Search results for: harvested tree volume

Authors: M. Insiat Islam Rabby, M. Mahbubur Rahman, Eshanul Islam, A. K. M. Sadrul Islam

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The numerical and simulative analysis of laminar heat exchange convection of aluminum oxide (Al₂O₃) - water nanofluid for the developed region through two parallel plates is presented in this present work. The second order single phase energy equation, mass and momentum equation are solved by using finite volume method with the ANSYS FLUENT 16 software. The distance between two parallel plates is 4 mm and length is 600 mm. Aluminum oxide (Al₂O₃) is used as nanoparticle and water is used as the base/working fluid for the investigation. At the time of simulation 1% to 5% volume concentrations of the Al₂O₃ nanoparticles are used for mixing with water to produce nanofluid and a wide range of interval of Reynolds number from 500 to 1100 at constant heat flux 500 W/m² at the channel wall has also been introduced. The result reveals that for increasing the Reynolds number the Nusselt number and heat transfer coefficient are increased linearly and friction factor decreased linearly in the developed region for both water and Al₂O₃-H₂O nanofluid. By increasing the volume fraction of Al₂O₃-H₂O nanofluid from 1% to 5% the value of Nusselt number increased rapidly from 0.7 to 7.32%, heat transfer coefficient increased 7.14% to 31.5% and friction factor increased very little from 0.1% to 4% for constant Reynolds number compared to pure water. At constant heat transfer coefficient 700 W/m2-K the pumping power advantages have been achieved 20% for 1% volume concentration and 62% for 3% volume concentration of nanofluid compared to pure water.

Keywords: convective heat transfer, pumping power, constant heat flux, nanofluid, nanoparticles, volume concentration, thermal conductivity

Procedia PDF Downloads 157

Authors: Yara Saleh, Sebastien Antherieu, Romain Dusautoir, Jules Sotty, Laurent Alleman, Ludivine Canivet, Esperanza Perdrix, Pierre Dubot, Anne Platel, Fabrice Nesslany, Guillaume Garcon, Jean-Marc Lo-Guidice

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Air pollution is one of the leading causes of premature death worldwide. Among air pollutants, particulate matter (PM) is a major health risk factor, through the induction of cardiopulmonary diseases and lung cancers. They are composed of coarse, fine and ultrafine particles (PM10, PM2.5, and PM0.1 respectively). Ultrafine particles are emerging unregulated pollutants that might have greater toxicity than larger particles, since they are more abundant and consequently have higher surface area per unit of mass. Our project aims to develop a relevant in vivo model of sub-chronic exposure to atmospheric particles in order to elucidate the specific respiratory impact of ultrafine particles compared to fine particulate matter. Quasi-ultrafine (PM0.18) and fine (PM2.5) particles have been collected in the urban industrial zone of Dunkirk in north France during a 7-month campaign, and submitted to physico-chemical characterization. BALB/c mice were then exposed intranasally to 10µg of PM0.18 or PM2.5 3 times a week. After 1 or 3-month exposure, broncho alveolar lavages (BAL) were performed and lung tissues were harvested for histological and transcriptomic analyses. The physico-chemical study of the collected particles shows that there is no major difference in elemental and surface chemical composition between PM0.18 and PM2.5. Furthermore, the results of the cytological analyses carried out show that both types of particulate fractions can be internalized in lung cells. However, the cell count in BAL and preliminary transcriptomic data suggest that PM0.18 could be more reactive and induce a stronger lung inflammation in exposed mice than PM2.5. Complementary studies are in progress to confirm these first data and to identify the metabolic pathways more specifically associated with the toxicity of ultrafine particles.

Keywords: environmental pollution, lung affect, mice, ultrafine particles

Procedia PDF Downloads 234

Authors: Ing-Jen Hung, Tzu-Chien Wang

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Nowadays, customers can interact with firms through miscellaneous online ads on different channels easily. In other words, customer now has innumerable options and limitless time to accomplish their commercial activities with firms, individualizing their own online customer journey. This kind of convenience emphasizes the importance of online advertisement allocation on different channels. Therefore, profound understanding of customer behavior can make considerable benefit from optimizing fund allocation on diverse ad channels. To achieve this objective, multiple firms utilize numerical methodology to create data-driven advertisement policy. In our research, we aim to exploit online customer click data to discover the correlations between each channel and their sequential relations. We use LSTM to deal with sequential property of our data and compare its accuracy with other non-sequential methods, such as CART decision tree, logistic regression, etc. Besides, we also classify our customers into several groups by their behavioral characteristics to perceive the differences between all groups as customer portrait. As a result, we discover distinct customer journey under each customer portrait. Our article provides some insights into marketing research and can help firm to formulate online advertising criteria.

