Search results for: oil palm tree census

Authors: Chih-Fong Tsai, Ya-Han Hu

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The aim of feature selection (or dimensionality reduction) is to filter out unrepresentative features (or variables) making the classifier perform better than the one without feature selection. Since there are many well-known feature selection algorithms, and different classifiers based on different selection results may perform differently, very few studies consider examining the effect of performing different feature selection algorithms on the classification performances by different classifiers over different types of datasets. In this paper, two widely used algorithms, which are the genetic algorithm (GA) and information gain (IG), are used to perform feature selection. On the other hand, three well-known classifiers are constructed, which are the CART decision tree (DT), multi-layer perceptron (MLP) neural network, and support vector machine (SVM). Based on 14 different types of datasets, the experimental results show that in most cases IG is a better feature selection algorithm than GA. In addition, the combinations of IG with DT and IG with SVM perform best and second best for small and large scale datasets.

Keywords: data mining, feature selection, pattern classification, dimensionality reduction

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Authors: Muhammad Adeel Arshad, Faiz-Ul Hassan, Yanfen Cheng

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According to FAO, enteric methane (CH4) production is about 44% of all greenhouse gas emissions from the livestock sector. Ruminants produce CH4 as a result of fermentation of feed in the rumen especially from roughages which yield more CH4 per unit of biomass ingested as compared to concentrates. Efficient ruminal fermentation is not possible without abating CO2 and CH4. Methane abatement strategies are required to curb the predicted rise in emissions associated with greater ruminant production in future to meet ever increasing animal protein requirements. Ecology of ruminal methanogenesis and avenues for its mitigation can be identified through various genomic and transcriptomic techniques. Programs such as Hungate1000 and the Global Rumen Census have been launched to enhance our understanding about global ruminal microbial communities. Through Hungate1000 project, a comprehensive reference set of rumen microbial genome sequences has been developed from cultivated rumen bacteria and methanogenic archaea along with representative rumen anaerobic fungi and ciliate protozoa cultures. But still many species of rumen microbes are underrepresented especially uncultivable microbes. Lack of sequence information specific to the rumen's microbial community has inhibited efforts to use genomic data to identify specific set of species and their target genes involved in methanogenesis. Metagenomic and metatranscriptomic study of entire microbial rumen populations offer new perspectives to understand interaction of methanogens with other rumen microbes and their potential association with total gas and methane production. Deep understanding of methanogenic pathway will help to devise potentially effective strategies to abate methane production while increasing feed efficiency in ruminants.

Keywords: Genome sequences, Hungate1000, methanogens, ruminal fermentation

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Authors: Nguyen J. M., Lucas G., Ruan S., Digonnet H., Antonioli D.

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Introduction: Deep Learning (DL) is a very powerful tool for analyzing image data. But for tabular data, it cannot compete with machine learning methods like XGBoost. The research question becomes: can tabular data be transformed into images that can be analyzed by simple CNNs (Convolutional Neuron Networks)? Will DL be the absolute tool for data classification? All current solutions consist in repositioning the variables in a 2x2 matrix using their correlation proximity. In doing so, it obtains an image whose pixels are the variables. We implement a technology, DeepNIC, that offers the possibility of obtaining an image for each variable, which can be analyzed by simple CNNs. Material and method: The 'ROP' (Regression OPtimized) model is a binary and atypical decision tree whose nodes are managed by a new artificial neuron, the Neurop. By positioning an artificial neuron in each node of the decision trees, it is possible to make an adjustment on a theoretically infinite number of variables at each node. From this new decision tree whose nodes are artificial neurons, we created the concept of a 'Random Forest of Perfect Trees' (RFPT), which disobeys Breiman's concepts by assembling very large numbers of small trees with no classification errors. From the results of the RFPT, we developed a family of 10 statistical information criteria, Nguyen Information Criterion (NICs), which evaluates in 3 dimensions the predictive quality of a variable: Performance, Complexity and Multiplicity of solution. A NIC is a probability that can be transformed into a grey level. The value of a NIC depends essentially on 2 super parameters used in Neurops. By varying these 2 super parameters, we obtain a 2x2 matrix of probabilities for each NIC. We can combine these 10 NICs with the functions AND, OR, and XOR. The total number of combinations is greater than 100,000. In total, we obtain for each variable an image of at least 1166x1167 pixels. The intensity of the pixels is proportional to the probability of the associated NIC. The color depends on the associated NIC. This image actually contains considerable information about the ability of the variable to make the prediction of Y, depending on the presence or absence of other variables. A basic CNNs model was trained for supervised classification. Results: The first results are impressive. Using the GSE22513 public data (Omic data set of markers of Taxane Sensitivity in Breast Cancer), DEEPNic outperformed other statistical methods, including XGBoost. We still need to generalize the comparison on several databases. Conclusion: The ability to transform any tabular variable into an image offers the possibility of merging image and tabular information in the same format. This opens up great perspectives in the analysis of metadata.

