Search results for: data storage

Authors: Deena Ramful-Baboolall, Eshana B. N. Bhatoo

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Morinda citrifolia L., commonly known as noni fruit, is rich in phytochemicals. This study investigated the phytophenolics content and antioxidant activity of green, mature green and ripe noni fruits. The vitamin C content ranged from 41.12 ± 0.083 to 143.63 ± 0.146 mg / 100 ml in fresh noni fruits. Ripe fruits contained the highest level of ascorbic acid followed by mature green and green fruits (p < 0.05). The total phenol content ranged from 0.909 (green) to 2.305 (ripe) mg / g of FW whilst the total flavonoid content ranged from 1.054 (green) to 2.116 (ripe) mg/g of FW. The in vitro antioxidant activity of the Morinda citrifolia L. extracts was also analysed using FRAP and TEAC assays. The reducing power of the fruit extracts as assessed by the FRAP assay decreased in the following order: ripe > mature green > green (p < 0.05). The TEAC values ranged from 0.2631 to 0.8921 µmol / g FW, with extracts of fruits at the mature green stage having highest values followed by fruits at the ripe and green stage respectively (p < 0.05). High correlation values were obtained between total phenolics, total flavonoids, ascorbic acid contents and the TEAC and FRAP assays (r > 0.8). Noni fruit extracts (0.2 and 0.4 % m / m) were compared with BHT (0.02 % m / m) on their ability to protect canola oil and mayonnaise, prepared with canola oil, against lipid oxidation during storage at 40°C. Mature green and ripe extracts, at both concentrations, were more effective than BHT in retarding oxidation in both food systems as evidenced by peroxide value and conjugated diene value determinations. Noni extracts were also very effective in inhibiting lipid peroxidation in tuna fish homogenates, assessed using TBARS assay. Noni fruits at the mature green and ripe stages represent a potential source of natural antioxidants for use a food additive.

Keywords: antioxidant, canola oil, mayonnaise, Morinda citrifolia L. fruit extracts, total flavonoids, total phenol

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Authors: Yue Huang, Yiheng Feng

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Due to the global pressure on climate and energy, many countries are vigorously promoting electric vehicles and building charging (public) charging facilities. Faced with the supply-demand gap of existing electric vehicle charging stations and unreasonable space usage in China, this paper takes the central city of Nanjing as an example, establishes a site selection model through multivariate data integration, conducts multiple linear regression SPSS analysis, gives quantitative site selection results, and provides optimization models and suggestions for charging station layout planning.

Keywords: electric vehicle, charging station, allocation optimization, urban mobility, urban infrastructure, nanjing

