Search results for: logistic model tree

Authors: F. Ainlhout, S. Boutaleb, M. C. Diaz-Barradas, M. Zunzunegui

Abstract:

Argania spinosa is an endemic tree of the southwest of Morocco, occupying 828,000 Ha, distributed mainly between Mediterranean vegetation and the desert. This tree can grow in extremely arid regions in Morocco, where annual rainfall ranges between 100-300 mm where no other tree species can live. It has been designated as a UNESCO Biosphere reserve since 1998. Argania tree is of great importance in human and animal feeding of rural population as well as for oil production, it is considered as a multi-usage tree. Admine forest located in the suburbs of Agadir city, 5 km inland, was selected to conduct this work. The aim of the study was to investigate the temporal variation in root-zone moisture dynamic in response to variation in climatic conditions and vegetation water uptake, using a geophysical technique called Electrical resistivity imaging (ERI). This technique discriminates resistive woody roots, dry and moisture soil. Time-dependent measurements (from April till July) of resistivity sections were performed along the surface transect (94 m Length) at 2 m fixed electrode spacing. Transect included eight Argan trees. The interactions between the tree and soil moisture were estimated by following the tree water status variations accompanying the soil moisture deficit. For that purpose we measured midday leaf water potential and relative water content during each sampling day, and for the eight trees. The first results showed that ERI can be used to accurately quantify the spatiotemporal distribution of root-zone moisture content and woody root. The section obtained shows three different layers: middle conductive one (moistured); a moderately resistive layer corresponding to relatively dry soil (calcareous formation with intercalation of marly strata) on top, this layer is interspersed by very resistant layer corresponding to woody roots. Below the conductive layer, we find the moderately resistive layer. We note that throughout the experiment, there was a continuous decrease in soil moisture at the different layers. With the ERI, we can clearly estimate the depth of the woody roots, which does not exceed 4 meters. In previous work on the same species, analyzing the δ18O in water of xylem and in the range of possible water sources, we argued that rain is the main water source in winter and spring, but not in summer, trees are not exploiting deep water from the aquifer as the popular assessment, instead of this they are using soil water at few meter depth. The results of the present work confirm the idea that the roots of Argania spinosa are not growing very deep.

Keywords: Argania spinosa, electrical resistivity imaging, root system, soil moisture

Procedia PDF Downloads 319

Authors: Sisay Negash, Zebene Asfaw, Kibreselassie Daniel, Michael Zech

Abstract:

A significant portion of the Ethiopian landscape features scattered trees that are deliberately managed in crop fields to enhance soil fertility and crop yield in which the compatibility of crops with these trees varies depending on location, tree species, and annual crop type. This study aimed to examine the effects of scattered Vachellia tortilis and Vachellia nilotica trees on selected physico-chemical properties of the soil, as well as the yield and yield components of sorghum in Ethiopia. Vachellia tortilis and Vachellia nilotica were selected on abundance occurrence and managed in crop fields. A randomized complete block design was used, with a distance from the tree canopy (middle, edge, and outside) as a treatment, and five trees of each species served as replications. Sorghum was planted up to 15 meters in the east, west, south, and north directions from the tree trunk to assess growth and yield. Soil samples were collected from the two tree species, three distance factors, three soil depths(0-20cm, 20-40cm, and 40-60cm), and five replications, totaling 45 samples for each tree species. These samples were analyzed for physical and chemical properties. The results indicated that both V. tortilis and V. nilotica significantly affected soil physico-chemical properties and sorghum yield. Specifically, soil moisture content, EC, total nitrogen, organic carbon, available phosphorus and potassium, CEC, sorghum plant height, panicle length, biomass, and yield decreased with increasing distance from the canopy. Conversely, bulk density and pH increased. Under the canopy, sorghum yield increased by 66.4% and 53.5% for V. tortilis and V. nilotica, respectively, due to higher soil moisture and nutrient availability. The study recommends promoting trees in crop fields, management options for new saplings, and further research on root decomposition and nutrient supply.

Keywords: canopy, crop yield, soil nutrient, soil organic matter, yield components

Procedia PDF Downloads 6

Authors: Rachel Healy

Abstract:

This paper builds on the speaker’s recent identification of an early sixteenth-century painting in the National Gallery of Ireland as containing rare portraits of Giorgio Cornaro (brother of Caterina, Queen of Cyprus) and his son Cardinal Francesco. It resolves similar long-standing confusion regarding the identities of sitters in related works by Titian, Raphael and Bernini, in works such as the Cornaro Triple Portrait in the National Gallery of Art, Washington DC, Man with a Falcon in The Joslyn Art Museum, Omaha, Head of a Cardinal, Wilton House, Wiltshire and The Cornaro Chapel, Santa Maria della Vittoria, Rome, by using an overlooked seventeenth-century painted Cornaro family tree, from Palazzo Corner-Mocenigo, as a tool for identifying these and other sitters in disputed portraits of one of Renaissance Venice’s wealthiest and most influential patrician families. In so doing, it will cast new light on Titian’s development as a portraitist and the extent to which important paintings commissioned by the Cornaro survived fires at two family palaces in Venice in the 1530s. It will also showcase the associations Raphael had with the Cornaro cardinal and will present new evidence relating to the likenesses Bernini fashioned for the Cornaro Chapel in 1647-52.

Keywords: Venice, portraits, titian, genealogy, Bernini, family tree, Raphael, venetian family, cornaro, sixteenth century Venice, portraiture

Procedia PDF Downloads 261

Authors: Yavuz Unal, Kemal Polat, H. Erdinc Kocer

Abstract:

In this study, a decision support system comprising three stages has been proposed to detect the disc abnormalities of the lumbar region. In the first stage named the feature extraction, T2-weighted sagittal and axial Magnetic Resonance Images (MRI) were taken from 55 people and then 27 appearance and shape features were acquired from both sagittal and transverse images. In the second stage named the feature weighting process, k-means clustering based feature weighting (KMCBFW) proposed by Gunes et al. Finally, in the third stage named the classification process, the classifier algorithms including multi-layer perceptron (MLP- neural network), support vector machine (SVM), Naïve Bayes, and decision tree have been used to classify whether the subject has lumbar disc or not. In order to test the performance of the proposed method, the classification accuracy (%), sensitivity, specificity, precision, recall, f-measure, kappa value, and computation times have been used. The best hybrid model is the combination of k-means clustering based feature weighting and decision tree in the detecting of lumbar disc disease based on both sagittal and axial MR images.

