data Envelopment Analysis (DEA) - to measure the regional efficiency change over time and determine the drivers of inefficiency and their scope of improvement. In addition, Window DEA analysis is carried out to compare the efficiency performance change for various time periods. Malmquist index (MI) is also analyzed to identify the sources of productivity change between two subsequent years. The analysis involves spatial and temporal panel data collected from 1999 to 2018 for the 13 regions of the country. Outcomes reveal that transport investment and improved railway accessibility, in general, have significantly contributed to regional economic development. Moreover, the endowment of the new railway stations has spill-over effects. The DEA Window analysis confirmed the dynamic improvement in the average regional efficiency over the study periods. MI showed that the technical efficiency change was the main source of regional productivity improvement. However, there is evidence of investment allocation discrepancy among regions which could limit the achievement of development goals in the long term. These relevant findings will assist the Saudi government in developing better strategic decisions for future transport investments and their allocation at the regional level.

Keywords: data envelopment analysis, transport investment, railway accessibility, efficiency

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Authors: Afif Saleh Abugharsa

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The IEEE 802.15.4 standard is designed for low-rate wireless personal area networks (LR-WPAN) with focus on enabling wireless sensor networks. It aims to give a low data rate, low power consumption, and low cost wireless networking on the device-level communication. The objective of this study is to investigate the performance of IEEE 802.15.4 based networks using simulation tool. In this project the network simulator 2 NS2 was used to several performance measures of wireless sensor networks. Three scenarios were considered, multi hop network with a single coordinator, star topology, and an ad hoc on demand distance vector AODV. Results such as packet delivery ratio, hop delay, and number of collisions are obtained from these scenarios.

Keywords: ZigBee, wireless sensor networks, IEEE 802.15.4, low power, low data rate

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Authors: Nevila Alliu, Saimir Heta, Ilma Robo, Vera Ostreni

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Periodontitis as an oral pathology caused by specific bacterial flora functions as a focal infection for the onset and aggravation of arteriosclerosis. These two distant pathologies, since they affect organs at a distance from each other, communicate with each other with correlation at the level of markers of inflammation in the blood. Fluctuations in the level of fibrinogen in the blood, depending on the active or passive phase of the existing periodontitis, affect the promotion of arteriosclerosis. The study is of the review type to analyze the effect of non-surgical periodontal treatment on fluctuations in the level of fibrinogen in the blood. The reduction of fibrinogen levels in the blood after non-surgical periodontal treatment of periodontitis in the patient's cavity is visible data and supported by literature sources. Also, the influence of a high amount of fibrinogen in the blood on the occurrence of arteriosclerosis is also another important data that again relies on many sources of literature. Conclusions: Thromboembolism and arteriosclerosis, as risk factors expressed in clinical data, have temporary bacteremia in the blood, which can occur significantly and often between phases of non-surgical periodontal treatment of periodontitis, treatments performed with treatment phases and protocols of predetermined treatment. Arterial thromboembolism has a significant factor, such as high levels of fibrinogen in the blood, which are significantly reduced during the period of non-surgical periodontal treatment.

Keywords: fibrinogen, refractory periodontitis, atherosclerosis, non-surgical, periodontal treatment

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Authors: Heeyong Noh, Seongryong Kang, Sungjoo Lee

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As the world becomes increasingly interconnected, firms have tried to explore market opportunities not only in the domestic market but also abroad. In particular, transactions of intangible assets in the global market now take on great importance. Accordingly, technology transfer activities such as patent licensing, copyright transfer, or workforce trainings which are considered significant to leverage an organization’s internal capabilities, are occurring more frequently and briskly across the world than ever before. Though a number of studies have addressed the issues regarding technology transfer, most of them have focused on university-industry technology transfer. Of course, some have investigated international technology transfer phenomenon but used patent citations data as a proxy. In order to understand the phenomena more clearly, it would be necessary to collect and analyze data that can measure technology transfer activities between firms more directly. Therefore, this study aims to examine the patterns of international trade in technology by employing data about international technology in-licensing activities in Korean firms. We also investigate the effect of international technology in-licensing strategy on a firm’s innovation performance. The research findings are expected to help R&D managers understand how firms have absorbed technological knowledge from foreign firms in the form of licensing and further develop effective international collaboration strategies. In addition, significant implications can be offered for political decision-making regarding technology trade within increasing international interconnections.

Keywords: international technology trade, technology trade effect, technology transfer, R&D managers

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Authors: Ismael Zamora, Elisabeth Ortega, Tatiana Radchenko, Guillem Plasencia

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The structure elucidation based on Mass Spectra (MS) data of unknown substances is an unresolved problem that affects many different fields of application. The recent overview of software available for structure elucidation of small molecules has shown the demand for efficient computational tool that will be able to perform structure elucidation of unknown small molecules and peptides. We developed an algorithm for De-Novo fragment analysis based on MS data that proposes a set of scored and ranked structures that are compatible with the MS and MSMS spectra. Several different algorithms were developed depending on the initial set of fragments and the structure building processes. Also, in all cases, several scores for the final molecule ranking were computed. They were validated with small and middle databases (DB) with the eleven test set compounds. Similar results were obtained from any of the databases that contained the fragments of the expected compound. We presented an algorithm. Or De-Novo fragment analysis based on only mass spectrometry (MS) data only that proposed a set of scored/ranked structures that was validated on different types of databases and showed good results as proof of concept. Moreover, the solutions proposed by Mass Spectrometry were submitted to the prediction of NMR spectra in order to elucidate which of the proposed structures was compatible with the NMR spectra collected.