Procedia PDF Downloads 77

Authors: Pei-Jyun Hsieh, Cheng-Hsuan Li, Bor-Chen Kuo

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For hyperspectral image classification, feature reduction is an important pre-processing for avoiding the Hughes phenomena due to the difficulty for collecting training samples. Hence, lots of researches developed feature selection methods such as F-score, HSIC (Hilbert-Schmidt Independence Criterion), and etc., to improve hyperspectral image classification. However, most of them only consider the class separability in the original space, i.e., a linear class separability. In this study, we proposed a nonlinear class separability measure based on kernel trick for selecting an appropriate feature subset. The proposed nonlinear class separability was formed by a generalized RBF kernel with different bandwidths with respect to different features. Moreover, it considered the within-class separability and the between-class separability. A genetic algorithm was applied to tune these bandwidths such that the smallest with-class separability and the largest between-class separability simultaneously. This indicates the corresponding feature space is more suitable for classification. In addition, the corresponding nonlinear classification boundary can separate classes very well. These optimal bandwidths also show the importance of bands for hyperspectral image classification. The reciprocals of these bandwidths can be viewed as weights of bands. The smaller bandwidth, the larger weight of the band, and the more importance for classification. Hence, the descending order of the reciprocals of the bands gives an order for selecting the appropriate feature subsets. In the experiments, three hyperspectral image data sets, the Indian Pine Site data set, the PAVIA data set, and the Salinas A data set, were used to demonstrate the selected feature subsets by the proposed nonlinear feature selection method are more appropriate for hyperspectral image classification. Only ten percent of samples were randomly selected to form the training dataset. All non-background samples were used to form the testing dataset. The support vector machine was applied to classify these testing samples based on selected feature subsets. According to the experiments on the Indian Pine Site data set with 220 bands, the highest accuracies by applying the proposed method, F-score, and HSIC are 0.8795, 0.8795, and 0.87404, respectively. However, the proposed method selects 158 features. F-score and HSIC select 168 features and 217 features, respectively. Moreover, the classification accuracies increase dramatically only using first few features. The classification accuracies with respect to feature subsets of 10 features, 20 features, 50 features, and 110 features are 0.69587, 0.7348, 0.79217, and 0.84164, respectively. Furthermore, only using half selected features (110 features) of the proposed method, the corresponding classification accuracy (0.84168) is approximate to the highest classification accuracy, 0.8795. For other two hyperspectral image data sets, the PAVIA data set and Salinas A data set, we can obtain the similar results. These results illustrate our proposed method can efficiently find feature subsets to improve hyperspectral image classification. One can apply the proposed method to determine the suitable feature subset first according to specific purposes. Then researchers can only use the corresponding sensors to obtain the hyperspectral image and classify the samples. This can not only improve the classification performance but also reduce the cost for obtaining hyperspectral images.

Keywords: hyperspectral image classification, nonlinear feature selection, kernel trick, support vector machine

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Authors: Jung-Hong Hong, Jing-Cen Yang, Cai-Yu Ou

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The analysis of geographic inequality heavily relies on the use of location-enabled statistical data and quantitative measures to present the spatial patterns of the selected phenomena and analyze their differences. To protect the privacy of individual instance and link to administrative units, point-based datasets are spatially aggregated to area-based statistical datasets, where only the overall status for the selected levels of spatial units is used for decision making. The partition of the spatial units thus has dominant influence on the outcomes of the analyzed results, well known as the Modifiable Areal Unit Problem (MAUP). A new spatial reference framework, the Taiwan Geographical Statistical Classification (TGSC), was recently introduced in Taiwan based on the spatial partition principles of homogeneous consideration of the number of population and households. Comparing to the outcomes of the traditional township units, TGSC provides additional levels of spatial units with finer granularity for presenting spatial phenomena and enables domain experts to select appropriate dissemination level for publishing statistical data. This paper compares the results of respectively using TGSC and township unit on the mortality data and examines the spatial characteristics of their outcomes. For the mortality data between the period of January 1^st, 2008 and December 31^st, 2010 of the Taitung County, the all-cause age-standardized death rate (ASDR) ranges from 571 to 1757 per 100,000 persons, whereas the 2^nd dissemination area (TGSC) shows greater variation, ranged from 0 to 2222 per 100,000. The finer granularity of spatial units of TGSC clearly provides better outcomes for identifying and evaluating the geographic inequality and can be further analyzed with the statistical measures from other perspectives (e.g., population, area, environment.). The management and analysis of the statistical data referring to the TGSC in this research is strongly supported by the use of Geographic Information System (GIS) technology. An integrated workflow that consists of the tasks of the processing of death certificates, the geocoding of street address, the quality assurance of geocoded results, the automatic calculation of statistic measures, the standardized encoding of measures and the geo-visualization of statistical outcomes is developed. This paper also introduces a set of auxiliary measures from a geographic distribution perspective to further examine the hidden spatial characteristics of mortality data and justify the analyzed results. With the common statistical area framework like TGSC, the preliminary results demonstrate promising potential for developing a web-based statistical service that can effectively access domain statistical data and present the analyzed outcomes in meaningful ways to avoid wrong decision making.