Keywords: tabular data, CNNs, NICs, DeepNICs, random forest of perfect trees, classification

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Authors: A. Benatiallah, D. Benatiallah, F. Abaidi, B. Nasri, A. Harrouz, S. Mansouri

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The region of Adrar is located in the south-western Algeria and covers a total area of 443.782 km², occupied by a population of 432,193 inhabitants. The main activity of population is agriculture, mainly based on the date palm cultivation occupies a total area of 23,532 ha. Adrar region climate is a continental desert characterized by a high variation in temperature between months (July, August) it exceeds 48°C and coldest months (December, January) with 16°C. Rainfall is very limited in frequency and volume with an aridity index of 4.6 to 5 which corresponds to a type of arid climate. Geologically Adrar region is located on the edge North West and is characterized by a Precambrian basement cover stolen sedimentary deposit of Phanerozoic age transgressive. The depression is filled by Touat site Paleozoic deposits (Cambrian to Namurian) of a vast sedimentary basin extending secondary age of the Saharan Atlas to the north hamada Tinhirt Tademaït and the plateau of south and Touat Gourara west to Gulf of Gabes in the Northeast. In this work we have study geothermal potential of Adrar region from the borehole data eatable in various sites across the area of 400,000 square kilometres; from these data we developed a GIS (Adrar_GIS) that plots data on the various points and boreholes in the region specifying information on available geothermal potential has variable depths.

Keywords: sig, geothermal, potenteil, temperature

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Authors: Fátima Aparecida de Carvalho, Ester Eliane Jeunon

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This study addresses issues in the public service: motivation to work and organizational commitment. The goal of this research was to examine how it configures the motivation to work and organizational commitment of the technical and administrative effective staff of the School of Engineering at UFMG. For this purpose a descriptive research under a quantitative and qualitative approach has been performed. In the quantitative research it has been applied a questionnaire to all 146 technical and administrative institution effective staff, that configures a census research. This questionnaire was divided into three parts, the first one aimed at performing a socio-demographic survey of participants, the second one aimed to measure motivation and the third one aimed at measuring organizational commitment. The Bases Organizational Commitment Scale (EBACO) was used in the analysis of data obtained in the third part of the questionnaire. The qualitative research was conducted through interviews with 08 managers, with open-ended questions structured in an analysis category, thus contemplating the administrative structure of the School of Engineering. The results of the research revealed that there is no relevant difference between the hygiene and motivational indices, related to the staff´s gender and area of work. Nonetheless, it was observed higher motivational indices for staff with shorter duration of employment in the institution. Also, the results shown high organizational commitment of the staff with the institution, with a predominance of the component “Requirement for performance”, followed by commitments “Consistent line of activity”, “Affiliative” and “Affective”, which reached almost tge some average in this study. Finally the results showed that all commitment indices have positive moderated correlation to the motivational indices, except the “shortage of alternative” index.

Keywords: motivation to work, organizational commitment, public service, human resources

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Authors: Seyed Abolhasan Naeini, Hamidreza Rahmani

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Laboratory consolidated undrained triaxial (CU) tests were carried out to study the strength behavior of silty soil reinforced with randomly plastic waste bottle chips. Specimens mixed with plastic waste chips in triaxial compression tests with 0.25, 0.50, 0.75, 1.0, and 1.25% by dry weight of soil and tree different length including 4, 8, and 12 mm. In all of the samples, the width and thickness of plastic chips were kept constant. According to the results, the amount and size of plastic waste bottle chips played an important role in the increasing of the strength parameters of reinforced silt compared to the pure soil. Because of good results, the suggested method of soil improvement can be used in many engineering problems such as increasing the bearing capacity and settlement reduction in foundations.

Keywords: reinforcement, silt, soil improvement, triaxial test, waste bottle chips

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Authors: Rafiu O. Sanni, Abayomi O. Olajuyigbe, Nelson R. Osungbemiro, Rotimi F. Olaniyan

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The Owena River lies within the drainage basins of the Oni, Siluko, and Ogbesse rivers. The river’s immediate surroundings are covered by dense forests, interspersed by plantations of cocoa, oil palm, kolanut, bananas, and other crops. The objectives were to identify the aquatic plants community, comprising the algae and aquatic macrophytes, observe their population dynamics in relation to the two seasons and identify their economic importance, especially to the neighbouring community. The study sites were determined using a stratified sampling method. Three strata were marked out for sampling namely strata I (upstream)–5 stations, strata II (reservoir) –2 stations, and strata III (outflow) 2 stations. These nine stations were tagged st1, st2, st3…st9. The aquatic macrophytes were collected using standard methods and identified at the University of Ibadan herbarium while the algal samples were collected using standard methods for microalgae. The periphytonic species were scraped from surfaces of rocks (perilithic), sucked with large syringe from mud (epipellic), scraped from suspended logs, washed from roots of aquatic angiosperms (epiphytic), as well as shaken from other particles such as suspended plant parts. Some were collected physically by scooping floating thallus of non-microscopic multicellular forms. The specimens were taken to the laboratory and observed under a microscope with mounted digital camera for photomicrography. Identification was done using Prescott.