Keywords: LSTM, customer journey, marketing, channel ads

Procedia PDF Downloads 117

Authors: M. Gazzah, B. Jaouachi, F. Sakli

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This research focuses on the yarn-to-yarn and metal-to-fabric friction effects on the residual bagging behavior expressed by residual bagging height, volume and recovery of some denim fabrics. The results show, that both residual bagging height and residual bagging volume, which is determined using image analysis method, are significantly affected due to the most influential fabric parameter variations, the weft yarns density and the mean frictional coefficients. After the applied number of fatigue cycles, the findings revealed that the weft yarn rigidity contributes on fabric bagging behavior accurately. Among the tested samples, our results show that the elastic fabrics present a high recovery ability to give low bagging height and volume values.

Keywords: bagging recovery, denim fabric, metal-to-fabric friction, residual bagging height, yarn-to-yarn friction

Procedia PDF Downloads 574

Authors: Hafiza Javaria Ashraf, Asim Abbasi, Muhammad Hussnain Babar, Muhammad Sufyan

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Brevicoryne brassicae (Aphid) is not only the major biotic constraint of rapeseed crop but also transmits 20 different viral pathogens that cause diseases in crucifers. Aphids cause major losses to rapeseed by stunting growth and yield, with real damage being contamination of harvested heads. The misuse of pesticides has led to tremendous economic losses and hazards to human health and environmental pollution. Thus, newer approaches for pest control are continuously being sought. The naturally occurring, biologically active plant-based products seem to have a prominent role in the development of future commercial pesticides not only for increased productivity but their eco-friendly nature. The present experiment was carried out in Research Area of Ayub Agriculture Research Institute, Faisalabad to check the efficacy of different botanicals against rapeseed aphid. The tested botanicals were, neem seed extract, neem leaf extract, dathora seed extract, kaner leaf extract and aak leaf extract. Insecticide, advantage 20 EC served as the positive control in the experiment. Data was recorded before and after 1, 3 and 7 days of treatment application. The results of the experiment revealed that neem seed extract exhibited maximum mortality (48.42%) followed by dathora (45.54%) and kaner leaf extract (40.29%) after 7 days of treatment application. However minimum mortality i.e. 26.64% was observed in case of aak leaf extract. Advantage encountered maximum mortality i.e. 86.14%. All treatments caused maximum mortality after 7 days of treatment application. In case of pollinators maximum population reduction was observed in case of insecticide (74.29%) while minimum reduction was observed in neem leaf extract (11.57%). Hence it was concluded that unlike insecticides, plant based products can be a better option for regulating pests and conserving beneficial insect fauna.

Keywords: Aphid, mortality, plant based, pollinators

Procedia PDF Downloads 223

Authors: Seyed Mohammad Ali Zahedi

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This field experiment was conducted in Isfahan in 2011 in order to study the effect of plant density and planting pattern on growth, yield and quality of silage corn (SC 704) using a randomized complete block design with split plot layout and four replications. The main plot consisted of three planting patterns (60 and 75 cm single planting row and 75 cm double planting row referred to as 60S, 75S and 75T, respectively). The subplots consisted of four levels of plant densities (65000, 80000, 95000 and 110000 plants per hectare). Each subplot consisted of 7 rows, each with 10m length. Vegetative and reproductive characteristics of plants at silking and hard dough stages (when the plants were harvested for silage) were evaluated. Results of variance analysis showed that the effects of planting pattern and plant density were significant on leaf area per plant, leaf area index (at silking), plant height, stem diameter, dry weights of leaf, stem and ear in silking and harvest stages and on fresh and dry yield, dry matter percentage and crude protein percentage at harvest. There was no planting pattern × plant density interaction for these parameters. As row space increased from 60 cm with single planting to 75 cm with single planting, leaf area index and plant height increased, but leaf area per plant, stem diameter, dry weight of leaf, stem and ear, dry matter percentage, dry matter yield and crude protein percentage decreased. Dry matter yield reduced from 24.9 to 18.5 t/ha and crude protein percentage decreased from 6.11 to 5.60 percent. When the plant density increased from 65000 to 110000 plant per hectare, leaf area index, plant height, dry weight of leaf, stem and ear and dry matter yield increased from 19.2 to 23.3 t/ha, whereas leaf area per plant, stem diameter, dry matter percentage and crude protein percentage decreased from 6.30 to 5.25. The best results were obtained with 60 cm row distance with single planting and 110000 plants per hectare.

