Search results for: food composition data

Authors: Khan R. Rahaman, Quazi K. Hassan

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Global changes in climate, environment, economies, populations, governments, institutions, and cultures converge in localities. Changes at a local scale, in turn, contribute to global changes as well as being affected by them. Our hypothesis is built on a consideration that temperature does vary at local level (i.e., termed as local warming) in comparison to the predicted models at the regional and/or global scale. To date, the bulk of the research relating local places to global climate change has been top-down, from the global toward the local, concentrating on methods of impact analysis that use as a starting point climate change scenarios derived from global models, even though these have little regional or local specificity. Thus, our focus is to understand such trends over the southern Alberta, which will enable decision makers, scientists, researcher community, and local people to adapt their policies based on local level temperature variations and to act accordingly. Specific objectives in this study are: (i) to understand the local warming (temperature in particular) trend in context of temperature normal during the period 1961-2010 at point locations using meteorological data; (ii) to validate the data by using specific yearly data, and (iii) to delineate the spatial extent of the local warming trends and understanding influential factors to adopt situation by local governments. Existing data has brought the evidence of such changes and future research emphasis will be given to validate this hypothesis based on remotely sensed data (i.e. MODIS product by NASA).

Keywords: local warming, climate change, urban area, Alberta, Canada

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Authors: Katsuyuki Umezawa, Makoto Nakazawa, Manabu Kobayashi, Yutaka Ishii, Michiko Nakano, Shigeichi Hirasawa

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This research aims to develop a self-study system equipped with an artificial teacher who gives advice to students by detecting the learners and to evaluate language learning in a unified framework. 'Detecting the learners' means that the system understands the learners' learning conditions, such as each learner’s degree of understanding, the difference in each learner’s thinking process, the degree of concentration or boredom in learning, and problem solving for each learner, which can be interpreted from learning behavior. In this paper, we propose a system to efficiently collect brain waves from learners by focusing on only the brain waves among the biological information for 'detecting the learners'. The conventional Electroencephalograph (EEG) measurement method during learning using a simple EEG has the following disadvantages. (1) The start and end of EEG measurement must be done manually by the experiment participant or staff. (2) Even when the EEG signal is weak, it may not be noticed, and the data may not be obtained. (3) Since the acquired EEG data is stored in each PC, there is a possibility that the time of data acquisition will be different in each PC. This time, we developed a system to collect brain wave data on the server side. This system overcame the above disadvantages.

Keywords: artificial teacher, e-learning, self-study system, simple EEG

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Authors: Valentina Alessandra Carvalho do Vale, Elmo Thiago Lins Cöuras Ford

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Increasingly telecommunications sectors are adopting optical technologies, due to its ability to transmit large amounts of data over long distances. However, as in all systems of data transmission, optical communication channels suffer from undesirable and non-deterministic effects, being essential to know the same. Thus, this research allows the assessment of these effects, as well as their characterization and beneficial uses of these effects.

Keywords: optical communication, optical fiber, non-deterministic effects, telecommunication

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Authors: Arturo Daag

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The Philippine Institute of Volcanology and Seismology (PHIVOLCS) is enhancing its liquefaction hazard map towards a detailed probabilistic approach using SDS and geophysical data. Target sites for liquefaction assessment are public schools in Metro Manila. Since target sites are in highly urbanized-setting, the objective of the project is to conduct both non-destructive geotechnical studies using Screw Driving Testing (SDFS) combined with geophysical data such as refraction microtremor array (ReMi), 3 component microtremor Horizontal to Vertical Spectral Ratio (HVSR), and ground penetrating RADAR (GPR). Initial test data was conducted in liquefaction impacted areas from the Mw 6.1 earthquake in Central Luzon last April 22, 2019 Province of Pampanga. Numerous accounts of liquefaction events were documented areas underlain by quaternary alluvium and mostly covered by recent lahar deposits. SDS estimated values showed a good correlation to actual SPT values obtained from available borehole data. Thus, confirming that SDS can be an alternative tool for liquefaction assessment and more efficient in terms of cost and time compared to SPT and CPT. Conducting borehole may limit its access in highly urbanized areas. In order to extend or extrapolate the SPT borehole data, non-destructive geophysical equipment was used. A 3-component microtremor obtains a subsurface velocity model in 1-D seismic shear wave velocity of the upper 30 meters of the profile (Vs30). For the ReMi, 12 geophone array with 6 to 8-meter spacing surveys were conducted. Microtremor data were computed through the Factor of Safety, which is the quotient of Cyclic Resistance Ratio (CRR) and Cyclic Stress Ratio (CSR). Complementary GPR was used to study the subsurface structure and used to inferred subsurface structures and groundwater conditions.