Keywords: aquatic plants, aquatic macrophytes, algae, Owena river

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Authors: Asharaf Abdul Salam

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Arabian Gulf countries have norms and rules different from others: having an exodus of male immigrant labor contract holders of age 20-60 years as a floating population. Such a demographic scenario camouflages population ageing. However, it is observed on examining vigilantly, not only in the native population but also in the general population. This research on population ageing in the Arabian Gulf examines ageing scenario and concerns through analyses of international databases (US Census Bureau and United Nations) and national level databases (Censuses and Surveys) apart from a review of published research. Transitions in demography and epidemiology lead to gains in life expectancy and thereby reductions in fertility, leading to ageing of the population in the region. Even after bringing adult immigrants, indices and age pyramids show an increasing ageing trend in the total population, demonstrating an ageing workforce. Besides, the exclusive native population analysis reveals a trend of expansive pyramids (pre-transitional stage) turning to constrictive (transition stage) and cylindrical (post-transition stage) shapes. Age-based indices such as the index of ageing, age dependency ratio, and median age confirm this trend. While the feminine nature of ageing is vivid, gains in life expectancy and causes of death in old age, indicating co-morbidity compression, are concerns to ageing. Preparations are in demand to cope with ageing from different dimensions, as explained in the United Nations Plans of Action. A strategy of strengthening informal care with supportive semi-formal and supplementary formal care networks would alleviate this crisis associated with population ageing.

Keywords: total versus native population, indices of ageing, age pyramids, feminine nature, comorbidity compression, strategic interventions

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Authors: Maíra O. Palm, Cintia Marangoni, Ozair Souza, Noeli Sellin

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Wastes from a vegetal extracts industry (cocoa, oak, Guarana and mate) were characterized by particle size, proximate and ultimate analysis, lignocellulosic fractions, high heating value, thermal analysis (Thermogravimetric analysis – TGA, and Differential thermal analysis - DTA) and energy density to evaluate their potential as biomass in the form of briquettes for power generation. All wastes presented adequate particle sizes to briquettes production. The wastes showed high moisture content, requiring previous drying for use as briquettes. Cocoa and oak wastes had the highest volatile matter contents with maximum mass loss at 310 ºC and 450 ºC, respectively. The solvents used in the aroma extraction process influenced in the moisture content of the wastes, which was higher for mate due to water has been used as solvent. All wastes showed an insignificant loss mass after 565 °C, hence resulting in low ash content. High carbon and hydrogen contents and low sulfur and nitrogen contents were observed ensuring a low generation of sulfur and nitrous oxides. Mate and cocoa exhibited the highest carbon and lignin content, and high heating value. The dried wastes had high heating value, from 17.1 MJ/kg to 20.8 MJ/kg. The results indicate the energy potential of wastes for use as fuel in power generation.

Keywords: agro-industrial waste, biomass, briquettes, combustion

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Authors: Langa Hendrick Musawenkosi, Twala Bhekisipho

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The success or failure of students is a concern for every academic institution, college, university, governments and students themselves. Several approaches have been researched to address this concern. In this paper, a view is held that when a student enters a university or college or an academic institution, he or she enters an academic environment. The academic environment is unique concept used to develop the solution for making predictions effectively. This paper presents a model to determine the propensity of a student to succeed or fail in the French South African Schneider Electric Education Center (FSASEC) at the Vaal University of Technology (VUT). The Decision Tree algorithm is used to implement the model at FSASEC.

Keywords: FSASEC, academic environment model, decision trees, k-nearest neighbor, machine learning, popularity index, support vector machine

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Authors: Ijaz Ahmed, Anwar Zada, Muhammad Waqas, M. U. Ashraf

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We demonstrate the performance of a very efficient tagger applies on hadronically decaying top quark pairs as signal based on deep neural network algorithms and compares with the QCD multi-jet background events. A significant enhancement of performance in boosted top quark events is observed with our limited computing resources. We also compare modern machine learning approaches and perform a multivariate analysis of boosted top-pair as well as single top quark production through weak interaction at √s = 14 TeV proton-proton Collider. The most relevant known background processes are incorporated. Through the techniques of Boosted Decision Tree (BDT), likelihood and Multlayer Perceptron (MLP) the analysis is trained to observe the performance in comparison with the conventional cut based and count approach

Keywords: top tagger, multivariate, deep learning, LHC, single top

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Authors: Catherine K. Derow

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Possibly a slime mold somehow mutated or already was mutated at progeniture and so stayed as a metazoan when it developed into the fruiting stage and so the slime mold(s) we are evolved and similar to are genetically differ from the slime molds in existence now. This may be why there are genetic links between humans and other metazoa now alive and slime molds now alive but we are now divergent branches of the evolutionary tree compared to the original slime mold, or perhaps slime mold-like organisms, that gave rise to metazoan animalia and perhaps algae and plantae as slime molds were undifferentiated enough in many ways that could allow their descendants to evolve into these three separate phylogenetic categories. Or it may be a slime mold was born or somehow progenated as multicellular, as the particular organism was mutated enough to have say divided in a a 'pseudo-embryonic' stage, and this could have happened for algae, plantae as well as animalia or all the branches may be from the same line but the missing link might be covered in 'phylogenetic sequence comparison noise'.