Keywords: silage corn, plant density, planting pattern, yield

Procedia PDF Downloads 335

Authors: Ozhan Simsek, Dicle Donmez, Burhanettin Imrak, Ahsen Isik Ozguven, Yildiz Aka Kacar

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Pomegranate (Punica granatum L.) is known to be one of the oldest edible fruit tree species, with a wide geographical global distribution. Fruits from the two defined varieties (Hicaznar and 33N26) were taken at intervals after pollination and fertilization at different sizes. Seed samples were used for transcriptome sequencing. Primary sequencing was produced by Illumina Hi-Seq™ 2000. Firstly, we had raw reads, and it was subjected to quality control (QC). Raw reads were filtered into clean reads and aligned to the reference sequences. De novo analysis was performed to detect genes expressed in seeds of pomegranate varieties. We performed downstream analysis to determine differentially expressed genes. We generated about 27.09 gb bases in total after Illumina Hi-Seq sequencing. All samples were assembled together, we got 59,264 Unigenes, the total length, average length, N50, and GC content of Unigenes are 84.547.276 bp, 1.426 bp, 2,137 bp, and 46.20 %, respectively. Unigenes were annotated with 7 functional databases, finally, 42.681(NR: 72.02%), 39.660 (NT: 66.92%), 30.790 (Swissprot: 51.95%), 20.212 (COG: 34.11%), 27.689 (KEGG: 46.72%), 12.328 (GO: 20.80%), and 33,833 (Interpro: 57.09%) Unigenes were annotated. With functional annotation results, we detected 42.376 CDS, and 4.999 SSR distribute on 16.143 Unigenes.

Keywords: next generation sequencing, SSR, RNA-Seq, Illumina

Procedia PDF Downloads 237

Authors: Maurice Ntahobari, Levin Kuhlmann, Mario Boley, Zhinoos Razavi Hesabi

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For machine learning based epileptic seizure prediction, it is important for the model to be implemented in small implantable or wearable devices that can be used to monitor epilepsy patients; however, current state-of-the-art methods are complex and computationally intensive. We use Shapley Additive Explanation (SHAP) to find relevant intracranial electroencephalogram (iEEG) features and improve the computational efficiency of a state-of-the-art seizure prediction method based on the extra trees classifier while maintaining prediction performance. Results for a small contest dataset and a much larger dataset with continuous recordings of up to 3 years per patient from 15 patients yield better than chance prediction performance (p < 0.004). Moreover, while the performance of the SHAP-based model is comparable to that of the benchmark, the overall training and prediction time of the model has been reduced by a factor of 1.83. It can also be noted that the feature called zero crossing value is the best EEG feature for seizure prediction. These results suggest state-of-the-art seizure prediction performance can be achieved using efficient methods based on optimal feature selection.

Keywords: machine learning, seizure prediction, extra tree classifier, SHAP, epilepsy

Procedia PDF Downloads 108

Authors: Sangeeta Palekar, Neeraj Rangwani, Akash Poddar, Jayu Kalambe

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The bio-medical analysis is an indispensable procedure for identifying health-related diseases like diabetes. Monitoring the glucose level in our body regularly helps us identify hyperglycemia and hypoglycemia, which can cause severe medical problems like nerve damage or kidney diseases. This paper presents a method for predicting the glucose concentration in blood samples using image processing and machine learning algorithms. The glucose solution is prepared by the glucose oxidase (GOD) and peroxidase (POD) method. An experimental database is generated based on the colorimetric technique. The image of the glucose solution is captured by the raspberry pi camera and analyzed using image processing by extracting the RGB, HSV, LUX color space values. Regression algorithms like multiple linear regression, decision tree, RandomForest, and XGBoost were used to predict the unknown glucose concentration. The multiple linear regression algorithm predicts the results with 97% accuracy. The image processing and machine learning-based approach reduce the hardware complexities of existing platforms.