Keywords: screw drive testing, microtremor, ground penetrating RADAR, liquefaction

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Authors: Meziani Samira, Menadi Noreddine, Labga Lahouaria, Chenni Fatima Zohra, Toumi Asma

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A comparative study between two varieties of soya beans was carried out in this work. The method consists of studying and proceeding to prepare a by-product (Flour) from two varieties of soybeans, a Chinese variety imported and marketed in Algeria. The chemical composition of ash, protein and fat was determined in this study. The minerals, namely potassium and sodium, were measured by flame spectrophotometer. In addition, the estimation of the polyphenol content and evaluation of the antioxidant activity Ferric Reducing Antioxidant Power assay (FRAP) f the methanol extracts of the flours were also carried out. The result revealed that soy flour from two cultivars, on average, contained 8% moisture, more than 50% protein, 1.58-1.87g fat, and 0.28-0.30g of ash. A slight difference was found for contents of 489 mg/ml of K + and 20 mg/ml of NA +. In addition, the phenolic content of the methanolic extracts gives a value of almost 37 mg EAG / g for both cultivars of soy flour. The estimated Reductive Antioxidant Iron (FRAP) potency of soy flour might be related to its polyphenol richness, which is similar to the variety of China. The flour Soya varieties tested contained a significant amount of protein and phenolic compounds with good antioxidant properties.

Keywords: soye beans, soya flour, protein, total polyphenols

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Authors: Wajeeha Zahid, Shafquat Rozi, Farhan Raza, Masood Kadir

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Background: Dental caries affects the overall health and well-being of children. Findings from various international studies regarding the association of dental caries with asthma are inconsistent. With the increasing burden of caries and childhood asthma, it becomes imperative for an underdeveloped country like Pakistan where resources are limited to identify whether there is a relationship between the two. This study aims to identify an association between dental caries and asthma. Methods: A cross-sectional study was conducted on 544 children aged 12-15 years recruited from five private schools in Karachi. Information on asthma was collected through the International Study of Asthma and Allergies in Childhood (ISAAC) questionnaire. The questionnaire addressed questions regarding child’s demographics, physician diagnoses of asthma, type of medication administered, family history of asthma and allergies, dietary habits and oral hygiene behavior. Dental caries was assessed using DMFT Index (Decayed, Missing, Filled teeth) index The data was analyzed using Cox proportional Hazard algorithm and crude and adjusted prevalence ratios with 95% CI were reported. Results: This study comprises of 306 (56.3%) boys and 238 (43.8%) girls. The mean age of children was 13.2 ± (0.05) years. The total number of children with carious teeth (DMFT > 0) were 166/544 (30.5%), and the decayed component contributed largely (22.8%) to the DMFT score. The prevalence of physician’s diagnosed asthma was 13%. This study identified almost 7% asthmatic children using the internationally validated International Study of Asthma and Allergies in Childhood (ISAAC) tool and 8 children with childhood asthma were identified by parent interviews. Overall prevalence of asthma was 109/544 (20%). The prevalence of caries in asthmatic children was 28.4% as compared to 31% among non-asthmatic children. The adjusted prevalence ratio of dental caries in asthmatic children was 0.8 (95% CI 0.59-1.29). After adjusting for carious food intake, age, oral hygiene index and dentist visit, the association between asthma and dental caries turned out to be non-significant. Conclusion: There was no association between asthma and dental caries among children who participated in this study.

Keywords: asthma, caries, children, school-based

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Authors: B. G. Oguntuase, M. O. Olofinsae

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This study was carried out to assess the contribution of bush meat to Edo people’s livelihood and the consequence of utilization on conservation. Five markets were selected in Ovia Southwest local government area of Edo State, twenty bush meat sellers were selected from each market. Direct observations were made to document the composition of wild animals under sale in the study area. A total of one hundred questionnaires were administered to the respondents. The questionnaires were all retrieved and analyzed using descriptive analysis. The results show that thirteen animal species are being traded in the area. The price for the animal species (whole animal) ranged from N200 to N9,520. Respondents reported that there is a decline in the animal population over time. Between 64% and 95% of the respondents acknowledged population decline in seven of the thirteen animal species available for sale compared to what it used to be some ten years ago. Sales of wild animal species could be regarded as a profitable business in the rural community, supporting livelihood of the community, but could have devastating effect on conservation as already observed in this study if harvesting of wild animals is not regulated on controlled or sustainable basis.

Keywords: conservation, economic benefits, hunting, population, wild animals

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Authors: Abir Derouiche, Abdesslem Layeb

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Association Rule Mining (ARM) is one of the most popular data mining tasks and it is widely used in various areas. The search for association rules is an NP-complete problem that is why metaheuristics have been widely used to solve it. The present paper presents the ARM as an optimization problem and surveys the proposed approaches in the literature based on metaheuristics.