Keywords: lumbar disc abnormality, lumbar MRI, lumbar spine, hybrid models, hybrid features, k-means clustering based feature weighting

Procedia PDF Downloads 508

Authors: Ruijia Hu, Susanna T.Y. Tong

Abstract:

Light detection and ranging (LiDAR) and four-channel red, green, blue, and near-infrared (RGBI) remote sensed imageries allow an accurate quantification and contiguous measurement of vegetation characteristics and forest structures. This information facilitates the generation of habitat structure variables for forest species distribution modelling. However, applications of remote sensing data, especially the combination of structural and spectral information, to support evidence-based decisions in forest managements and conservation practices at local scale are not widely adopted. In this study, we examined the habitat requirements of pileated woodpecker (Dryocopus pileatus) (PW) in Hamilton County, Ohio, using ecologically relevant forest structural and vegetation characteristics derived from LiDAR and RGBI data. We hypothesized that the habitat of PW is shaped by vegetation characteristics that are directly associated with the availability of food, hiding and nesting resources, the spatial arrangement of habitat patches within home range, as well as proximity to water sources. We used 186 PW presence or absence locations to model their presence and absence in generalized additive model (GAM) at two scales, representing foraging and home range size, respectively. The results confirm PW’s preference for tall and large mature stands with structural complexity, typical of late-successional or old-growth forests. Besides, the crown size of dead trees shows a positive relationship with PW occurrence, therefore indicating the importance of declining living trees or early-stage dead trees within PW home range. These locations are preferred by PW for nest cavity excavation as it attempts to balance the ease of excavation and tree security. In addition, we found that PW can adjust its travel distance to the nearest water resource, suggesting that habitat fragmentation can have certain impacts on PW. Based on our findings, we recommend that forest managers should use different priorities to manage nesting, roosting, and feeding habitats. Particularly, when devising forest management and hazard tree removal plans, one needs to consider retaining enough cavity trees within high-quality PW habitat. By mapping PW habitat suitability for the study area, we highlight the importance of riparian corridor in facilitating PW to adjust to the fragmented urban landscape. Indeed, habitat improvement for PW in the study area could be achieved by conserving riparian corridors and promoting riparian forest succession along major rivers in Hamilton County.

Keywords: deadwood detection, generalized additive model, individual tree crown delineation, LiDAR, pileated woodpecker, RGBI aerial imagery, species distribution models

Procedia PDF Downloads 43

Authors: Teklu Urgessa, Wookjae Maeng, Joong Seek Lee

Abstract:

Application of five implementations of three data mining classification techniques was experimented for extracting important insights from tourism data. The aim was to find out the best performing algorithm among the compared ones for tourism knowledge discovery. Knowledge discovery process from data was used as a process model. 10-fold cross validation method is used for testing purpose. Various data preprocessing activities were performed to get the final dataset for model building. Classification models of the selected algorithms were built with different scenarios on the preprocessed dataset. The outperformed algorithm tourism dataset was Random Forest (76%) before applying information gain based attribute selection and J48 (C4.5) (75%) after selection of top relevant attributes to the class (target) attribute. In terms of time for model building, attribute selection improves the efficiency of all algorithms. Artificial Neural Network (multilayer perceptron) showed the highest improvement (90%). The rules extracted from the decision tree model are presented, which showed intricate, non-trivial knowledge/insight that would otherwise not be discovered by simple statistical analysis with mediocre accuracy of the machine using classification algorithms.

Keywords: classification algorithms, data mining, knowledge discovery, tourism

Procedia PDF Downloads 282

Authors: Zachary Pan

Abstract:

Our general mission is to solve the problem of classifying images into different body part types and deciding if each of them is healthy or not. However, for now, we will determine healthiness for only one-sixth of the body parts, specifically the chest. We will detect pneumonia in X-ray scans of those chest images. With this type of AI, doctors can use it as a second opinion when they are taking CT or X-ray scans of their patients. Another ad-vantage of using this machine learning classifier is that it has no human weaknesses like fatigue. The overall ap-proach to this problem is to split the problem into two parts: first, classify the image, then determine if it is healthy. In order to classify the image into a specific body part class, the body parts dataset must be split into test and training sets. We can then use many models, like neural networks or logistic regression models, and fit them using the training set. Now, using the test set, we can obtain a realistic accuracy the models will have on images in the real world since these testing images have never been seen by the models before. In order to increase this testing accuracy, we can also apply many complex algorithms to the models, like multiplicative weight update. For the second part of the problem, to determine if the body part is healthy, we can have another dataset consisting of healthy and non-healthy images of the specific body part and once again split that into the test and training sets. We then use another neural network to train on those training set images and use the testing set to figure out its accuracy. We will do this process only for the chest images. A major conclusion reached is that convolutional neural networks are the most reliable and accurate at image classification. In classifying the images, the logistic regression model, the neural network, neural networks with multiplicative weight update, neural networks with the black box algorithm, and the convolutional neural network achieved 96.83 percent accuracy, 97.33 percent accuracy, 97.83 percent accuracy, 96.67 percent accuracy, and 98.83 percent accuracy, respectively. On the other hand, the overall accuracy of the model that de-termines if the images are healthy or not is around 78.37 percent accuracy.