Keywords: artificial intelligence glucose detection, glucose oxidase, peroxidase, image processing, machine learning

Procedia PDF Downloads 199

Authors: Chaghoub Soraya, Zhang Xiaoyan

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This research presents the first constant approximation algorithm to the p-median network design problem with multiple cable types. This problem was addressed with a single cable type and there is a bifactor approximation algorithm for the problem. To the best of our knowledge, the algorithm proposed in this paper is the first constant approximation algorithm for the p-median network design with multiple cable types. The addressed problem is a combination of two well studied problems which are p-median problem and network design problem. The introduced algorithm is a random sampling approximation algorithm of constant factor which is conceived by using some random sampling techniques form the literature. It is based on a redistribution Lemma from the literature and a steiner tree problem as a subproblem. This algorithm is simple, and it relies on the notions of random sampling and probability. The proposed approach gives an approximation solution with one constant ratio without violating any of the constraints, in contrast to the one proposed in the literature. This paper provides a (21 + 2)-approximation algorithm for the p-median network design problem with multiple cable types using random sampling techniques.

Keywords: approximation algorithms, buy-at-bulk, combinatorial optimization, network design, p-median

Procedia PDF Downloads 198

Authors: L. Salomon, P. Sklenar

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The Andes hold the highest plant species diversity in the world. How this occurred is one of the most intriguing questions in studies addressing the origin and patterning of plant diversity worldwide. Recently, the explosive adaptive radiations found in high Andean groups have been pointed as triggers to this spectacular diversity. The Andes is the species-richest area for the biggest genus from the Asteraceae family: Senecio. There, the genus presents an incredible diversity of species, striking growth form variation, and large niche span. Even when some studies tried to disentangle the evolutionary story for some Andean species in Senecio, they obtained partially resolved and low supported phylogenies, as expected for recently radiated groups. The high-throughput sequencing (HTS) approaches have proved to be a powerful tool answering phylogenetic questions in those groups whose evolutionary stories are recent and traditional techniques like Sanger sequencing are not informative enough. Although these tools have been used to understand the evolution of an increasing number of Andean groups, nowadays, their scope has not been applied for Senecio. This project aims to contribute to a better knowledge of the mechanisms shaping the hyper diversity of Senecio in the Andean region, using HTS focusing on Senecio ser. Culcitium (Asteraceae), recently recircumscribed. Firstly, reconstructing a highly resolved and supported phylogeny, and after assessing the role of allopatric differentiation, hybridization, and genome duplication in the diversification of the group. Using the Hyb-Seq approach, combining target enrichment using Asteraceae COS loci baits and genome skimming, more than 100 new accessions were generated. HybPhyloMaker and HybPiper pipelines were used for the phylogenetic analyses, and another pipeline in development (Paralogue Wizard) was used to deal with paralogues. RAxML was used to generate gene trees and Astral for species tree reconstruction. Phyparts were used to explore as first step of gene tree discordance along the clades. Fully resolved with moderated supported trees were obtained, showing Senecio ser. Culcitium as monophyletic. Within the group, some species formed well-supported clades with morphologically related species, while some species would not have exclusive ancestry, in concordance with previous studies using amplified fragment length polymorphism (AFLP) showing geographical differentiation. Discordance between gene trees was detected. Paralogues were detected for many loci, indicating possible genome duplications; ploidy level estimation using flow cytometry will be carried out during the next months in order to identify the role of this process in the diversification of the group. Likewise, TreeSetViz package for Mesquite, hierarchical likelihood ratio congruence test using Concaterpillar, and Procrustean Approach to Cophylogeny (PACo), will be used to evaluate the congruence among different inheritance patterns. In order to evaluate the influence of hybridization and Incomplete Lineage Sorting (ILS) in each resultant clade from the phylogeny, Joly et al.'s 2009 method in a coalescent scenario and Paterson’s D-statistic will be performed. Even when the main discordance sources between gene trees were not explored in detail yet, the data show that at least to some degree, processes such as genome duplication, hybridization, and/or ILS could be involved in the evolution of the group.