Keywords: Optimization, Metaheuristics, Data Mining, Association rules Mining

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Authors: Bezzi Nacer

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The study focused on the qualitative and quantitative study of Trace elements contained in the natural phosphate ore of Djebel Onk layer and their behaviour to the various mineralurgic modes of treatment. The main objective is to locate the importance of these contents according to granulometry and their association with the existing mineralogical species and to define how the most appropriate treatment. The raw ore is in first submitted to a prior mechanical treatment consisting of homogenization operations, of grinding and of sifting, in order to separate it into three particle-size classes: fine <100 µm (F); medium 100-500 µm (I) and coarse > 500 µm (G), and then treated by calcination, washing and floatation. The identification of the different mineralogical phases, the chemical composition and the thermal behaviour of these samples were realized by various techniques: MEB, DRX, ATG-ATD, etc. The study of Trace elements, carried out by ICP-MS, identified thirty items, consisting mainly of rare earths and of transition metals. A close relation between trace elements and various minerals phases (apatite, dolomite and silicates), through operations of substitution. These elements are distributed between several mineralogical phases, in particular apatite (strontium, uranium, chrome, barium, cadmium) and silicates (strontium, sodium, nickel, zinc and copper).

Keywords: valorization of natural phosphate ore, heavy metals, qualitative and quantitative analysis, various mineralurgic

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Authors: Yi-Ping Lo, Shi-Yao Wei, Chih-Chun Ma

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Since Google launched Google Glass in 2012, numbers of commercial wearable devices were released, such as smart belt, smart band, smart shoes, smart clothes ... etc. However, most of these devices perform as sensors to show the readings of measurements and few of them provide the interactive feedback to the user. Furthermore, these devices are single task devices which are not able to communicate with each other. In this paper a new health promotion system, Ubiquitous Life People Informatics Engine (U-Life PIE), will be presented. This engine consists of People Informatics Engine (PIE) and the interactive user interface. PIE collects all the data from the compatible devices, analyzes this data comprehensively and communicates between devices via various application programming interfaces. All the data and informations are stored on the PIE unit, therefore, the user is able to view the instant and historical data on their mobile devices any time. It also provides the real-time hands-free feedback and instructions through the user interface visually, acoustically and tactilely. These feedback and instructions suggest the user to adjust their posture or habits in order to avoid the physical injuries and prevent illness.

Keywords: machine learning, wearable devices, user interface, user experience, internet of things

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Authors: Muhammad Saleem

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Background: Inattentive food choices and engagement in excessive physical activities by male athletes can potentially lead to adverse health consequences. Objective: The aim was to ascertain the occurrence of risk factors associated with the Male Athlete Triad among young elite athletes in Pakistan. Methodology: In 2018, a cross-sectional study based on questionnaires was conducted at the Pakistan Sports Board. The study aimed to explore the risk factors related to the Male Athlete Triad in young elite athletes who were part of national training camps in major metropolitan areas. The study included proficient male elite athletes aged 18 to 25 years, capable of understanding the English questionnaire. The athletes completed a survey encompassing aspects like demographic information, educational background, Body Mass Index (BMI), sports involvement, and hours of participation. Additionally, they filled out the Eating Attitude Test-26 (EAT-26) and questionnaires assessing risks of amenorrhea and low bone mineral density. The prevalence of risk factors for each of the three components was individually evaluated. The collected data underwent analysis using SPSS-20, with descriptive statistics being applied. Results: The study comprised a sample of 90 elite athletes (mean age: 23.57 ± 2.37 years, mean BMI: 21.97 ± 1.90) engaged in various sports. The EAT-26 results indicated that 50% of athletes were at risk of developing an eating disorder. Moreover, 83.3% exhibited disordered eating behaviors that necessitated referral. Risks for amenorrhea were observed in 15% of the participants, and regarding low bone mineral density, notable risks were absent except for the consumption of caffeinated beverages, which was noted in 51.7% of participants. Conclusion: The study identified a significant prevalence of disordered eating risk among male elite athletes in Pakistan. However, the risks associated with amenorrhea and low bone mineral density were not a major concern in this particular group.