Keywords: De Novo, structure elucidation, mass spectrometry, NMR

Procedia PDF Downloads 273

Authors: Anumol Jose, Sajana Nazar, M. R. Vishnu, M. Anilkumar

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Water yam is an underutilized tropical tuber crop and a rich source of polyphenol compounds and acylated anthocyanins. There is an inverse relationship between the risk of chronic human diseases and the consumption of polyphenolic rich diet. Dioscorea alata is a plant species with several undocumented landraces. In this study, several landraces of water yam with distinct morphological features were collected from all over kerala. Distinct variation in morphological feature among landraces was tuber colour and only those landraces which expressed consistent morphological characters for constitutively two growing seasons were included in the study. Plants were categorized according to the L*a*b* colour attributes of tuber extracts. There were five categories, red, pink, orange, yellow and white. Total phenol, flavanoid and anthocyanin content of the tuber extracts were measured spectroscopically and correlated with antioxidant properties determined by 2,2-diphenyl-1-picryl-hydrazyl-hydrate free radical method and ferric reducing antioxidant power assay. Landraces showed statistically significant difference in all the parameters studied and strong correlation were observed between total phenol and antioxidant activity. Out of the five categories orange coloured tubers showed relatively high phenol and flavanoid content.Colour variations of tuber extracts correlated with anthocyanin quantity and polymeric nature of anthocyanins. This study helps to identify and categorize landraces of D.alata with potential health benefits and commercial applications. Distinct colour characteristics of tuber could be useful in the field of natural colorants. This study also aimed to document and preserve landraces of water yams for further study and research in this area.

Keywords: the antioxidant property, anthocyanins, polyphenols, water yam

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Authors: Subhra Prosun Paul, Shruti Aggarwal

Abstract:

Now a days Routing Protocol in Wireless Sensor Network has become a promising technique in the different fields of the latest computer technology. Routing in Wireless Sensor Network is a demanding task due to the different design issues of all sensor nodes. Network architecture, no of nodes, traffic of routing, the capacity of each sensor node, network consistency, service value are the important factor for the design and analysis of Routing Protocol in Wireless Sensor Network. Additionally, internal energy, the distance between nodes, the load of sensor nodes play a significant role in the efficient routing protocol. In this paper, our intention is to analyze the research trends in different routing protocols of Wireless Sensor Network in terms of different parameters. In order to explain the research trends on Routing Protocol in Wireless Sensor Network, different data related to this research topic are analyzed with the help of Web of Science and Scopus databases. The data analysis is performed from global perspective-taking different parameters like author, source, document, country, organization, keyword, year, and a number of the publication. Different types of experiments are also performed, which help us to evaluate the recent research tendency in the Routing Protocol of Wireless Sensor Network. In order to do this, we have used Web of Science and Scopus databases separately for data analysis. We have observed that there has been a tremendous development of research on this topic in the last few years as it has become a very popular topic day by day.

Keywords: analysis, routing protocol, research trends, wireless sensor network

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Authors: Saima Q. Memon, Mazhar I. Khaskheli

Abstract:

The aim of present study was to investigate the efficiency of mulberry leaves for the removal of both arsenic (III) and arsenic (V) from aqueous medium. Batch equilibrium studies were carried out to optimize various parameters such as pH of metal ion solution, volume of sorbate, sorbent doze, and agitation speed and agitation time. Maximum sorption efficiency of mulberry leaves for As (III) and As (V) at optimum conditions were 2818 μg.g-1 and 4930 μg.g-1, respectively. The experimental data was a good fit to Freundlich and D-R adsorption isotherm. Energy of adsorption was found to be in the range of 3-6 KJ/mole suggesting the physical nature of process. Kinetic data followed the first order rate, Morris-Weber equations. Developed method was applied to remove arsenic from real water samples.

Keywords: arsenic removal, mulberry, adsorption isotherms, kinetics of adsorption

Procedia PDF Downloads 252

Authors: J. R. Wang, Y. Chiang, W. S. Hsu, S. H. Chen, J. H. Yang, S. W. Chen, C. Shih, Y. F. Chang, Y. H. Huang, B. R. Shen

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In this research, the RASCAL code was used to simulate and analyze the postulated UF₆ fire accident which may occur in the Institute of Nuclear Energy Research (INER). There are four main steps in this research. In the first step, the UF₆ data of INER were collected. In the second step, the RASCAL analysis methodology and model was established by using these data. Third, this RASCAL model was used to perform the simulation and analysis of the postulated UF₆ fire accident. Three cases were simulated and analyzed in this step. Finally, the analysis results of RASCAL were compared with the hazardous levels of the chemicals. According to the compared results of three cases, Case 3 has the maximum danger in human health.

Keywords: RASCAL, UF₆, safety, hydrogen fluoride

Procedia PDF Downloads 201