Keywords: adaptive radiations, Andes, genome duplication, hybridization, Senecio

Procedia PDF Downloads 135

Authors: Rafay Ahmed, Condon Lau

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Bone healing is a complex and sequential process involving changes at the molecular level. Raman spectroscopy is a promising technique to study bone mineral and matrix environments simultaneously. In this study, subcritical calvarial defects are used to study bone composition during healing without discomposing the fracture. The model allowed to monitor the natural healing of bone avoiding mechanical harm to the callus. Calvarial defects were created using 1mm burr drill in the parietal bones of Sprague-Dawley rats (n=8) that served in vivo defects. After 7 days, their skulls were harvested after euthanizing. One additional defect per sample was created on the opposite parietal bone using same calvarial defect procedure to serve as control defect. Raman spectroscopy (785 nm) was established to investigate bone parameters of three different skull surfaces; in vivo defects, control defects and normal surface. Principal component analysis (PCA) was utilized for the data analysis and interpretation of Raman spectra and helped in the classification of groups. PCA was able to distinguish in vivo defects from normal surface and control defects. PC1 shows that the major variation at 958 cm⁻¹, which corresponds to ʋ1 phosphate mineral band. PC2 shows the major variation at 1448 cm⁻¹ which is the characteristic band of CH2 deformation and corresponds to collagens. Raman parameters, namely, mineral to matrix ratio and crystallinity was found significantly decreased in the in vivo defects compared to surface and controls. Scanning electron microscope and optical microscope images show the formation of newly generated matrix by means of bony bridges of collagens. Optical profiler shows that surface roughness increased by 30% from controls to in vivo defects after 7 days. These results agree with Raman assessment parameters and confirm the new collagen formation during healing.

Keywords: Raman spectroscopy, principal component analysis, calvarial defects, tissue characterization

Procedia PDF Downloads 220

Authors: Diego De Castro Rodrigues, Marcelo B. Nery, Sergio Adorno

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The project aims to develop a framework for social data analysis, particularly by mobilizing criminal records and applying descriptive computational techniques, such as associative algorithms and extraction of tree decision rules, among others. The methods and instruments discussed in this work will enable the discovery of patterns, providing a guided means to identify similarities between recurring situations in the social sphere using descriptive techniques and data visualization. The study area has been defined as the city of São Paulo, with the structuring of social data as the central idea, with a particular focus on the quality of the information. Given this, a set of tools will be validated, including the use of a database and tools for visualizing the results. Among the main deliverables related to products and the development of articles are the discoveries made during the research phase. The effectiveness and utility of the results will depend on studies involving real data, validated both by domain experts and by identifying and comparing the patterns found in this study with other phenomena described in the literature. The intention is to contribute to evidence-based understanding and decision-making in the social field.

Keywords: social data analysis, criminal records, computational techniques, data mining, big data

Procedia PDF Downloads 81

Authors: Sakeh N. Mohd, Bahari M. N. Abdul, Abdullah S. N. Akmar

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Oil palm production generates high export earnings to many countries especially in Southeast Asian region. Infection by necrotrophic fungus, Ganoderma boninense on oil palm results in basal stem rot which compromises oil palm production leading to significant economic loss. There are no reliable disease treatments nor promising resistant oil palm variety has been cultivated to eradicate the disease up to date. Thus, understanding molecular mechanisms underlying early interactions of oil palm with Ganoderma boninense may be vital to promote preventive or control measure of the disease. In the present study, four months old oil palm seedlings were infected via artificial inoculation of Ganoderma boninense on rubber wood blocks. Roots of six biological replicates of treated and untreated oil palm seedlings were harvested at 0, 3, 7 and 11 days post inoculation. Next-generation sequencing was performed to generate high-throughput RNA-Seq data and identify differentially expressed genes (DEGs) during early oil palm-Ganoderma boninense interaction. Based on de novo transcriptome assembly, a total of 427,122,605 paired-end clean reads were assembled into 30,654 unigenes. DEGs analysis revealed upregulation of 173 transcription factors on Ganoderma boninense-treated oil palm seedlings. Sixty-one transcription factors were categorized as DEGs according to stringent cut-off values of genes with log2 ratio [Number of treated oil palm seedlings/ Number of untreated oil palm seedlings] ≥ |1.0| (corresponding to 2-fold or more upregulation) and P-value ≤ 0.01. Transcription factors in response to biotic stress will be screened out from abiotic stress using reverse transcriptase polymerase chain reaction. Transcription factors unique to biotic stress will be verified using real-time polymerase chain reaction. The findings will help researchers to pinpoint defense response mechanism specific against Ganoderma boninense.