Keywords: mortality map, spatial patterns, statistical area, variation

Procedia PDF Downloads 243

Authors: Bruk Gebregziabher

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The safe and efficient operation of Autonomous Mobile Robots (AMRs) in complex environments, such as manufacturing, logistics, and agriculture, necessitates accurate multiobject tracking and predictive collision avoidance. This paper presents algorithms and techniques for addressing these challenges using Lidar sensor data, emphasizing ensemble Kalman filter. The developed predictive collision avoidance algorithm employs the data provided by lidar sensors to track multiple objects and predict their velocities and future positions, enabling the AMR to navigate safely and effectively. A modification to the dynamic windowing approach is introduced to enhance the performance of the collision avoidance system. The overall system architecture encompasses object detection, multi-object tracking, and predictive collision avoidance control. The experimental results, obtained from both simulation and real-world data, demonstrate the effectiveness of the proposed methods in various scenarios, which lays the foundation for future research on global planners, other controllers, and the integration of additional sensors. This thesis contributes to the ongoing development of safe and efficient autonomous systems in complex and dynamic environments.

Keywords: autonomous mobile robots, multi-object tracking, predictive collision avoidance, ensemble Kalman filter, lidar sensors

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Authors: Sogand Barghi

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The integration of artificial intelligence (AI) in accounting practices is reshaping the landscape of financial management. This paper explores the innovative applications of AI in the realm of accounting, emphasizing its transformative impact on efficiency, accuracy, decision-making, and financial insights. By harnessing AI's capabilities in data analysis, pattern recognition, and automation, accounting professionals can redefine their roles, elevate strategic decision-making, and unlock unparalleled value for businesses. This paper delves into AI-driven solutions such as automated data entry, fraud detection, predictive analytics, and intelligent financial reporting, highlighting their potential to revolutionize the accounting profession. Artificial intelligence has swiftly emerged as a game-changer across industries, and accounting is no exception. This paper seeks to illuminate the profound ways in which AI is reshaping accounting practices, transcending conventional boundaries, and propelling the profession toward a new era of efficiency and insight-driven decision-making. One of the most impactful applications of AI in accounting is automation. Tasks that were once labor-intensive and time-consuming, such as data entry and reconciliation, can now be streamlined through AI-driven algorithms. This not only reduces the risk of errors but also allows accountants to allocate their valuable time to more strategic and analytical tasks. AI's ability to analyze vast amounts of data in real time enables it to detect irregularities and anomalies that might go unnoticed by traditional methods. Fraud detection algorithms can continuously monitor financial transactions, flagging any suspicious patterns and thereby bolstering financial security. AI-driven predictive analytics can forecast future financial trends based on historical data and market variables. This empowers organizations to make informed decisions, optimize resource allocation, and develop proactive strategies that enhance profitability and sustainability. Traditional financial reporting often involves extensive manual effort and data manipulation. With AI, reporting becomes more intelligent and intuitive. Automated report generation not only saves time but also ensures accuracy and consistency in financial statements. While the potential benefits of AI in accounting are undeniable, there are challenges to address. Data privacy and security concerns, the need for continuous learning to keep up with evolving AI technologies, and potential biases within algorithms demand careful attention. The convergence of AI and accounting marks a pivotal juncture in the evolution of financial management. By harnessing the capabilities of AI, accounting professionals can transcend routine tasks, becoming strategic advisors and data-driven decision-makers. The applications discussed in this paper underline the transformative power of AI, setting the stage for an accounting landscape that is smarter, more efficient, and more insightful than ever before. The future of accounting is here, and it's driven by artificial intelligence.