Keywords: metazoan evolution, unicellular bridge to metazoans, evolution, slime mold

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Authors: Igor V. Grigoriev

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Understanding of biological processes encoded in fungi is instrumental in addressing future food, feed, and energy demands of the growing human population. Genomics is a powerful and quickly evolving tool to understand these processes. The Fungal Genomics Program of the US Department of Energy Joint Genome Institute (JGI) partners with researchers around the world to explore fungi in several large scale genomics projects, changing the fungal genomics landscape. The key trends of these changes include: (i) rapidly increasing scale of sequencing and analysis, (ii) developing approaches to go beyond culturable fungi and explore fungal ‘dark matter,’ or unculturables, and (iii) functional genomics and multi-omics data integration. Power of comparative genomics has been recently demonstrated in several JGI projects targeting mycorrhizae, plant pathogens, wood decay fungi, and sugar fermenting yeasts. The largest JGI project ‘1000 Fungal Genomes’ aims at exploring the diversity across the Fungal Tree of Life in order to better understand fungal evolution and to build a catalogue of genes, enzymes, and pathways for biotechnological applications. At this point, at least 65% of over 700 known families have one or more reference genomes sequenced, enabling metagenomics studies of microbial communities and their interactions with plants. For many of the remaining families no representative species are available from culture collections. To sequence genomes of unculturable fungi two approaches have been developed: (a) sequencing DNA from fruiting bodies of ‘macro’ and (b) single cell genomics using fungal spores. The latter has been tested using zoospores from the early diverging fungi and resulted in several near-complete genomes from underexplored branches of the Fungal Tree, including the first genomes of Zoopagomycotina. Genome sequence serves as a reference for transcriptomics studies, the first step towards functional genomics. In the JGI fungal mini-ENCODE project transcriptomes of the model fungus Neurospora crassa grown on a spectrum of carbon sources have been collected to build regulatory gene networks. Epigenomics is another tool to understand gene regulation and recently introduced single molecule sequencing platforms not only provide better genome assemblies but can also detect DNA modifications. For example, 6mC methylome was surveyed across many diverse fungi and the highest among Eukaryota levels of 6mC methylation has been reported. Finally, data production at such scale requires data integration to enable efficient data analysis. Over 700 fungal genomes and other -omes have been integrated in JGI MycoCosm portal and equipped with comparative genomics tools to enable researchers addressing a broad spectrum of biological questions and applications for bioenergy and biotechnology.

Keywords: fungal genomics, single cell genomics, DNA methylation, comparative genomics

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Authors: Barsharani Maharana

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India is the country that shelters the largest number of poor. One of the major areas of concern in India is the unsatisfactory situation of the poor in social developmental and health parameters, not only in rural areas which are partly devoid of the facilities but also in the urban areas where the facilities are insufficient to provide services of a satisfactory quality. Objectives: 1) to examine the association between the environmental condition and health condition among poor women in urban areas. 2) to find out the significance of the effect of environment on the child health among the poor children. 3) to present the scenario of poor among highly urbanized and less urbanized states with respect to the health and environment. Data: data from National Family Health survey-3 and census are used to fulfill the objectives. Methodology: In this study, the standard of living condition of people living in urban areas is computed by taking some household characteristics and assets. People possessing low standard of living are considered as poor. Bivariate and multivariate analysis are employed to examine the effect of environment on poor women and children. A geographical information system is used to present the health and environmental condition of poor in highly and less urbanized states. Results: The findings reveal that the poor women who are not accessed to improved source of water, and sanitation facility are facing more health problems. Children who are living in a dirty environment and are not accessed to improved source of drinking water, among them prevalence of diarrhea and fever is found to be high. As well, the health condition of poor in highly urbanized states is dreadful. Policy implications: Government should emphasize on the implementation of programs regarding the improvement in the infrastructural facilities and health care treatment of urban poor.