Keywords: Ganoderma boninense, necrotrophic, next-generation sequencing, transcription factors

Procedia PDF Downloads 263

Authors: Asma Ben Yaghlane, Mohamed Naceur Azaiez

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We consider the problem of attacks on networks. We define the concept of system survivability in networks in the presence of intelligent threats. Our setting of the problem assumes a flow to be sent from one source node to a destination node. The attacker attempts to disable the network by preventing the flow to reach its destination while the defender attempts to identify the best path-set to use to maximize the chance of arrival of the flow to the destination node. Our concept is shown to be different from the classical concept of network reliability. We distinguish two types of network survivability related to the defender and to the attacker of the network, respectively. We prove that the defender-based-network survivability plays the role of a lower bound while the attacker-based-network survivability plays the role of an upper bound of network reliability. We also prove that both concepts almost never agree nor coincide with network reliability. Moreover, we use the shortest-path problem to determine the defender-based-network survivability and the min-cut problem to determine the attacker-based-network survivability. We extend the problem to a variety of models including the minimum-spanning-tree problem and the multiple source-/destination-network problems.

Keywords: defense/attack strategies, information, networks, reliability, survivability

Procedia PDF Downloads 390

Authors: Thomas F. Ducey, Kristin M. Trippe, James A. Ippolito, Jeffrey M. Novak, Mark G. Johnson, Gilbert C. Sigua

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The Oronogo-Duenweg mining belt –approximately 20 square miles around the Joplin, Missouri area– is a designated United States Environmental Protection Agency Superfund site due to lead-contaminated soil and groundwater by former mining and smelting operations. Over almost a century of mining (from 1848 to the late 1960’s), an estimated ten million tons of cadmium, lead, and zinc containing material have been deposited on approximately 9,000 acres. Sites that have undergone remediation, in which the O, A, and B horizons have been removed along with the lead contamination, the exposed C horizon remains incalcitrant to revegetation efforts. These sites also suffer from poor soil microbial activity, as measured by soil extracellular enzymatic assays, though 16S ribosomal ribonucleic acid (rRNA) indicates that microbial diversity is equal to sites that have avoided mine-related contamination. Soil analysis reveals low soil organic carbon, along with high levels of bio-available zinc, that reflect the poor soil fertility conditions and low microbial activity. Our study looked at the use of several materials to restore and remediate these sites, with the goal of improving soil health. The following materials, and their purposes for incorporation into the study, were as follows: manure-based biochar for the binding of zinc and other heavy metals responsible for phytotoxicity, locally sourced biosolids and compost to incorporate organic carbon into the depleted soils, effective microorganisms harvested from nearby pristine sites to provide a stable community for nutrient cycling in the newly composited 'soil material'. Our results indicate that all four materials used in conjunction result in the greatest benefit to these mine-impacted soils, based on above ground biomass, microbial biomass, and soil enzymatic activities.

Keywords: locally effective microorganisms, biochar, remediation, reclamation

Procedia PDF Downloads 215

Authors: Jung-Soo Lee, Ujjal Kumar Nath, IllSup Nou, Dulal Chandra

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Leaf lettuce is one of the most important leafy vegetables that is used as raw for salad and part of everyday dishes in many parts of the world including Asian countries. Since it is used as fresh, its quality maintenance is crucial which depends on several pre- and postharvest factors. In order to investigate the effects of pre-fix factors on the postharvest quality, the interaction of pre-fix factors such as growing conditions and fixed factor like cultivars were evaluated. Four Korean leaf lettuce cultivars ‘Cheongchima’, ‘Cheongchuckmyeon’, ‘Geockchima’ and ‘Geockchuckmyeon’ were grown under natural condition (as control) and altered growing condition (green house) with excess soil water and 50% shading to monitor their postharvest qualities. Several growth parameters like plant height, number of leaves, leaf thickness, fresh biomass yield as well as postharvest qualities like fresh weight loss, respiration rate, changes in color and shelf-life were measured in lettuce during storage up to 36 days at 5°C. Plant height and the number of leaves were affected by both pre-fix growing conditions as well as the cultivars. However, fresh biomass yield was affected by only growing condition, whereas leaf thickness was affected by cultivars. Additionally, the degrees of fresh weight loss and respiration rate of leaf lettuce at postharvest stages were influenced by pre-fix growing conditions and cultivars. However, changes in color of leaves during storage were less remarkable in samples harvested from of ‘Cheongchima’ and ‘Cheongchuckmyeon’ cultivars grown in excess watering with 50% shade than that grown in control condition. Consequently, these two cultivars also showed longer shelf-life when they were grown in excess watering with 50% shade than other cultivars or samples were grown in control condition. Based on the measured parameters, it can be concluded that postharvest quality of leaf lettuce might be accelerated by growing lettuce under excess soil water with 50% shading.