Keywords: Pakistan, osteoporosis, female athlete triad, bone mineral density, athlete, amenorrhea, eating disorders

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Authors: Haruna Saidu, Sulaiman Mohammed, Abdulkarim Ali Deba, Shaza Eva Mohamad

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Determining the diversity of the indigenous microorganisms in Palm Oil Mill Effluent (POME) could allow their wider application for the treatment of recalcitrant agro-based wastewater discharge into the environment. Many research studies mainly determined the efficiency of microorganism or their co-cultivation with microalgae for enhanced treatment of wastewater, suggesting a limited emphasis on the application of microbial diversity. In this study, the microorganism was cultured in POME for a period of 15 days using microalgae as a source of carbon. Pyrosequencing analysis reveals a diversity of microbial community in 20% (v/v) culture than the control experiment. Most of the bacterial species identified in POME belong to the families of Bacillaceae, Paenibacillaceae, Enterococcaceae, Clostridiaceae, Peptostreptococcaceae, Caulobacteraceae, Enterobacteriaceae, Moraxellaceae, and Pseudomonadaceae. Alpha (α) diversity analysis reveals the high composition of the microbial community of 52 in both samples. Beta (β) diversity index indicated the occurrence of similar species of microorganisms in unweighted uni fra than the weighted uni fra of both samples. It is therefore suggested that bacteria found in these families could have a potential for synergistic treatment of high-strength wastewater generated from the palm oil industry.

Keywords: diversity, microorganism, wastewater, pyrosequencing, palm oil mill effluent

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Authors: Julia Desiree Velastegui Caceres, Luis Alejandro Velastegui Caceres, Oswaldo Padilla, Eduardo Kirby, Francisco Guerrero, Theofilos Toulkeridis

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It is fundamental to conserve sites of natural and cultural heritage with any available technique or existing methodology of preservation in order to sustain them for the following generations. We propose a further skill to protect the actual view of such sites, in which with high technology instrumentation we are able to digitally preserve natural and cultural heritages applied in Ecuador. In this project the use of laser technology is presented for three-dimensional models, with high accuracy in a relatively short period of time. In Ecuador so far, there are not any records on the use and processing of data obtained by this new technological trend. The importance of the project is the description of the methodology of the laser scanner system using the Faro Laser Scanner Focus 3D 120, the method for 3D modeling of geospatial data and the development of virtual environments in the areas of Cultural and Natural Heritage. In order to inform users this trend in technology in which three-dimensional models are generated, the use of such tools has been developed to be able to be displayed in all kinds of digitally formats. The results of the obtained 3D models allows to demonstrate that this technology is extremely useful in these areas, but also indicating that each data campaign needs an individual slightly different proceeding starting with the data capture and processing to obtain finally the chosen virtual environments.

Keywords: laser scanner system, 3D model, cultural heritage, natural heritage

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Authors: Mohadeseh Shojaei Shahrokhabadi, Ding-Geng (Din) Chen

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Positive continuous outcomes with a substantial number of zero values and incomplete longitudinal follow-up are quite common in medical cost data. To jointly model semi-continuous longitudinal cost data and survival data and to provide marginalized covariate effect estimates, a marginalized two-part joint model (MTJM) has been developed for outcome variables with lognormal distributions. In this paper, we propose MTJM models for outcome variables from a generalized gamma (GG) family of distributions. The GG distribution constitutes a general family that includes approximately all of the most frequently used distributions like the Gamma, Exponential, Weibull, and Log Normal. In the proposed MTJM-GG model, the conditional mean from a conventional two-part model with a three-parameter GG distribution is parameterized to provide the marginal interpretation for regression coefficients. In addition, MTJM-gamma and MTJM-Weibull are developed as special cases of MTJM-GG. To illustrate the applicability of the MTJM-GG, we applied the model to a set of real electronic health record data recently collected in Iran, and we provided SAS code for application. The simulation results showed that when the outcome distribution is unknown or misspecified, which is usually the case in real data sets, the MTJM-GG consistently outperforms other models. The GG family of distribution facilitates estimating a model with improved fit over the MTJM-gamma, standard Weibull, or Log-Normal distributions.

Keywords: marginalized two-part model, zero-inflated, right-skewed, semi-continuous, generalized gamma

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Authors: Nishank Raisinghani

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Efficiently predicting drug response remains a challenge in the realm of drug discovery. To address this issue, we propose four model architectures that combine graphical representation with varying positions of multiheaded self-attention mechanisms. By leveraging two types of multi-omics data, transcriptomics and genomics, we create a comprehensive representation of target cells and enable drug response prediction in precision medicine. A majority of our architectures utilize multiple transformer models, one with a graph attention mechanism and the other with a multiheaded self-attention mechanism, to generate latent representations of both drug and omics data, respectively. Our model architectures apply an attention mechanism to both drug and multiomics data, with the goal of procuring more comprehensive latent representations. The latent representations are then concatenated and input into a fully connected network to predict the IC-50 score, a measure of cell drug response. We experiment with all four of these architectures and extract results from all of them. Our study greatly contributes to the future of drug discovery and precision medicine by looking to optimize the time and accuracy of drug response prediction.