Keywords: artificial intelligence, accounting, automation, predictive analytics, financial reporting

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Authors: H. Yousefnia, S. Zolghadri

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The measurement of organ radiation exposure dose is one of the most important steps to be taken initially, for developing a new radiopharmaceutical. In this study, the dosimetric studies of a novel agent for SPECT-imaging of the bone metastasis, 111In-1,4,7,10-tetraazacyclododecane-1,4,7,10 tetraethylene phosphonic acid (111In-DOTMP) complex, have been carried out to estimate the dose in human organs based on the data derived from rats. The radiolabeled complex was prepared with high radiochemical purity in the optimal conditions. Biodistribution studies of the complex was investigated in the male Syrian rats at selected times after injection (2, 4, 24 and 48 h). The human absorbed dose estimation of the complex was made based on data derived from the rats by the radiation absorbed dose assessment resource (RADAR) method. 111In-DOTMP complex was prepared with high radiochemical purity of >99% (ITLC). Total body effective absorbed dose for 111In-DOTMP was 0.061 mSv/MBq. This value is comparable to the other 111In clinically used complexes. The results show that the dose with respect to the critical organs is satisfactory within the acceptable range for diagnostic nuclear medicine procedures. Generally, 111In-DOTMP has interesting characteristics and can be considered as a viable agent for SPECT-imaging of the bone metastasis in the near future.

Keywords: In-111, DOTMP, Internal Dosimetry, RADAR

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Authors: Mahmud Gungor, Ali Aydin, Erdal Akyol, Suat Tasdelen

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The understanding of geotechnical characteristics of near-surface material and the effects of the groundwater is very important problem in such as site studies. For showing the relations between seismic data and groundwater we selected about 25 km2 as the study area. It has been presented which is a detailed work of seismic data and groundwater depths of Gokpinar Damp area. Seismic waves velocity (Vp and Vs) are very important parameters showing the soil properties. The seismic records were used the method of the multichannel analysis of surface waves near area of Gokpinar Damp area. Sixty sites in this area have been investigated with survey lines about 60 m in length. MASW (Multichannel analysis of surface wave) method has been used to generate one-dimensional shear wave velocity profile at locations. These shear wave velocities are used to estimate equivalent shear wave velocity in the study area at every 2 and 5 m intervals up to a depth of 45 m. Levels of equivalent shear wave velocity of soil are used the classified of the study area. After the results of the study, it must be considered as components of urban planning and building design of Gokpinar Damp area, Denizli and the application and use of these results should be required and enforced by municipal authorities.

Keywords: seismic data, Gokpinar Damp, urban planning, Denizli

Procedia PDF Downloads 278

Authors: Arian Ebneyamini, Hoda Azimi, Jules Thibaults, F. Handan Tezel

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In this study, a modification of Hennepe model for pervaporation separation of butanol from aqueous solutions using Polydimethylsiloxane (PDMS) mixed matrix membranes has been introduced and validated by experimental data. The model was compared to the original Hennepe model and few other models which are applicable for membrane gas separation processes such as Maxwell, Lewis Nielson and Pal. Theoretical modifications for non-ideal interface morphology have been offered to predict the permeability in case of interface void, interface rigidification and pore-blockage. The model was in a good agreement with experimental data.

Keywords: butanol, PDMS, modeling, pervaporation, mixed matrix membranes

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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

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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

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Authors: Renzo Castillo, George Tsatsaronis

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High-temperature ceramic membranes for air separation represents an important option to reduce the significant efficiency drops incurred in state-of-the-art cryogenic air separation for high tonnage oxygen production required in oxyfuel power stations. This study is focused on the thermodynamic analysis of two power plant model designs: the state-of-the-art supercritical 600ᵒC hard coal plant (reference power plant Nordrhein-Westfalen) and the membrane-based oxyfuel concept implemented in this reference plant. In the latter case, the oxygen is separated through a mixed-conducting hollow fiber perovskite membrane unit in the three-end operation mode, which has been simulated under vacuum conditions on the permeate side and at high-pressure conditions on the feed side. The thermodynamic performance of each plant concept is assessed by conventional exergetic analysis, which determines location, magnitude and sources of efficiency losses, and advanced exergetic analysis, where endogenous/exogenous and avoidable/unavoidable parts of exergy destruction are calculated at the component and full process level. These calculations identify thermodynamic interdependencies among components and reveal the real potential for efficiency improvements. The endogenous and exogenous exergy destruction portions are calculated by the decomposition method, a recently developed straightforward methodology, which is suitable for complex power stations with a large number of process components. Lastly, an improvement priority ranking for relevant components, as well as suggested changes in process layouts are presented for both power stations.