Keywords: cultivar, growing condition, leaf lettuce, postharvest quality, shelf-life

Procedia PDF Downloads 258

Authors: G. Disciglio, G. Gatta, F. Lops, A. Libutti, A. Tarantino, E. Tarantino

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The experimental trial was carried out in open field at Foggia district (Apulia Region, Southern Italy), during the spring-summer season 2014, in order to evaluate the effect of four biostimulant products (RadiconÒ, Viormon plusÒ, LysodinÒ and SiaptonÒ 10L), compared with a control (no biostimulant), on the infestation of processing tomato crop (cv Dres) by the chlorophyll-lacking root parasite Phelipanche ramosa. Biostimulants consist in different categories of products (microbial inoculants, humic and fulvic acids, hydrolyzed proteins and aminoacids, seaweed extracts) which play various roles in plant growing, including the improvement of crop resistance and quali-quantitative characteristics of yield. The experimental trial was arranged according to a complete randomized block design with five treatments, each of one replicated three times. The processing tomato seedlings were transplanted on 5 May 2014. Throughout the crop cycle, P. ramosa infestation was assessed according to the number of emerged shoots (branched plants) counted in each plot, at 66, 78 and 92 day after transplanting. The tomato fruits were harvested at full-stage of maturity on 8 August 2014. From each plot, the marketable yield was measured and the quali-quantitative yield parameters (mean weight, dry matter content, colour coordinate, colour index and soluble solids content of the fruits) were determined. The whole dataset was tested according to the basic assumptions for the analysis of variance (ANOVA) and the differences between the means were determined using Tukey’s tests at the 5% probability level. The results of the study showed that none of the applied biostimulants provided a whole control of Phelipanche, although some positive effects were obtained from their application. To this respect, the RadiconÒ appeared to be the most effective in reducing the infestation of this root-parasite in tomato crop. This treatment also gave the higher tomato yield.

Keywords: biostimulant, control methods, Phelipanche ramosa, tomato crop

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Authors: Edward Schwalb, Matthias Dehmer, Michael Schlenkrich, Farzaneh Taslimi, Ketron Mitchell-Wynne, Horen Kuecuekyan

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We present a generalized feature extraction approach for supporting Machine Learning (ML) algorithms which perform tasks similar to Finite-Element Analysis (FEA). We report results for estimating the Head Injury Categorization (HIC) of vehicle engine compartments across various impact scenarios. Our experiments demonstrate that models learned using features derived with a simple discretization approach provide a reasonable approximation of a full simulation. We observe that Decision Trees could be as effective as Neural Networks for the HIC task. The simplicity and performance of the learned Decision Trees could offer a trade-off of a multiple order of magnitude increase in speed and cost improvement over full simulation for a reasonable approximation. When used as a complement to full simulation, the approach enables rapid approximate feedback to engineering teams before submission for full analysis. The approach produces mesh independent features and is further agnostic of the assembly structure.

Keywords: mechanical design validation, FEA, supervised decision tree, convolutional neural network.

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Authors: Fabio Fabris, Alex A. Freitas

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Hierarchical classification is a special type of classification task where the class labels are organised into a hierarchy, with more generic class labels being ancestors of more specific ones. Meta-learning for classification-algorithm recommendation consists of recommending to the user a classification algorithm, from a pool of candidate algorithms, for a dataset, based on the past performance of the candidate algorithms in other datasets. Meta-learning is normally used in conventional, non-hierarchical classification. By contrast, this paper proposes a meta-learning approach for more challenging task of hierarchical classification, and evaluates it in a large number of bioinformatics datasets. Hierarchical classification is especially relevant for bioinformatics problems, as protein and gene functions tend to be organised into a hierarchy of class labels. This work proposes meta-learning approach for recommending the best hierarchical classification algorithm to a hierarchical classification dataset. This work’s contributions are: 1) proposing an algorithm for splitting hierarchical datasets into new datasets to increase the number of meta-instances, 2) proposing meta-features for hierarchical classification, and 3) interpreting decision-tree meta-models for hierarchical classification algorithm recommendation.