Keywords: drug discovery, transformers, graph neural networks, multiomics

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Authors: Megha Rao, Søren H. Jensen, Xiufu Sun, Anke Hagen, Mogens B. Mogensen

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Solid oxide electrolysis cells have an immense potential in converting CO₂ and H₂O into syngas during co-electrolysis operation. The produced syngas can be further converted into hydrocarbons. This kind of technology is called power-to-gas or power-to-liquid. To produce hydrocarbons via this route, durability of the cells is still a challenge, which needs to be further investigated in order to improve the cells. In this work, various nickel-yttria stabilized zirconia (Ni-YSZ) fuel electrode supported or YSZ electrolyte supported cells, cerium gadolinium oxide (CGO) barrier layer, and an oxygen electrode are investigated for durability under co-electrolysis conditions in both galvanostatic and potentiostatic conditions. While changing the gas on the oxygen electrode, keeping the fuel electrode gas composition constant, a change in the gas concentration arc was observed by impedance spectroscopy. Measurements of open circuit potential revealed the presence of leaks in the setup. It is speculated that the change in concentration impedance may be related to the leaks. Furthermore, the cells were also tested under pressurized conditions to find an inter-play between the leak rate and the pressure. A mathematical modeling together with electrochemical and microscopy analysis is presented.

Keywords: co-electrolysis, durability, leaks, gas concentration arc

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Authors: Ling Qi, Matloob Khushi, Josiah Poon

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This paper introduces a pre-trained Masked Candlestick Model (MCM) for trading time-series data. The pre-trained model is based on three core designs. First, we convert trading price data at each data point as a set of normalized elements and produce embeddings of each element. Second, we generate a masked sequence of such embedded elements as inputs for self-supervised learning. Third, we use the encoder mechanism from the transformer to train the inputs. The masked model learns the contextual relations among the sequence of embedded elements, which can aid downstream classification tasks. To evaluate the performance of the pre-trained model, we fine-tune MCM for three different downstream classification tasks to predict future price trends. The fine-tuned models achieved better accuracy rates for all three tasks than the baseline models. To better analyze the effectiveness of MCM, we test the same architecture for three currency pairs, namely EUR/GBP, AUD/USD, and EUR/JPY. The experimentation results demonstrate MCM’s effectiveness on all three currency pairs and indicate the MCM’s capability for signal extraction from trading data.

Keywords: masked language model, transformer, time series prediction, trading prediction, embedding, transfer learning, self-supervised learning

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Authors: Muhammad Nouman, Fahad Tiwana, Muhammad Irfan, Mohsin Tiwana

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Traffic congestion has been increasing significantly in major metropolitan areas as a result of increased motorization, urbanization, population growth and changes in the urban density. Traffic congestion compromises efficiency of transport infrastructure and causes multiple traffic concerns; including but not limited to increase of travel time, safety hazards, air pollution, and fuel consumption. Traffic management has become a serious challenge for federal and provincial governments, as well as exasperated commuters. Effective, flexible, efficient and user-friendly traffic information/database management systems characterize traffic conditions by making use of traffic counts for storage, processing, and visualization. While, the emerging data collection technologies continue to proliferate, its accuracy can be guaranteed through the comparison of observed data with the manual handheld counters. This paper presents the design of tablet based manual traffic counting application and framework for development of traffic database management system for Pakistan. The database management system comprises of three components including traffic counting android application; establishing online database and its visualization using Google maps. Oracle relational database was chosen to develop the data structure whereas structured query language (SQL) was adopted to program the system architecture. The GIS application links the data from the database and projects it onto a dynamic map for traffic conditions visualization. The traffic counting device and example of a database application in the real-world problem provided a creative outlet to visualize the uses and advantages of a database management system in real time. Also, traffic data counts by means of handheld tablet/ mobile application can be used for transportation planning and forecasting.

Keywords: manual count, emerging data sources, traffic information quality, traffic surveillance, traffic counting device, android; data visualization, traffic management

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Authors: A. Al-Rashidi, A. El-Hamalawi

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In this paper, a performance study was conducted to investigate single and dual-axis PV systems to generate electricity in five different sites in Kuwait. Relevant data were obtained by using two sources for validation purposes. A commercial software, PVsyst, was used to analyse the data, such as metrological data and other input parameters, and compute the performance parameters such as capacity factor (CF) and final yield (YF). The results indicated that single and dual-axis PV systems would be very beneficial to electricity generation in Kuwait as an alternative source to conventional power plants, especially with the increased demand over time. The ranges were also found to be competitive in comparison to leading countries using similar systems. A significant increase in CF and YF values around 24% and 28.8% was achieved related to the use of single and dual systems, respectively.