Keywords: body part, healthcare, machine learning, neural networks

Procedia PDF Downloads 85

Authors: Ozan Kahraman, Hao Feng

Abstract:

Several models such as Weibull, Modified Gompertz, Biphasic linear, and Log-logistic models have been proposed in order to describe non-linear inactivation kinetics and used to fit non-linear inactivation data of several microorganisms for inactivation by heat, high pressure processing or pulsed electric field. First-order kinetic parameters (D-values and z-values) have often been used in order to identify microbial inactivation by non-thermal processing methods such as ultrasound. Most ultrasonic inactivation studies employed first-order kinetic parameters (D-values and z-values) in order to describe the reduction on microbial survival count. This study was conducted to analyze the E. coli O157:H7 inactivation data by using five microbial survival models (First-order, Weibull, Modified Gompertz, Biphasic linear and Log-logistic). First-order, Weibull, Modified Gompertz, Biphasic linear and Log-logistic kinetic models were used for fitting inactivation curves of Escherichia coli O157:H7. The residual sum of squares and the total sum of squares criteria were used to evaluate the models. The statistical indices of the kinetic models were used to fit inactivation data for E. coli O157:H7 by MTS at three temperatures (40, 50, and 60 0C) and three pressures (100, 200, and 300 kPa). Based on the statistical indices and visual observations, the Weibull and Biphasic models were best fitting of the data for MTS treatment as shown by high R2 values. The non-linear kinetic models, including the Modified Gompertz, First-order, and Log-logistic models did not provide any better fit to data from MTS compared the Weibull and Biphasic models. It was observed that the data found in this study did not follow the first-order kinetics. It is possibly because of the cells which are sensitive to ultrasound treatment were inactivated first, resulting in a fast inactivation period, while those resistant to ultrasound were killed slowly. The Weibull and biphasic models were found as more flexible in order to determine the survival curves of E. coli O157:H7 treated by MTS on apple-carrot juice.

Keywords: Weibull, Biphasic, MTS, kinetic models, E.coli O157:H7

Procedia PDF Downloads 353

Authors: Ehsan Mehryaar, Reza Bushehri

Abstract:

One of the principal parameters defining the clay soil dynamic response is the strain-shear modulus relation. Predicting the strain and, subsequently, shear modulus reduction of the soil is essential for performance analysis of structures exposed to earthquake and dynamic loadings. Many soil properties affect soil’s dynamic behavior. In order to capture those effects, in this study, a database containing 1193 data points consists of maximum shear modulus, strain, moisture content, initial void ratio, plastic limit, liquid limit, initial confining pressure resulting from dynamic laboratory testing of 21 clays is collected for predicting the shear modulus vs. strain curve of soil. A model based on an extreme gradient boosting technique is proposed. A tree-structured parzan estimator hyper-parameter tuning algorithm is utilized simultaneously to find the best hyper-parameters for the model. The performance of the model is compared to the existing empirical equations using the coefficient of correlation and root mean square error.

Keywords: XGBoost, hyper-parameter tuning, soil shear modulus, dynamic response

Procedia PDF Downloads 186

Authors: Yibeltal T. Bayou, Yohana S. Mashalla, Gloria Thupayagale-Tshweneagae

Abstract:

Background: According to the World Health Organization, caesarean section delivery rates of more than 10-15% caesarean section deliveries in any specific geographic region in the world are not justifiable. The aim of the study was to describe the level and analyse determinants of caesarean section delivery in Addis Ababa. Methods: Data was collected in Addis Ababa using a structured questionnaire administered to 901 women aged 15-49 years through a stratified two-stage cluster sampling technique. Binary logistic regression model was employed to identify predictors of caesarean section delivery. Results: Among the 835 women who delivered their last birth at healthcare facilities, 19.2% of them gave birth by caesarean section. About 9.0% of the caesarean section births were due to mother’s request or service provider’s influence without any medical indication. The caesarean section delivery rate was much higher than the recommended rate particularly among the non-slum residents (27.2%); clients of private healthcare facilities (41.1%); currently married women (20.6%); women with secondary (22.2%) and tertiary (33.6%) level of education; and women belonging to the highest wealth quintile household (28.2%). The majority (65.8%) of the caesarean section clients were not informed about the consequences of caesarean section delivery by service providers. The logistic regression model shows that older age (30-49), secondary and above education, non-slum residence, high-risk pregnancy and receiving adequate antenatal care were significantly positively associated with caesarean section delivery. Conclusion: Despite the unreserved effort towards achieving MDG 5 through safe skilled delivery assistance among others, the high caesarean section rate beyond the recommend limit, and the finding that caesarean sections done without medical indications were also alarming. The government and city administration should take appropriate measures before the problems become setbacks in healthcare provision. Further investigations should focus on the effect of caesarean section delivery on maternal and child health outcomes in the study area.

Keywords: Addis Ababa, caesarean section, mode of delivery, slum residence

Procedia PDF Downloads 392

Authors: Afshin Veisi

Abstract:

In this study, the physical properties of wood in poplar wood (Populous sp.) were analyzed in longitudinal and radial directions of the stem. Three Populous Alba tree were cut in chaloos zone and from each tree, 3 discs were selected at 130cm, half of tree and under of crown. The test samples from pith to bark (heartwood to sapwood) were prepared from these discs for measuring the involved properties such as, wet, dry and critical specific gravity, porosity, volume shrinkage and swelling based on the ASTM standard, and data in two radial and longitudinal directions in the trank were statistically analyzed. Such as, variations of wet, dry and critical specific gravity had in radial direction respectively: irregular increase, increase and increase, and in longitudinal direction respectively: irregular decrease, irregular increase and increase. Results of variations to moisture content and porosity show that in radial direction respectively: irregular increasing and decreasing, and in longitudinal direction from down to up respectively: irregular decreasing and stability. Volume shrinkage and swelling variations show in radial direction irregular and in longitudinal axial regular decreasing.