Keywords: environment, urban poor, health, sanitation

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Authors: Amine Alaoui Sanae, Tagjdid Reda, Loutfi Chafiqa, Melloul Merouane, Laloui Aziz, Touil Nadia, El Fahim, E. Mostafa

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Background: Rotavirus group A (RVA) strains with G8P[14] specificities are usually detected in calves and goats. However, these strains have been reported globally in humans and have often been characterized as originating from zoonotic transmissions, particularly in area where ruminants and humans live side-by-side. Whether human P[14] genotypes are two-way and can be transmitted to animal species remains to be established. Here we describe VP4 deduced amino-acid relationships of three Moroccan P[14] genotypes originating from different species and the receptiveness of an alpine goat to a human G8P[14] through an experimental infection. Material/methods: the human MA31 RVA strain was originally identified in a four years old girl presenting an acute gastroenteritis hospitalized at the pediatric care unit in Rabat Hospital in 2011. The virus was isolated and propagated in MA104 cells in the presence of trypsin. Ch_10S and 8045_S animal RVA strains were identified in fecal samples of a 2-week-old native goat and 3-week-old calf with diarrhea in 2011 in Bouaarfa and My Bousselham respectively. Genomic RNAs of all strains were subjected to a two-step RT-PCR and sequenced using the consensus primers VP4. The phylogenetic tree for MA31, Ch_10S and 8045_S VP4 and a set of published P[14] genotypes was constructed using MEGA6 software. The receptivity of MA31 strain by an eight month-old alpine goat was assayed. The animal was orally and intraperitonally inoculated with a dose of 8.5 TCID50 of virus stock at passage level 3. The shedding of the virus was tested by a real time RT-PCR assay. Results: The phylogenetic tree showed that the three Moroccan strains MA31, Ch_10S and 8045_S VP4 were highly related to each other (100% similar at the nucleotide level). They were clustered together with the B10925, Sp813, PA77 and P169 strains isolated in Belgium, Spain and Italy respectively. The Belgian strain B10925 was the most closely related to the Moroccan strains. In contrast, the 8045_S and Ch_10S strains were clustered distantly from the Tunisian calf strain B137 and the goat strain cap455 isolated in South Africa respectively. The human MA31 RVA strain was able to induce bloody diarrhea at 2 days post infection (dpi) in the alpine goat kid. RVA virus shedding started by 2 dpi (Ct value of 28) and continued until 5 dpi (Ct value of 25) with a concomitant elevation in the body temperature. Conclusions: Our study while limited to one animal, is the first study proving experimentally that a human P[14] genotype causes diarrhea and virus shedding in the goat. This result reinforce the potential role of inter- species transmission in generating novel and rare rotavirus strains such G8P[14] which infect humans.

Keywords: interspecies transmission, rotavirus, goat, human

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Authors: Shweta Dhindhwal

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Dopamine is an essential neurotransmitter in the central nervous system of all vertebrates and plays an important role in many processes such as motor function, learning and behavior, and sensory activity. One of the important functions of dopamine is release of pituitary hormones. It is synthesized from the amino acid tyrosine. Two types of dopamine receptors, D1-like and D2-like, have been reported in fish. The dopamine containing neurons are located in the olfactory bulbs, the ventral regions of the pre-optic area and tuberal hypothalamus. Distribution of the dopaminergic system has not been studied in the murrel, Channa punctatus. The present study deals with identification of D2 receptors in the brain of murrel. A phylogenetic tree has been constructed using partial sequence of D2 receptor. Distribution of putative dopaminergic neurons in the brain has been investigated. Also, formalin induced hypertrophy of neurosecretory cells in murrel has been studied.

Keywords: dopamine, fish, pre-optic area, murrel

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Authors: Carlos Roncero-Parra, Alfonso Parreño-Torres, Jorge Mateo Sotos, Alejandro L. Borja

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In this research, different methods based on machine/deep learning algorithms are presented for the classification and diagnosis of patients with mental disorders such as alzheimer. For this purpose, the signals obtained from 32 unipolar electrodes identified by non-invasive EEG were examined, and their basic properties were obtained. More specifically, different well-known machine learning based classifiers have been used, i.e., support vector machine (SVM), Bayesian linear discriminant analysis (BLDA), decision tree (DT), Gaussian Naïve Bayes (GNB), K-nearest neighbor (KNN) and Convolutional Neural Network (CNN). A total of 668 patients from five different hospitals have been studied in the period from 2011 to 2021. The best accuracy is obtained was around 93 % in both ADM and ADA classifications. It can be concluded that such a classification will enable the training of algorithms that can be used to identify and classify different mental disorders with high accuracy.

Keywords: alzheimer, machine learning, deep learning, EEG

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Authors: Abdelrahman A. Ramzy, Bassel S. Abdallah, Mohamed E. Bahgat, Sarah M. Abdelkader, Sherif H. ElGohary

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Attention-Deficit/Hyperactivity Disorder (ADHD), epilepsy, and autism affect millions of children worldwide, many of which are undiagnosed despite the fact that all of these disorders are detectable in early childhood. Late diagnosis can cause severe problems due to the late treatment and to the misconceptions and lack of awareness as a whole towards these disorders. Moreover, electroencephalography (EEG) has played a vital role in the assessment of neural function in children. Therefore, quantitative EEG measurement will be utilized as a tool for use in the evaluation of patients who may have ADHD, epilepsy, and autism. We propose a screening tool that uses EEG signals and machine learning algorithms to detect these disorders at an early age in an automated manner. The proposed classifiers used with epilepsy as a step taken for the work done so far, provided an accuracy of approximately 97% using SVM, Naïve Bayes and Decision tree, while 98% using KNN, which gives hope for the work yet to be conducted.