Keywords: single-axis and dual-axis photovoltaic systems, capacity factor, final yield, Kuwait

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Authors: Metahri Leyla

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Herbal medicine is one of the oldest medicines in the world. It constitutes an interesting alternative to treat and cure without creating new diseases. Despite the progress of medicine, the increase in the number of doctors, the creation of social security, many parents have resorted to herbal medicine for their children; they are increasingly asking for "natural remedies", "without risk" for their children. Herbal tea is a very accessible way to enjoy the benefits of herbal medicine. Accordingly; the objective of our study is to obtain detailed information on the composition and mode of administration of these herbal teas and to identify the different plants used; their beneficial effects, as well as their possible toxicity. The current research work represents an ethnobotanical survey spread over one month (from January 6, 2021, to February 19, 2021) carried out by means of an electronic questionnaire concerning 753 respondents involving single or multiparous mothers. The obtained results reveal that a total of 684 mothers used herbal teas for their infants, which revealed the use of 55 herbal remedies for several indications, the most sought after are the carminative effect and relief of colic, and which 9% of users noticed undesirable effects linked to the administration of herbal teas to their infants. As a conclusion, it has been asserted that the use of herbal teas as a natural remedy by Algerian mothers is a widely accepted practice, however, the "natural" nature of the plants does not mean that they are harmless.

Keywords: herbal medicine, herbal teas, children, mothers, medicinal plants

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Authors: Jing Jiang, Wenhuan XU, Lei Zhang, Shiyi Zhang, Tongli Wang

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Climate data quality significantly affects the reliability of ecological modeling. In the Asia Pacific (AP) region, low-quality climate data hinders ecological modeling. ClimateAP, a software developed in 2017, generates high-quality climate data for the AP region, benefiting researchers in forestry and agriculture. However, its adoption remains limited. This study aims to confirm the validity of biologically relevant variable data generated by ClimateAP during the normal climate period through comparison with the currently available gridded data. Climate data from 2,366 weather stations were used to evaluate the prediction accuracy of ClimateAP in comparison with the commonly used gridded data from WorldClim1.4. Univariate regressions were applied to 48 monthly biologically relevant variables, and the relationship between the observational data and the predictions made by ClimateAP and WorldClim was evaluated using Adjusted R-Squared and Root Mean Squared Error (RMSE). Locations were categorized into mountainous and flat landforms, considering elevation, slope, ruggedness, and Topographic Position Index. Univariate regressions were then applied to all biologically relevant variables for each landform category. Random Forest (RF) models were implemented for the climatic niche modeling of Cunninghamia lanceolata. A comparative analysis of the prediction accuracies of RF models constructed with distinct climate data sources was conducted to evaluate their relative effectiveness. Biologically relevant variables were obtained from three unpublished Chinese meteorological datasets. ClimateAPv3.0 and WorldClim predictions were obtained from weather station coordinates and WorldClim1.4 rasters, respectively, for the normal climate period of 1961-1990. Occurrence data for Cunninghamia lanceolata came from integrated biodiversity databases with 3,745 unique points. ClimateAP explains a minimum of 94.74%, 97.77%, 96.89%, and 94.40% of monthly maximum, minimum, average temperature, and precipitation variances, respectively. It outperforms WorldClim in 37 biologically relevant variables with lower RMSE values. ClimateAP achieves higher R-squared values for the 12 monthly minimum temperature variables and consistently higher Adjusted R-squared values across all landforms for precipitation. ClimateAP's temperature data yields lower Adjusted R-squared values than gridded data in high-elevation, rugged, and mountainous areas but achieves higher values in mid-slope drainages, plains, open slopes, and upper slopes. Using ClimateAP improves the prediction accuracy of tree occurrence from 77.90% to 82.77%. The biologically relevant climate data produced by ClimateAP is validated based on evaluations using observations from weather stations. The use of ClimateAP leads to an improvement in data quality, especially in non-mountainous regions. The results also suggest that using biologically relevant variables generated by ClimateAP can slightly enhance climatic niche modeling for tree species, offering a better understanding of tree species adaptation and resilience compared to using gridded data.

Keywords: climate data validation, data quality, Asia pacific climate, climatic niche modeling, random forest models, tree species

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Authors: S. Nandagopalan, N. Pradeep

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The aim of this paper is to propose a general framework for storing, analyzing, and extracting knowledge from two-dimensional echocardiographic images, color Doppler images, non-medical images, and general data sets. A number of high performance data mining algorithms have been used to carry out this task. Our framework encompasses four layers namely physical storage, object identification, knowledge discovery, user level. Techniques such as active contour model to identify the cardiac chambers, pixel classification to segment the color Doppler echo image, universal model for image retrieval, Bayesian method for classification, parallel algorithms for image segmentation, etc., were employed. Using the feature vector database that have been efficiently constructed, one can perform various data mining tasks like clustering, classification, etc. with efficient algorithms along with image mining given a query image. All these facilities are included in the framework that is supported by state-of-the-art user interface (UI). The algorithms were tested with actual patient data and Coral image database and the results show that their performance is better than the results reported already.