Keywords: poplar wood, physical properties, shrinkage, swelling, critical specific gravity, wet specific gravity, dry specific gravity

Procedia PDF Downloads 269

Authors: Raymond Feng, Shadi Ghiasi

Abstract:

An electroencephalogram (EEG) is a non-invasive technique that records electrical activity in the brain using scalp electrodes. Researchers have studied the use of EEG to detect emotions and moods by collecting signals from participants and analyzing how those signals correlate with their activities. In this study, researchers investigated the relationship between EEG signals and music enjoyment. Participants listened to music while data was collected. During the signal-processing phase, power spectral densities (PSDs) were computed from the signals, and dominant brainwave frequencies were extracted from the PSDs to form a comprehensive feature matrix. A machine learning approach was then taken to find correlations between the processed data and the music enjoyment level indicated by the participants. To improve on previous research, multiple machine learning models were employed, including K-Nearest Neighbors Classifier, Support Vector Classifier, and Decision Tree Classifier. Hyperparameters were used to fine-tune each model to further increase its performance. The experiments showed that a strong correlation exists, with the Decision Tree Classifier with hyperparameters yielding 85% accuracy. This study proves that EEG is a reliable means to detect music enjoyment and has future applications, including personalized music recommendation, mood adjustment, and mental health therapy.

Keywords: EEG, electroencephalogram, machine learning, mood, music enjoyment, physiological signals

Procedia PDF Downloads 40

Authors: Marc Toutain, Pascale Leconte, Antoine Gauthier

Abstract:

Introduction: Eating Disorders (ED), such as anorexia, bulimia, and binge eating, are psychiatric illnesses that mostly affect young people. The main symptoms concern eating (restriction, excessive food intake) and weight control behaviors (laxatives, vomiting). Psychological comorbidities (depression, executive function disorders, etc.) and problematic behaviors toward physical activity (PA) are commonly associated with ED. Acquaintances on ED risk factors are still lacking, and more community sample studies are needed to improve prevention and early detection. To our knowledge, studies are needed to specifically investigate the link between ED risk level, PA, and psychological risk factors in a community sample of adolescents. The aim of this study is to assess the relation between ED risk level, exercise (type, frequency, and motivations for engaging in exercise), and psychological factors based on the Jacobi risk factors model. We suppose that a high risk of ED will be associated with the practice of high caloric cost PA, motivations oriented to weight and shape control, and psychological disturbances. Method: An online survey destined for students has been sent to several middle schools and colleges in northwest France. This survey combined several questionnaires, the Eating Attitude Test-26 assessing ED risk; the Exercise Motivation Inventory–2 assessing motivations toward PA; the Hospital Anxiety and Depression Scale assessing anxiety and depression, the Contour Drawing Rating Scale; and the Body Esteem Scale assessing body dissatisfaction, Rosenberg Self-esteem Scale assessing self-esteem, the Exercise Dependence Scale-Revised assessing PA dependence, the Multidimensional Assessment of Interoceptive Awareness assessing interoceptive awareness and the Frost Multidimensional Perfectionism Scale assessing perfectionism. Machine learning analysis will be performed in order to constitute groups with a tree-based model clustering method, extract risk profile(s) with a bootstrap method comparison, and predict ED risk with a prediction method based on a decision tree-based model. Expected results: 1044 complete records have already been collected, and the survey will be closed at the end of May 2022. Records will be analyzed with a clustering method and a bootstrap method in order to reveal risk profile(s). Furthermore, a predictive tree decision method will be done to extract an accurate predictive model of ED risk. This analysis will confirm typical main risk factors and will give more data on presumed strong risk factors such as exercise motivations and interoceptive deficit. Furthermore, it will enlighten particular risk profiles with a strong level of proof and greatly contribute to improving the early detection of ED and contribute to a better understanding of ED risk factors.

Keywords: eating disorders, risk factors, physical activity, machine learning

Procedia PDF Downloads 72

Authors: Ekele Alih, Enejo Jalija

Abstract:

Female Genital Mutilation (FGM) is made up of three types namely: Clitoridectomy, Excision and Infibulation. In this study, we examine the factors responsible for FGM in order to identify the causality variables in a logistic regression approach. From the result of the survey conducted by the Public Health Division, Nigeria Institute of Medical Research, Yaba, Lagos State, the tau statistic, τ was used to screen 9 factors that causes FGM in order to select few of the predictors before multiple regression equation is obtained. The need for this may be that the sample size may not be able to sustain having a regression with all the predictors or to avoid multi-collinearity. A total of 300 respondents, comprising 150 adult males and 150 adult females were selected for the household survey based on the multi-stage sampling procedure. The tau statistic,

Keywords: female genital mutilation, logistic regression, tau statistic, African society

Procedia PDF Downloads 243

Authors: Lee Jeong Min, Lee Mi Hee, Eo Yang Dam

Abstract:

Geospatial feature extraction is a very important issue in the remote sensing research. In the meantime, the image classification based on statistical techniques, but, in recent years, data mining and machine learning techniques for automated image processing technology is being applied to remote sensing it has focused on improved results generated possibility. In this study, artificial neural network and decision tree technique is applied to classify the high-resolution satellite images, as compared to the MLC processing result is a statistical technique and an analysis of the pros and cons between each of the techniques.