Keywords: ADHD, autism, epilepsy, EEG, SVM

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Authors: C. Hamamura, V. Gialluca

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Much of the Eclectricity Distributors has about 70% of its electricity interruptions arising from cause "trees", alone or associated with wind and rain and with or without falling branch and / or trees. This contributes inexorably and significantly to outages, resulting in high costs as compensation in addition to the operation and maintenance costs. On the other hand, there is little data structure and solutions to better organize the trees pruning plan effectively, minimizing costs and environmentally friendly. This work describes the development of an algorithm to provide data of trees associated to power grid. The method is accomplished on several steps using satellite imagery and geographically vectorized grid. A sliding window like approach is performed to seek the area around the grid. The proposed method counted 764 trees on a patch of the grid, which was very close to the 738 trees counted manually. The trees data was used as a part of a larger project that implements a system to optimize tree pruning plan.

Keywords: image pattern recognition, trees pruning, trees recognition, neural network

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Authors: Bhojan Dhakal, Naba Raj Devkota, Chet Raj Upreti, Maheshwar Sapkota

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A study was conducted to find out the production potential, nutrient composition, and the variability of the most commonly available fodder trees along with the varying altitude to help optimize the dry matter requirement during winter lean period. The study was carried out from March to June, 2012 in Lho and Prok Village Development Committee of Manaslu Conservation Area (MCA), located in Gorkha district of Nepal. The other objective of the research was to learn the impact of climate change on livestock production linking it with feed availability. The study was conducted in two parts: social and biological. Accordingly, a households (HHs) survey was conducted to collect primary data from 70 HHs, focusing on the perception of respondents on impacts of climatic variability on the feeding management. The next part consisted of understanding yield potential and nutrient composition of the four most commonly available fodder trees (M. azedirach, M. alba, F. roxburghii, F. nemoralis), within two altitudes range: (1500-2000 masl and 2000-2500 masl) by using a RCB design in 2*4 factorial combination of treatments, each replicated four times. Results revealed that majority of the farmers perceived the change in climatic phenomenon more severely within the past five years. Farmers were using different adaptation technologies such as collection of forage from jungle, reducing unproductive animals, fodder trees utilization, and crop by product feeding at feed scarcity period. Ranking of the different fodder trees on the basis of indigenous knowledge and experiences revealed that F. roxburghii was the best-preferred fodder tree species (index value 0.72) in terms overall preferability whereas M. azedirach had highest growth and productivity (index value 0.77), F. roxburghii had highest adoptability (index value 0.69) and palatability (index value 0.69) as well. Similarly, fresh yield and dry matter yield of the each fodder trees was significant (P < 0.01) between the altitude and within species. Fodder trees yield analysis revealed that the highest dry matter (DM) yield (28 kg/tree) was obtained for F. roxburghii but that remained statistically similar (P > 0.05) to the other treatment. On the other hand, most of the parameters: ether extract (EE), acid detergent lignin (ADL), acid detergent fibre (ADF), cell wall digestibility (CWD), relative digestibility (RD), digestible nutrient (TDN), and Calcium (Ca) among the treatments were highly significant (P < 0.01). This indicates the scope of introducing productive and nutritive fodder trees species even at the high altitude to help reduce fodder scarcity problem during winter. The finding also revealed the scope of promoting all available local fodder trees species as crude protein content of these species were similar.

Keywords: fodder trees, yield potential, climate change, nutrient composition

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Authors: Asharaf Abdul Salam, Ibrahim Elsegaey, Rshood Khraif, Abdullah AlMutairi, Ali Aldosari

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Arab World that comprises of 22 member states of Arab League undergoes rapid transition in demographic front - fertility, mortality and migration. A distinctive geographic region spread across West Asia and North East Africa unified by Arabic language shares common values and characteristics even though diverse in economic and political conditions. Demographic lag that characterizes Arab World is unique but the present trend of declining fertility combined with the existing relatively low mortality undergoes significant changes in its population size. The current research aimed at (i) assessing the growth of population, over a period of 3 decades, (ii) exploring the components and (iii) understanding the public health impact. Based on International Data Base (IDB) of US Census Bureau, for 3 time periods – 1992, 2002 and 2012; 21 countries of Arab World have been analyzed by dividing them into four geographic sectors namely Gulf Cooperation Council (GCC), West Asia, Maghreb and Nile Valley African Horn. Population of Arab World grew widely during the past both through natural growth and migration. Immigrations pronounced especially in the resource intensive GCC nations not only from East Asian and central African countries but also from resource thrifty Arab nations. Migrations within the Arab World as well as outside of the Arab World remark an interesting demographic phenomenon that requires further research. But the transformations on public health statistics – impact of demographic change – depict a new era in the Arab World.