Keywords: exergy, carbon capture and storage, ceramic membranes, perovskite, oxyfuel combustion

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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

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Authors: Brian Bilich

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A typical global learning program is a two-week project based, culturally immersive and academically relevant experience built around a project with purpose and catered to student and business groups. Global Learning in Continuing Education at Austin Community College promotes global readiness through projects with purpose with special attention given to balancing learning, hospitality and travel. A recent project involved CommunityFirst! Village; a 51-acre planned community which provides affordable, permanent housing for men and women coming out of chronic homelessness. Global Learning students collaborated with residents and staff at the Community First! Village on a project to produce two-dimensional remodeling plans of residents’ tiny homes with a focus on but not limited to design improvements on elements related to accessibility, increased usability of living and storage space and esthetic upgrades to boost psychological and emotional appeal. The goal of project-based learning in the context of global learning in Continuing Educaiton at Austin Community Collegen general is two fold. One, in rapid fashion we develop a project which gives the learner a hands-on opportunity to exercise soft and technical skills, like creativity and communication and analytical thinking. Two, by basing projects on global social conflict issues, the project of purpose promotes the development of empathy for other people and fosters a sense of corporate social responsibility in future generations of business leadership. In the example provide above the project informed the student group on the topic of chronic homelessness and promoted awareness and empathy for this underserved segment of the community. Project-based global learning based on projects with purpose has the potential to cultivate global readiness by developing empathy and strengthening emotional intelligence for future generations.

Keywords: project-based learning, global learning, global readiness, globalization, international exchange, collaboration

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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

Procedia PDF Downloads 37

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: Sílvia Pereira Mello, Eliane Rodrigues, Maria de Lourdes Andrade, Marcelo Pereira, Giselle Dias, Jose Seixas Filho

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Frog meat is a highly digestible food and its use is recommended in diets aimed at fighting cholesterol, obesity, and arterial hypertension, as well as for treating gastrointestinal disorders. In this study, the soups were developed with frog meat in addition to other ingredients which did not present allergenic potential. The carcasses of the thawed frogs went through bleaching and deboning, and other ingredients (vegetables and condiments) were then added to the separated meat. After the process of cooking, the soups were cooled and later on frozen at -40° C for 3 hours and then taken to the LS 3000 B lyophilizer for 24 hours. The soups were submitted to microbiological analysis: enumeration of total coliforms and Bacillus cereus; identification of coagulase positive Staphylococcus; isolation and identification of Salmonella spp.; and physical-chemical analysis; application of micro-Kjeldahl method for protein, Soxhlet method for lipids, use of a heating chamber at 105ºC for moisture, incineration method (500-550°C) for ash, and Decagon's Pawkit equipment for determining water activity. Acceptance test was performed with 50 elderly people, all between 60 and 85 years of age. The degree of acceptance was demonstrated using a seven points structured hedonic scale in which the taster expressed their impression towards the product. Results of the microbiological analysis showed that all samples met the standards established by the National Health Surveillance Agency of Brazil (ANVISA). Results of the acceptance test indicated that all the soups were accepted considering overall impression and intended consumption. In addition to its excellent nutritional quality, the dehydrated soups made with frog meat are presented as a solution for consumers due to convenience in preparation, consumption and storage.