Keywords: remote sensing, artificial neural network, decision tree, maximum likelihood classification

Procedia PDF Downloads 338

Authors: Muhammad Shoaib, Hassan M Al-Swaidan

Abstract:

Saudi Arabia is the major date producer in the world. In order to maximize the production from date tree, pruning of the date trees is required annually. Large amount of this agriculture waste material (palm tree fronds) is available in Saudi Arabia and considered as an ideal source as a precursor for production of activated carbon (AC). The single step procedure for the preparation of micro porous activated carbon (AC) from Saudi date tree fronds using mixture of gases (N2 and CO2) is carried out at carbonization/activation temperature at 850°C and at different ramp rates of 10, 20 and 30 degree per minute. Alloy 330 horizontal reactor is used for tube furnace. Flow rate of nitrogen and carbon dioxide gases are kept at 150 ml/min and 50 ml/min respectively during the preparation. Characterization results reveal that the BET surface area, pore volume, and average pore diameter of the resulting activated carbon generally decreases with the increase in ramp rate. The activated carbon prepared at a ramp rate of 10 degrees/minute attains larger surface area and can offer higher potential to produce activated carbon of greater adsorption capacity from agriculture wastes such as date fronds. The BET surface areas of the activated carbons prepared at a ramp rate of 10, 20 and 30 degree/minute after 30 minutes activation time are 1094, 1020 and 515 m2/g, respectively. Scanning electron microscopy (SEM) for surface morphology, and FTIR for functional groups was carried out that also verified the same trend. Moreover, by increasing the ramp rate from 10 and 20 degrees/min the yield remains same, i.e. 18%, whereas at a ramp rate of 30 degrees/min the yield increases from 18 to 20%. Thus, it is feasible to produce high-quality micro porous activated carbon from date frond agro waste using N2 carbonization followed by physical activation with CO2 and N2 mixture. This micro porous activated carbon can be used as adsorbent of heavy metals from wastewater, NOx SOx emission adsorption from ambient air and electricity generation plants, purification of gases, sewage treatment and many other applications.

Keywords: activated carbon, date tree fronds, agricultural waste, applied chemistry

Procedia PDF Downloads 266

Authors: Maurice Bilioniere, Katie Lanneau

Abstract:

Suddenly appeared in 2011, invasions of sargasso seaweeds Fluitans and Natans are a climatic hazard which causes many problems in the Caribbean. Faced with the growth and frequency of the phenomenon of massive sargasso stranding on their coasts, the French West Indies are moving towards the path of industrial recovery. In this context of innovative projects, we will analyze the necessary requirements for the management and performance of the supply chain, taking into account the observed volatility of the sargasso input. Our prospective approach will consist in studying the theoretical framework of modeling a hybrid supply chain by coupling the discreet event simulation (DES) with a valuation of the process costs according to the "activity-based costing" method (ABC). The PREVA approach (PRocess EVAluation) chosen for our modeling has the advantage of evaluating the financial flows of the logistic process using an analytical model chained with an action model for the evaluation or optimization of physical flows.

Keywords: sargasso, PREVA modeling, supply chain, ABC method, discreet event simulation (DES)

Procedia PDF Downloads 163

Authors: Mohamed El-Shikh

Abstract:

Phaseolus vulgaris L. is the world’s second most important bean after soybeans; used for human food and animal feed. It has generally been linked to reduced risk of cardiovascular disease, diabetes mellitus, obesity, cancer and diseases of digestive tract. The effectiveness of ISSR in achievement of the genetic diversity among 60 common bean accessions; represent various germplasms around the world was investigated. In general, the studied Phaseolus vulgaris accessions were divided into 2 major groups. All of the South-American accessions were separated into the second major group. These accessions may have different genetic features that are distinct from the rest of the accessions clustered in the major group. Asia and Europe accessions (1-20) seem to be more genetically similar (99%) to each other as they clustered in the same sub-group. The American and African varieties showed similarities as well and clustered in the same sub-tree group. In contrast, Asian and American accessions No. 22 and 23 showed a high level of genetic similarities, although these were isolated from different regions. The phylogenetic tree showed that all the Asian accessions (along with Australian No. 59 and 60) were similar except Indian and Yemen accessions No. 9 and 20. Only Netherlands accession No. 3 was different from the rest of European accessions. Morocco accession No. 52 was genetically different from the rest of the African accessions. Canadian accession No. 44 seems to be different from the other North American accessions including Guatemala, Mexico and USA.

Keywords: phylogenetic tree, Phaseolus vulgaris, ISSR technique, genetics

Procedia PDF Downloads 400

Authors: Likewin Thomas, M. V. Manoj Kumar, B. Annappa

Abstract:

Traditional execution process follows the path of execution drawn by the process analyst without observing the behaviour of resource and other real-time constraints. Identifying process model, predicting the behaviour of resource and recommending the optimal path of execution for a real time process is challenging. The proposed AlfyMiner: αyM iner gives a new dimension in process execution with the novel techniques Process Model Analyser: PMAMiner and Resource behaviour Analyser: RBAMiner for recommending the probable path of execution. PMAMiner discovers next probable activity for currently executing activity in an online process using variant matching technique to identify the set of next probable activity, among which the next probable activity is discovered using decision tree model. RBAMiner identifies the resource suitable for performing the discovered next probable activity and observe the behaviour based on; load and performance using polynomial regression model, and waiting time using queueing theory. Based on the observed behaviour αyM iner recommend the probable path of execution with; next probable activity and the best suitable resource for performing it. Experiments were conducted on process logs of CoSeLoG Project1 and 72% of accuracy is obtained in identifying and recommending next probable activity and the efficiency of resource performance was optimised by 59% by decreasing their load.

Keywords: cross-organization process mining, process behaviour, path of execution, polynomial regression model

Procedia PDF Downloads 318

Authors: Shaowei Sun

Abstract:

Let G be a graph with vertex set V(G)={v_1,v_2,...,v_n} and edge set E(G). For any vertex v belong to V(G), let d_v denote the degree of v. The normalized Laplacian matrix of the graph G is the matrix where the non-diagonal (i,j)-th entry is -1/(d_id_j) when vertex i is adjacent to vertex j and 0 when they are not adjacent, and the diagonal (i,i)-th entry is the di. In this paper, we discuss some bounds on the largest and the second smallest normalized Laplacian eigenvalue of trees and graphs. As following, we found some new bounds on the second smallest normalized Laplacian eigenvalue of tree T in terms of graph parameters. Moreover, we use Sage to give some conjectures on the second largest and the third smallest normalized eigenvalues of graph.