Keywords: active contour, Bayesian, echocardiographic image, feature vector

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Authors: Janet Holland

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Eye tracking is a great way to triangulate multiple data sources for deeper, more complete knowledge of how instructional materials are really being used and emotional connections made. Using sensor based biometrics provides a detailed local analysis in real time expanding our ability to collect science based data for a more comprehensive level of understanding, not previously possible, for teaching and learning. The knowledge gained will be used to make future improvements to instructional materials, tools, and interactions. The literature has been examined and a preliminary pilot test was implemented to develop a methodology for research in Instructional Design and Technology. Eye tracking now offers the addition of objective metrics obtained from eye tracking and other biometric data collection with analysis for a fresh perspective.

Keywords: area of interest, eye tracking, biometrics, fixation, fixation count, fixation sequence, fixation time, gaze points, heat map, saccades, time to first fixation

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Authors: M. T. Alodat

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In this paper, we propose a mechanism for skewing any symmetric distribution. The new distribution is called the deflation-inflation distribution (DID). We discuss some statistical properties of the DID such moments, stochastic representation, log-concavity. Also we fit the distribution to real data and we compare it to normal distribution and Azzlaini's skew normal distribution. Numerical results show that the DID fits the the tree ring data better than the other two distributions.

Keywords: normal distribution, moments, Fisher information, symmetric distributions

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Authors: Illyas Md Isa, Sharifah Norain Mohd Sharif, Norhayati Hashim

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A mercury(II) sensor was developed by using multi-walled carbon nano tubes (MWCNTs) paste electrode modified with Zn/Al layered double hydroxide-3(4-methoxyphenyl) propionate nano composite (Zn/Al-HMPP). The optimum conditions by cyclic voltammetry were observed at electrode composition 2.5% (w/w) of Zn/Al-HMPP/MWCNTs, 0.4 M potassium chloride, pH 4.0, and scan rate of 100 mVs-1. The sensor exhibited wide linear range from 1x10-3 M to 1x10-7 M Hg2+ and 1x10-7 M to 1x10-9 M Hg2+, with a detection limit of 1 x 10-10 M Hg2+. The high sensitivity of the proposed electrode towards Hg(II) was confirmed by double potential-step chronocoulometry which indicated these values; diffusion coefficient 1.5445 x 10-9 cm2 s-1, surface charge 524.5 µC s-½ and surface coverage 4.41 x 10-2 mol cm-2. The presence of 25-fold concentration of most metal ions had no influence on the anodic peak current. With characteristics such as high sensitivity, selectivity and repeatability the electrode was then proposed as the appropriate alternative for the determination of mercury.

Keywords: Cyclic voltammetry, Mercury(II), Modified carbon paste electrode, Nanocomposite

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Authors: Najoua El Hajjaji El Idrissi, Necip Gokhan Kasapoglu

Abstract:

Polarimetric Synthetic Aperture Radar-based imaging is a powerful technique used for earth observation and classification of surfaces. Forest evolution has been one of the vital areas of attention for the remote sensing experts. The information about forest areas can be achieved by remote sensing, whether by using active radars or optical instruments. However, due to several weather constraints, such as cloud cover, limited information can be recovered using optical data and for that reason, Polarimetric Synthetic Aperture Radar (PolSAR) is used as a powerful tool for forestry inventory. In this [14paper, we applied support vector machine (SVM) and Mahalanobis distance to the fully polarimetric AIRSAR P, L, C-bands data from the Nezer forest areas, the classification is based in the separation of different tree ages. The classification results were evaluated and the results show that the SVM performs better than the Mahalanobis distance and SVM achieves approximately 75% accuracy. This result proves that SVM classification can be used as a useful method to evaluate fully polarimetric SAR data with sufficient value of accuracy.

Keywords: classification, synthetic aperture radar, SAR polarimetry, support vector machine, mahalanobis distance

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Authors: Shalaka Kadam, Dinesh Apte, Sagar Mainkar

Abstract:

The life cycle of products is becoming shorter and shorter due to increased competition in market, shorter product development time and increased product diversity. Short life cycles are normal in retail industry, style business, entertainment media, and telecom and semiconductor industry. The subject of accurate forecasting for demand of short lifecycle products is of special enthusiasm for many researchers and organizations. Due to short life cycle of products the amount of historical data that is available for forecasting is very minimal or even absent when new or modified products are launched in market. The companies dealing with such products want to increase the accuracy in demand forecasting so that they can utilize the full potential of the market at the same time do not oversupply. This provides the challenge to develop a forecasting model that can forecast accurately while handling large variations in data and consider the complex relationships between various parameters of data. Many statistical models have been proposed in literature for forecasting time series data. Traditional time series forecasting models do not work well for short life cycles due to lack of historical data. Also artificial neural networks (ANN) models are very time consuming to perform forecasting. We have studied the existing models that are used for forecasting and their limitations. This work proposes an effective and powerful forecasting approach for short life cycle time series forecasting. We have proposed an approach which takes into consideration different scenarios related to data availability for short lifecycle products. We then suggest a methodology which combines statistical analysis with structured judgement. Also the defined approach can be applied across domains. We then describe the method of creating a profile from analogous products. This profile can then be used for forecasting products with historical data of analogous products. We have designed an application which combines data, analytics and domain knowledge using point-and-click technology. The forecasting results generated are compared using MAPE, MSE and RMSE error scores. Conclusion: Based on the results it is observed that no one approach is sufficient for short life-cycle forecasting and we need to combine two or more approaches for achieving the desired accuracy.