Keywords: demographic change, public health statistics, net migration, natural growth, geographic sectors, fertility and mortality, life expectancy

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Authors: Guilherme Ribeiro, Alexandre Oliveira, Antonio Ferreira, Shyam Visweswaran, Gregory Cooper

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Patient-specific models are instance-based learning algorithms that take advantage of the particular features of the patient case at hand to predict an outcome. We introduce two patient-specific algorithms based on decision tree paradigm that use AUC as a metric to select an attribute. We apply the patient specific algorithms to predict outcomes in several datasets, including medical datasets. Compared to the patient-specific decision path (PSDP) entropy-based and CART methods, the AUC-based patient-specific decision path models performed equivalently on area under the ROC curve (AUC). Our results provide support for patient-specific methods being a promising approach for making clinical predictions.

Keywords: approach instance-based, area under the ROC curve, patient-specific decision path, clinical predictions

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Authors: Fitri Rahmi Fadhilah, Edhyana Sahiratmadja, Ani Melani Maskoen, Ratu Safitri, Supartini Syarif, Herman Susanto

Abstract:

Cervical cancer is the second most common cancer in women after breast cancer. In Indonesia, the incidence of cervical cancer cases is estimated at 25-40 per 100,000 women per year. Human papillomavirus (HPV) infection is a major cause of cervical cancer, and HPV-16 is the most common genotype that infects the cervical tissue. The major late protein L1 may be associated with infectivity and pathogenicity and its variation can be used to classify HPV isolates. The aim of this study was to determine the phylogenetic tree of HPV 16 L1 gene from cervical cancer patient isolates in Bandung. After confirming HPV-16 by Linear Array Genotyping Test, L1 gene was amplified using specific primers and subject for sequencing. Phylogenetic analysis revealed that HPV 16 from Bandung was in the subgroup of Asia and East Asia, showing the close host-agent relationship among the Asian type.

Keywords: L1 HPV 16, cervical cancer, bandung, phylogenetic

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Authors: Mariam Bibi, Rubab Mehboob, Mehreen Sirshar

Abstract:

Satisfying the customer requirements is the ultimate goal of producing or developing any product. The quality of the product is decided on the bases of the level of customer satisfaction. There are different techniques which have been reported during the survey which enhance the quality of the product through software defect prediction and by locating the missing software requirements. Some mining techniques were proposed to assess the individual performance indicators in collaborative environment to reduce errors at individual level. The basic intention is to produce a product with zero or few defects thereby producing a best product quality wise. In the analysis of survey the techniques like Genetic algorithm, artificial neural network, classification and clustering techniques and decision tree are studied. After analysis it has been discovered that these techniques contributed much to the improvement and enhancement of the quality of the product.

Keywords: data mining, defect prediction, missing requirements, software quality

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Authors: Sibongiseni Thabethe, Ian Korir

Abstract:

The effective management of spent fuel pool (SFP) safety has been raised as one of the emerging issues to further enhance nuclear installation safety after the Fukushima accident on March 11, 2011. Before then, SFP safety-related issues have been mainly focused on (a) controlling the configuration of the fuel assemblies in the pool with no loss of pool coolants and (b) ensuring adequate pool storage space to prevent fuel criticality owing to chain reactions of the fission products and the ability for neutron absorption to keep the fuel cool. A probabilistic safety (PSA) assessment was performed using the systems analysis program for hands-on integrated reliability evaluations (SAPHIRE) computer code. Event and fault tree analysis was done to develop a PSA model for the Koeberg SFP. We present preliminary PSA results of events that lead to boiling and cause fuel uncovering, resulting in possible fuel damage in the Koeberg SFP.

Keywords: computer code, fuel assemblies, probabilistic risk assessment, spent fuel pool

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Authors: Lucy W. Mburu, Richard Karanja, Simon N. Mwendia

Abstract:

Over recent years, learners have experienced a constant need to access on demand knowledge that is fully aligned with the paradigm of cloud computing. As motivated by the global sustainable development goal to ensure inclusive and equitable learning opportunities, this research has developed a framework hinged on the knowledge-as-a-service architecture that utilizes knowledge from ambient learning systems. Through statistical analysis and decision tree modeling, the study discovers influential variables for ambient learning among university students. The main aim is to generate a platform for disseminating and exploiting the available knowledge to aid the learning process and, thus, to improve educational support on the ambient learning system. The research further explores how collaborative effort can be used to form a knowledge network that allows access to heterogeneous sources of knowledge, which benefits knowledge consumers, such as the developers of ambient learning systems.