Keywords: graph, normalized Laplacian eigenvalues, normalized Laplacian matrix, tree

Procedia PDF Downloads 320

Authors: Mindaye Teshome, Evaldo Muñoz Braz, Carlos M. M. Eleto Torres, Patricia Mattos

Abstract:

Sustainable forest management planning requires up-to-date information on the structure, standing volume, biomass, and growth rate of trees from a given forest. This kind of information is lacking in many forests in Ethiopia. The objective of this study was to quantify the population structure, diameter growth rate, and standing volume of wood from Juniperus procera trees in the Chilimo forest. A total of 163 sample plots were set up in the forest to collect the relevant vegetation data. Growth ring measurements were conducted on stem disc samples collected from 12 J. procera trees. Diameter and height measurements were recorded from a total of 1399 individual trees with dbh ≥ 2 cm. The growth rate, maximum current and mean annual increments, minimum logging diameter, and cutting cycle were estimated, and alternative cutting cycles were established. Using these data, the harvestable volume of wood was projected by alternating four minimum logging diameters and five cutting cycles following the stand table projection method. The results show that J. procera trees have an average density of 183 stems ha⁻¹, a total basal area of 12.1 m² ha⁻¹, and a standing volume of 98.9 m³ ha⁻¹. The mean annual diameter growth ranges between 0.50 and 0.65 cm year⁻¹ with an overall mean of 0.59 cm year⁻¹. The population of J. procera tree followed a reverse J-shape diameter distribution pattern. The maximum current annual increment in volume (CAI) occurred at around 49 years when trees reached 30 cm in diameter. Trees showed the maximum mean annual increment in volume (MAI) around 91 years, with a diameter size of 50 cm. The simulation analysis revealed that 40 cm MLD and a 15-year cutting cycle are the best minimum logging diameter and cutting cycle. This combination showed the largest harvestable volume of wood potential, volume increments, and a 35% recovery of the initially harvested volume. It is concluded that the forest is well stocked and has a large amount of harvestable volume of wood from J. procera trees. This will enable the country to partly meet the national wood demand through domestic wood production. The use of the current population structure and diameter growth data from tree ring analysis enables the exact prediction of the harvestable volume of wood. The developed model supplied an idea about the productivity of the J. procera tree population and enables policymakers to develop specific management criteria for wood harvesting.

Keywords: logging, growth model, cutting cycle, minimum logging diameter

Procedia PDF Downloads 77

Authors: Dieudonne Uwizeye

Abstract:

Introduction: Diarrhoea is one of the major causes of morbidity and mortality among communities living in urban informal settlements of developing countries. It is assumed that mountainous environment introduces variations of the burden among residents of the same settlements. Design and Objective: A cross-sectional study was done in Kigali to explore the effect of mountainous informal settlements on diarrhoea risk variations. Data were collected among 1,152 households through household survey and transect walk to observe the status of sanitation. The outcome variable was the incidence of diarrhoea among household members of any age. The study used the most knowledgeable person in the household as the main respondent. Mostly this was the woman of the house as she was more likely to know the health status of every household member as she plays various roles: mother, wife, and head of the household among others. The analysis used cross tabulation and logistic regression analysis. Results: Results suggest that risks for diarrhoea vary depending on home location in the settlements. Diarrhoea risk increased as the distance from the road increased. The results of the logistic regression analysis indicate the adjusted odds ratio of 2.97 with 95% confidence interval being 1.35-6.55 and 3.50 adjusted odds ratio with 95% confidence interval being 1.61-7.60 in level two and three respectively compared with level one. The status of sanitation within and around homes was also significantly associated with the increase of diarrhoea. Equally, it is indicated that stable households were less likely to have diarrhoea. The logistic regression analysis indicated the adjusted odds ratio of 0.45 with 95% confidence interval being 0.25-0.81. However, the study did not find evidence for a significant association between diarrhoea risks and household socioeconomic status in the multivariable model. It is assumed that environmental factors in mountainous settings prevailed. Households using the available public water sources were more likely to have diarrhoea in their households. Recommendation: The study recommends the provision and extension of infrastructure for improved water, drainage, sanitation and wastes management facilities. Equally, studies should be done to identify the level of contamination and potential origin of contaminants for water sources in the valleys to adequately control the risks for diarrhoea in mountainous urban settings.

Keywords: urbanisation, diarrhoea risk, mountainous environment, urban informal settlements in Rwanda

Procedia PDF Downloads 158

Authors: Dhanuj M. Gandikota

Abstract:

Sensor-derived three-dimensional (3D) point clouds of trees are invaluable in remote sensing analysis for the accurate measurement of key structural metrics, bio-inventory values, spatial planning/visualization, and ecological modeling. Machine learning (ML) holds the potential in addressing the restrictive tradeoffs in cost, spatial coverage, resolution, and information gain that exist in current point cloud sensing methods. Terrestrial laser scanning (TLS) remains the highest fidelity source of both canopy and below-canopy structural features, but usage is limited in both coverage and cost, requiring manual deployment to map out large, forested areas. While aerial laser scanning (ALS) remains a reliable avenue of LIDAR active remote sensing, ALS is also cost-restrictive in deployment methods. Space-borne photogrammetry from high-resolution satellite constellations is an avenue of passive remote sensing with promising viability in research for the accurate construction of vegetation 3-D point clouds. It provides both the lowest comparative cost and the largest spatial coverage across remote sensing methods. However, both space-borne photogrammetry and ALS demonstrate technical limitations in the capture of valuable below-canopy point cloud data. Looking to minimize these tradeoffs, we explored a class of powerful ML algorithms called Deep Learning (DL) that show promise in recent research on 3-D point cloud reconstruction and interpolation. Our research details the efficacy of applying these DL techniques to reconstruct accurate below-canopy point clouds from space-borne and aerial remote sensing through learned patterns of tree species fractal symmetry properties and the supplementation of locally sourced bio-inventory metrics. From our dataset, consisting of tree point clouds obtained from TLS, we deconstructed the point clouds of each tree into those that would be obtained through ALS and satellite photogrammetry of varying resolutions. We fed this ALS/satellite point cloud dataset, along with the simulated local bio-inventory metrics, into the DL point cloud reconstruction architectures to generate the full 3-D tree point clouds (the truth values are denoted by the full TLS tree point clouds containing the below-canopy information). Point cloud reconstruction accuracy was validated both through the measurement of error from the original TLS point clouds as well as the error of extraction of key structural metrics, such as crown base height, diameter above root crown, and leaf/wood volume. The results of this research additionally demonstrate the supplemental performance gain of using minimum locally sourced bio-inventory metric information as an input in ML systems to reach specified accuracy thresholds of tree point cloud reconstruction. This research provides insight into methods for the rapid, cost-effective, and accurate construction of below-canopy tree 3-D point clouds, as well as the supported potential of ML and DL to learn complex, unmodeled patterns of fractal tree growth symmetry.