Keywords: bacteriological quality, lithobates catesbeianus, instant soup, proximate composition, sensory analysis

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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: John Gichimu Mbaka, Jan Helmrich Martin von Baumbach, Celia Somlai, Denis Köpfer, Andreas Maeck, Andreas Lorke, Ralf Schäfer

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Leaf litter decomposition is an important process providing energy to biotic communities. Additionally, methane gas (CH4) has been identified as an important alternative source of carbon and energy in some freshwater food webs.Flow regulation and dams can strongly alter freshwater ecosystems, but little is known about the effect of small impoundments on leaf litter decomposition and methane derived carbon in streams. In this study, we tested the effect of small water storage impoundments on leaf litter decomposition rates and methane derived carbon. Leaf litter decomposition rates were assessed by comparing treatment sites located close to nine impoundments (Rheinland Pfalz state, Germany) and reference sites located far away from the impoundments.CH4 concentrations were measured in eleven impoundments and correlated with the δ13C values of two subfamilies of chironomid larvae (i.e. Chironomini and Tanypodinae). Leaf litter break down rates were significantly lower in study sites located immediately above the impoundments, especially associated with a reduction in the abundance of shredders. Chironomini larvae had the lower mean δ13C values (‒29.2 to ‒25.5 ‰), than Tanypodinae larvae (‒26.9 to ‒25.3 ‰).No significant relationships were established between CH4 concentrations and δ13C values of chironomids (p> 0.05).Mean δ13C values of chironomid larvae (mean: ‒26.8‰, range: ‒ 29.2‰ to ‒ 25.3‰) were similar to those of sedimentary organic matter (SOM) (mean: ‒28.4‰, range: ‒ 29.3‰ to ‒ 27.1‰) and tree leaf litter (mean: ‒29.8 ‰, range: ‒ 30.5‰ to ‒ 29.1‰). In conclusion, this study demonstrates that small impoundments may have a negative effect on leaf litter decomposition in forest streams and that CH4 has limited influence on the benthic food web in stream impoundments.

Keywords: river functioning, chironomids, Alder tree, stable isotopes, methane oxidation, shredder

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Authors: Tanvi P. Patel, Warish D. Patel

Abstract:

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

Abstract:

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

Procedia PDF Downloads 261

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

Abstract:

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: Francisca Margarita Ocran

Abstract:

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: Gidado Lawal Likko

Abstract:

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

Abstract:

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

Abstract:

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

Abstract:

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

Procedia PDF Downloads 89

Authors: Ayalkibet Mekonnen, Adane Abebe

Abstract:

In hydrological cycle there are many water-related human interventions that modify the natural systems. Rainwater harvesting is one such intervention that involves harnessing of water in the upstream. Water harvesting used in upstream prevents water runoff on downstream mainly disturbance on biodiversity and ecosystems. The main objectives of the study are to assess the effects of water harvesting technologies on downstream water availability in the Woreda. To address the above problem, SWAT model, cost-benefit ratio and optimal control approach was used to analyse the hydrological and socioeconomic impact and tradeoffs on water availability of the community, respectively. The downstream impacts of increasing water consumption in the upstream rain-fed areas of the Bilate and Shala Catchment are simulated using the semi-distributed SWAT model. The two land use scenarios tested at sub basin levels (1) conventional land use represents the current land use practice (Agri-CON) and (2) in-field rainwater harvesting (IRWH), improving soil water availability through rainwater harvesting land use scenario. The simulated water balance results showed that the highest peak mean monthly direct flow obtained from Agri-CON land use (127.1 m3/ha), followed by Agri-IRWH land use (11.5 mm) and LULC 2005 (90.1 m3/ha). The Agri-IRWH scenario reduced direct flow by 10% compared to Agri-CON and more groundwater flow contributed by Agri-IRWH (190 m3/ha) than Agri-CON (125 m3/ha). The overall result suggests that the water yield of the Woreda may not be negatively affected by the Agri-IRWH land use scenario. The technology in the Woreda benefited positively having an average benefit cost ratio of 4.2. Water harvesting for domestic use was not optimal that the value of the water per demand harvested was less than the amount of water needed. Storage tanks, series of check dams, gravel filled dams are an alternative solutions for water harvesting.

Keywords: water harvesting, SWAT model, land use scenario, Agri-CON, Agri-IRWH, trade off, benefit cost ratio

Procedia PDF Downloads 321