Keywords: actionable knowledge, ambient learning, cloud computing, decision trees, knowledge as a service

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Authors: P. Halder, A. Zaman

Abstract:

It is estimated that heart disease accounts for one in ten deaths worldwide. United States deaths due to heart disease are among the leading causes of death according to the World Health Organization. Cardiovascular diseases (CVDs) account for one in four U.S. deaths, according to the Centers for Disease Control and Prevention (CDC). According to statistics, women are more likely than men to die from heart disease as a result of strokes. A 50% increase in men's mortality was reported by the World Health Organization in 2009. The consequences of cardiovascular disease are severe. The causes of heart disease include diabetes, high blood pressure, high cholesterol, abnormal pulse rates, etc. Machine learning (ML) can be used to make predictions and decisions in the healthcare industry. Thus, scientists have turned to modern technologies like Machine Learning and Data Mining to predict diseases. The disease prediction is based on four algorithms. Compared to other boosts, the Ada boost is much more accurate.

Keywords: heart disease, cardiovascular disease, coronary artery disease, feature selection, random forest, AdaBoost, SVM, decision tree

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Authors: Ying Ying Liu, Austin Cooke, Parimala Thulasiraman

Abstract:

Internet of Things (IoT) has attracted a recent spark in research on Internet of Vehicles (IoV). In this paper, we focus on one research area in IoV: preserving location privacy of vehicle data. We discuss existing location privacy preserving techniques and provide a scheme for evaluating these techniques under IoV traffic condition. We propose a different strategy in applying Differential Privacy using k-d tree data structure to preserve location privacy and experiment on real world Gowalla data set. We show that our strategy produces differentially private data, good preservation of utility by achieving similar regression accuracy to the original dataset on an LSTM (Long Term Short Term Memory) neural network traffic predictor.

Keywords: differential privacy, internet of things, internet of vehicles, location privacy, privacy preservation scheme

Procedia PDF Downloads 180

Authors: A. Hayrapetyan, A. Simon, P. Marques, G. Renart

Abstract:

Business survival is a question of greatest interest for any economy. Firm characteristics that can explain or predict performance and, ultimately, business survival become of the greatest significance, as the sustainable longevity of any business can mean health for the future of the country. Family Firms (FFs) are one of the most ubiquitous forms of business worldwide, as more than half of European firms (60%) are considered as family firms. Therefore, the inherent characteristics of FFs are one of the possible explanatory variables for firm survival because FFs have strategic goals that differentiate them from other types of businesses. Although there is literature on the performance of FFs across generations, there are fewer studies on the factors that impact the survival of family and non-family FFs, as there is a lack of data on failed firms. To address this gap, this paper explores the differential survival of family firms versus non-family firms with a representative sample of companies of the region of Catalonia (Northeast of Spain) that were adhoc classified as family or nonfamily firms, as well as classified as failed or surviving, since no census data for family firms or for failed firms is available in Spain. By using the COX regression model on a representative sample of 629 family and non-family firms, this study investigates to what extent financial ratios, such as Liquidity, Solvency Rate can impact business survival, taking into consideration the socioemotional side of family firms, as well as revealing the differences between family and non-family firms. The findings show that the liquidity rate is significant for non-family firm survival, whereas not for family firms. On the other hand, FFs can benefit while having a higher solvency rate. Ultimately, this paper discovers that FFs increase their chances of survival when they are small, as the growth in size starts negatively impacting the socioemotional objectives of the firm. This study proves the existence of significant differences between family and non-family firms’ survival during economic crises, suggesting that the prioritization of emotional wealth creates distinct conditions for both types of firms.

Keywords: COX regression, economy crises, family firm, non-family firm, survival

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Authors: Tomas O. Ortega, Cecilia T. Reyes, Cecile O. Legaspi, Cylde G. Abayon, Anna Mae C. Relingo, Mary Eden M. Teruel

Abstract:

A study was conducted to evaluate the performance in the delivery of environmental management programs of the local government of Malay, Aklan, Philippines. The samples were determined by adopting the Multi-Stage Random Probability Sampling technique. The 150 respondents were drawn from barangays with larger shares of the population based on the Philippine Statistical Authority’s Data on Census Population and Housing for the year 2015. The qualified sample respondents were selected using the Kish Grid. Female respondents were targeted for even numbered questionnaires while male respondents were targeted for odd numbers. The four major core concepts namely awareness, availment, satisfaction and need for action were used in measuring the rating of the respondents and presented in frequency and percentage distributions. The reasons for their response were likewise gathered. The study inferred that a large portion of the respondents was profoundly aware of the environmental management programs implemented by their local government unit especially the solid waste management and the clean-up programs/projects. Programs to control air pollution and waste water management obtained the least awareness ratings from the respondents. A high percentage of respondents had availed of environmental management programs, particularly solid waste management. Overall, majority of the respondents were satisfied with the environmental management programs rendered by the local government unit and therefore needs less action. It is recommended that the local government unit must strengthen air pollution control program. Appropriate action must be taken to support the people’s interest in this program most particularly to the individuals who burn their garbage. Seminars and training-workshops about appropriate waste disposal will most likely help settle this issue.

Keywords: availment, awareness, environmental management, need for action, satisfaction

Procedia PDF Downloads 311