Keywords: deep learning, machine learning, satellite, photogrammetry, aerial laser scanning, terrestrial laser scanning, point cloud, fractal symmetry

Procedia PDF Downloads 88

Authors: Jay L. Fu

Abstract:

Patients with Alzheimer's disease progressively lose their memory and thinking skills and, eventually, the ability to carry out simple daily tasks. The disease is irreversible, but early detection and treatment can slow down the disease progression. In this research, publicly available MRI data and demographic data from 373 MRI imaging sessions were utilized to build models to predict dementia. Various machine learning models, including logistic regression, k-nearest neighbor, support vector machine, random forest, and neural network, were developed. Data were divided into training and testing sets, where training sets were used to build the predictive model, and testing sets were used to assess the accuracy of prediction. Key risk factors were identified, and various models were compared to come forward with the best prediction model. Among these models, the random forest model appeared to be the best model with an accuracy of 90.34%. MMSE, nWBV, and gender were the three most important contributing factors to the detection of Alzheimer’s. Among all the models used, the percent in which at least 4 of the 5 models shared the same diagnosis for a testing input was 90.42%. These machine learning models allow early detection of Alzheimer’s with good accuracy, which ultimately leads to early treatment of these patients.

Keywords: Alzheimer's disease, clinical diagnosis, magnetic resonance imaging, machine learning prediction

Procedia PDF Downloads 132

Authors: Alina P. Colling, Robert G. Hekkenberg

Abstract:

Recent years have seen extensive technological advancement in truck platooning, as reflected in the literature. Its main benefits are the improvement of traffic stability and the reduction of air drag, resulting in less fuel consumption, in comparison to using individual trucks. Platooning is now being adapted to the waterborne transport sector in the NOVIMAR project through the development of a Vessel Train (VT) concept. The main focus of VT’s, as opposed to the truck platoons, is the decrease in manning on board, ultimately working towards autonomous vessel operations. This crew reduction can prove to be an important selling point in achieving economic competitiveness of the waterborne approach when compared to alternative modes of transport. This paper discusses the expected benefits and drawbacks of the VT concept, in terms of the technical logistic performance and generalized costs. More specifically, VT’s can provide flexibility in destination choices for shippers but also add complexity when performing special manoeuvres in VT formation. In order to quantify the cost and performances, a model is developed and simulations are carried out for various case studies. These compare the application of VT’s in the short sea and inland water transport, with specific sailing regimes and technologies installed on board to allow different levels of autonomy. The results enable the identification of the most important boundary conditions for the successful operation of the waterborne platooning concept. These findings serve as a framework for future business applications of the VT.

Keywords: autonomous vessels, NOVIMAR, vessel trains, waterborne platooning

Procedia PDF Downloads 207

Authors: Muhammad Zaheer Awan, Adnan Qadir, Zeeshan Anjum

Abstract:

Housefly, Musca domestica Linnaeus is ubiquitous and hazardous for Homo sapiens and livestock in sundry venerations. Musca domestica cart 100 different pathogens, such as typhoid, salmonella, bacillary dysentery, tuberculosis, anthrax and parasitic worms. The flies in rural areas usually carry more pathogens. Houseflies feed on liquid or semi-liquid substances besides solid materials which are softened by saliva. Neem botanically known as Azadirachta indica belongs to the family Meliaceae and is an indigenous tree to Pakistan. The neem tree is also one such tree which has been revered by the Pakistanis and Kashmiris for its medicinal properties. Present study showed neem seed extract has potentially toxic ability that affect Total Haemocyte Count (THC) and Differential Haemocytes Count (DHC) in insect’s blood cells, of the housefly. A significant variation in haemolymph density was observed just after application, 30 minutes and 60 minutes post treatment in term of THC and DHC in comparison with novastar. The study strappingly acclaim use of neem seed extract as insecticide as compare to artificial insecticides.

Keywords: neem, Azadirachta indica, Musca domestica, differential haemocyte count (DHC), total haemocytes count (DHC), novastar

Procedia PDF Downloads 192

Authors: Ghada A. Alfattni

Abstract:

Analysing unbalanced datasets is one of the challenges that practitioners in machine learning field face. However, many researches have been carried out to determine the effectiveness of the use of the synthetic minority over-sampling technique (SMOTE) to address this issue. The aim of this study was therefore to compare the effectiveness of the SMOTE over different models on unbalanced datasets. Three classification models (Logistic Regression, Support Vector Machine and Nearest Neighbour) were tested with multiple datasets, then the same datasets were oversampled by using SMOTE and applied again to the three models to compare the differences in the performances. Results of experiments show that the highest number of nearest neighbours gives lower values of error rates. 