Keywords: forecast, short life cycle product, structured judgement, time series

Procedia PDF Downloads 339

Authors: Victor Bodell, Lukas Ekstrom, Somayeh Aghanavesi

Abstract:

Fuel consumption (FC) is one of the key factors in determining expenses of operating a heavy-duty vehicle. A customer may therefore request an estimate of the FC of a desired vehicle. The modular design of heavy-duty vehicles allows their construction by specifying the building blocks, such as gear box, engine and chassis type. If the combination of building blocks is unprecedented, it is unfeasible to measure the FC, since this would first r equire the construction of the vehicle. This paper proposes a machine learning approach to predict FC. This study uses around 40,000 vehicles specific and o perational e nvironmental c onditions i nformation, such as road slopes and driver profiles. A ll v ehicles h ave d iesel engines and a mileage of more than 20,000 km. The data is used to investigate the accuracy of machine learning algorithms Linear regression (LR), K-nearest neighbor (KNN) and Artificial n eural n etworks (ANN) in predicting fuel consumption for heavy-duty vehicles. Performance of the algorithms is evaluated by reporting the prediction error on both simulated data and operational measurements. The performance of the algorithms is compared using nested cross-validation and statistical hypothesis testing. The statistical evaluation procedure finds that ANNs have the lowest prediction error compared to LR and KNN in estimating fuel consumption on both simulated and operational data. The models have a mean relative prediction error of 0.3% on simulated data, and 4.2% on operational data.

Keywords: artificial neural networks, fuel consumption, friedman test, machine learning, statistical hypothesis testing

Procedia PDF Downloads 162

Authors: Sinan Alsaadi, Mustafa Merdan

Abstract:

Communication and access systems can be made with many methods in today’s world. These systems are such standards as Wifi, Wimax, Bluetooth, GPS and GPRS. Devices which use these standards also use system resources excessively in direct proportion to their transmission speed. However, large-scale data communication is not always needed. In such cases, a technology which will use system resources as little as possible and support smart network topologies has been needed in order to enable the transmissions of such small packet data and provide the control for this kind of devices. IEEE issued 802.15.4 standard upon this necessity and enabled the production of Zigbee protocol which takes these standards as its basis and devices which support this protocol. In our project, this communication protocol was preferred. The aim of this study is to provide the immediate data transmission of our robot from the field within the scope of the project. In addition, making the communication with the robot through Zigbee Protocol has also been aimed. While sitting on the computer, obtaining the desired data from the region where the robot is located has been taken as the basis. Arduino Uno R3 microcontroller which provides the control mechanism, 1298 shield as the motor driver.

Keywords: ZigBee, wireless network, remote monitoring, smart home, agricultural industry

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Authors: Hamdi Belfeki, Belgacem Chandoul, Mnasser Hassouna, Mondher Mejri

Abstract:

Aqueous and ethanolic extracts of eight Tunisian aromatic and medicinal plants (TAMP) were characterized by studying their composition in polyphenols and also their antiradical and antioxidant capacities. In absence and in the presence of the various extracts, α-amylase from Bacillus subtlis activity, was measured in order to detect a potential inhibition. The total contents of polyphenols and flavonoid vary in function of TAMP and the mobile phase used for the extraction (distilled water or ethanol). The ethanolic extracts showed the most significant antiradical and antioxidant activities. Only the extracts from Coriandrum sativum showed a significant inhibiting effect on the α-amylase activity. This inhibiting capacity could be correlated with the chemical profile of the two extracts, due to the fact that they have the greatest amount of total flavonoid. The ethanolic extract has the most important antioxidant and anti-radicalizing activities among the sixteen extracts studied. The inhibition kinetics of the two coriander extracts were evaluated by pre-incubation method, using Lineweaver-Burk’s equation, obtained by linearization of Michaeilis-Menten’s expression. The results showed that both extracts exercised a competitive inhibition mechanism.

Keywords: α-amylase, antioxidant activity, aromatic and medicinal plants, inhibition

Procedia PDF Downloads 431