Keywords: imbalanced datasets, SMOTE, machine learning, logistic regression, support vector machine, nearest neighbour

Procedia PDF Downloads 331

Authors: Benchamat Laksaniyanon, Padcharee Phasuk, Rungtawan Boonphanakan

Abstract:

With globalization, organizations had to adjust to an unstable environment in order to survive in a competitive arena. Typically within the field of management, different types of organizations include mechanistic, bureaucratic and organic ones. In fact, bureaucratic and mechanistic organizations have some characteristics in common. Bureaucracy is one type of Thailand organization which adapted from mechanistic concept to develop an organization that is suitable for the characteristic and culture of Thailand. The objective of this study is to compare the adjustment strategies of both organizations in order to find key performance indicators (KPI) suitable for improving organization in Thailand. The methodology employed is binary logistic regression. The results of this study will be valuable for developing future management strategies for both bureaucratic and mechanistic organizations.

Keywords: mechanistic, bureaucratic and organic organization, binary logistic regression, key performance indicators (KPI)

Procedia PDF Downloads 344

Authors: Arlene López-Sampson, Tony Page, Betsy Jackes

Abstract:

Aquilaria genus produces a highly valuable fragrant oleoresin known as agarwood. Agarwood forms in a few trees in the wild as a response to injure or pathogen attack. The resin is used in perfume and incense industry and medicine. Cultivation of Aquilaria species as a sustainable source of the resin is now a common strategy. Physiological traits are frequently used as a proxy of crop and tree productivity. Aquilaria species growing in Queensland, Australia were studied to investigate relationship between leaf-productivity traits with tree growth. Specifically, 28 trees, representing 12 plus trees and 16 trees from yield plots, were selected to conduct carbon isotope analysis (δ13C) and monitor six leaf attributes. Trees were grouped on four diametric classes (diameter at 150 mm above ground level) ensuring the variability in growth of the whole population was sampled. Model averaging technique based on the Akaike’s information criterion (AIC) was computed to identify whether leaf traits could assist in diameter prediction. Carbon isotope values were correlated with height classes and leaf traits to determine any relationship. In average four leaves per shoot were recorded. Approximately one new leaf per week is produced by a shoot. Rate of leaf expansion was estimated in 1.45 mm day-1. There were no statistical differences between diametric classes and leaf expansion rate and number of new leaves per week (p > 0.05). Range of δ13C values in leaves of Aquilaria species was from -25.5 ‰ to -31 ‰ with an average of -28.4 ‰ (± 1.5 ‰). Only 39% of the variability in height can be explained by δ13C in leaf. Leaf δ13C and nitrogen content values were positively correlated. This relationship implies that leaves with higher photosynthetic capacities also had lower intercellular carbon dioxide concentrations (ci/ca) and less depleted values of 13C. Most of the predictor variables have a weak correlation with diameter (D). However, analysis of the 95% confidence of best-ranked regression models indicated that the predictors that could likely explain growth in Aquilaria species are petiole length (PeLen), values of δ13C (true13C) and δ15N (true15N), leaf area (LA), specific leaf area (SLA) and number of new leaf produced per week (NL.week). The model constructed with PeLen, true13C, true15N, LA, SLA and NL.week could explain 45% (R2 0.4573) of the variability in D. The leaf traits studied gave a better understanding of the leaf attributes that could assist in the selection of high-productivity trees in Aquilaria.

Keywords: 13C, petiole length, specific leaf area, tree growth

Procedia PDF Downloads 494

Authors: Yanto Santosa, Dede Aulia Rahman, Cory Wulan, Abdul Haris Mustari

Abstract:

The global demand for long-tailed macaques for medical experimentation has continued to increase. Fulfillment of Indonesian export demands has been mostly from natural habitats, based on a harvesting quota. This quota has been determined according to the total catch for a given year, and not based on consideration of any demographic parameters or physical environmental factors with regard to the animal; hence threatening the sustainability of the various populations. It is therefore necessary to formulate a method for calculating a sustainable harvesting quota, based on population parameters in natural habitats. Considering the possibility of variations in habitat characteristics and population parameters, a time series observation of demographic and physical/biotic parameters, in various habitats, was performed on 13 groups of long-tailed macaques, distributed throughout the West Java, Lampung and Yogyakarta areas of Indonesia. These provinces were selected for comparison of the influence of human/tourism activities. Data on population parameters that was collected included data on life expectancy according to age class, numbers of individuals by sex and age class, and ‘ratio of infants to reproductive females’. The estimation of population growth was based on a population dynamic growth model: the Leslie matrix. The harvesting quota was calculated as being the difference between the actual population size and the MVP (minimum viable population) for each sex and age class. Observation indicated that there were variations within group size (24 – 106 individuals), gender (sex) ratio (1:1 to 1:1.3), life expectancy value (0.30 to 0.93), and ‘ratio of infants to reproductive females’ (0.23 to 1.56). Results of subsequent calculations showed that sustainable harvesting quotas for each studied group of long-tailed macaques, ranged from 29 to 110 individuals. An estimation model of the MVP for each age class was formulated as Log Y = 0.315 + 0.884 Log Ni (number of individual on ith age class). This study also found that life expectancy for the juvenile age class was affected by the humidity under tree stands, and dietary plants’ density at sapling, pole and tree stages (equation: Y= 2.296 – 1.535 RH + 0.002 Kpcg – 0.002 Ktg – 0.001 Kphn, R2 = 89.6% with a significance value of 0.001). By contrast, for the sub-adult-adult age class, life expectancy was significantly affected by slope (equation: Y=0.377 = 0.012 Kml, R2 = 50.4%, with significance level of 0.007). The infant to reproductive female ratio was affected by humidity under tree stands, and dietary plant density at sapling and pole stages (equation: Y = -1.432 + 2.172 RH – 0.004 Kpcg + 0.003 Ktg, R2 = 82.0% with significance level of 0.001). This research confirmed the importance of population parameters in determining the minimum viable population, and that MVP varied according to habitat characteristics (especially food availability). It would be difficult therefore, to formulate a general mathematical equation model for determining a harvesting quota for the species as a whole.

Keywords: harvesting, long-tailed macaque, population, quota

Procedia PDF Downloads 415