Keywords: algorithm recommendation, meta-learning, bioinformatics, hierarchical classification

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Authors: H. Aksas, S. Boughrara, K. Louhab

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The goal of this study was the use of life cycle assessment (LCA) methodology to assess the environmental impact of pharmaceutical product (four kinds of syrup form and tree kinds of pomade form), which are produced in one leader manufactory in Algeria town that is SAIDAL Company. The impacts generated have evaluated using SimpaPro7.1 with CML92 Method for syrup form and EPD 2007 for pomade form. All impacts evaluated have compared between them, with determination of the compound contributing to each impacts in each case. Data needed to conduct Life Cycle Inventory (LCI) came from this factory, by the collection of theoretical data near the responsible technicians and engineers of the company, the practical data are resulting from the assay of pharmaceutical liquid, obtained at the laboratories of the university. This data represent different raw material imported from European and Asian country necessarily to formulate the drug. Energy used is coming from Algerian resource for the input. Outputs are the result of effluent analysis of this factory with different form (liquid, solid and gas form). All this data (input and output) represent the ecobalance.

Keywords: pharmaceutical product, drug residues, LCA methodology, environmental impacts

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Authors: Janece Jean A. Polizon, Victor B. Amoroso

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Mt. Hamiguitan Range in Davao Oriental, Mindanao Island, Philippines is the only protected area with pygmy forest and a priority site for protection and conservation. This range harbors different vegetation types such as agroecosystem, dipterocarp forest, montane forest and mossy forest. This study was conducted to determine the diversity of trees and shrubs in different vegetation types of Mt. Hamiguitan Range. Transect walk and 16 sampling plots of 20 x 20 m were established in the different vegetation types. Specimens collected were classified and identified using the Flora Malesiana and type images. Assessment of status was determined based on International Union for the Conservation of Nature (IUCN). There were 223 species of trees, 141 genera and 71 families. Of the vegetation types, the pygmy forest obtained a comparatively high diversity value of H=1.348 followed by montane forest with H=1.284. The high species importance value (SIV) of Diospyros philippinensis for trees indicates that these species have an important role in regulating the stability of the ecosystem. The tree profile of the pygmy forest is different due to the ultramafic substrate causing the dwarfness of the trees. These forest types should be given high priority for protection and conservation.

Keywords: diversity, Mt Hamiguitan, vegetation, trees, shrubs

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Authors: Diego Saqui, José H. Saito, José R. Campos, Lúcio A. de C. Jorge

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Hyperspectral images and remote sensing are important for many applications. A problem in the use of these images is the high volume of data to be processed, stored and transferred. Dimensionality reduction techniques can be used to reduce the volume of data. In this paper, an approach to band selection based on clustering algorithms is presented. This approach allows to reduce the volume of data. The proposed structure is based on Fuzzy C-Means (or K-Means) and NWHFC algorithms. New attributes in relation to other studies in the literature, such as kurtosis and low correlation, are also considered. A comparison of the results of the approach using the Fuzzy C-Means and K-Means with different attributes is performed. The use of both algorithms show similar good results but, particularly when used attributes variance and kurtosis in the clustering process, however applicable in hyperspectral images.

Keywords: band selection, fuzzy c-means, k-means, hyperspectral image

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Authors: Teklu Urgessa, Wookjae Maeng, Joong Seek Lee

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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

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Authors: B. K. Gill, Himani Sharma, V. K. Rattan

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Experimental data of refractive index, excess molar volume and viscosity of binary mixture of morpholine with cumene over the whole composition range at 298.15 K, 303.15 K, 308.15 K and normal atmospheric pressure have been measured. The experimental data were used to compute the density, deviation in molar refraction, deviation in viscosity and excess Gibbs free energy of activation as a function of composition. The experimental viscosity data have been correlated with empirical equations like Grunberg- Nissan, Herric correlation and three body McAllister’s equation. The excess thermodynamic properties were fitted to Redlich-Kister polynomial equation. The variation of these properties with composition and temperature of the binary mixtures are discussed in terms of intermolecular interactions.

Keywords: cumene, excess Gibbs free energy, excess molar volume, morpholine

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Authors: Ehsan Mehryaar, Reza Bushehri

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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

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Authors: Priyanka Yadav, Rajkumar Ghosh, Alok Saini

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Human civilization cannot exist and thrive in the absence of adequate water. As a result, even in smart cities, water plays an important role in human existence. The key causes of this catastrophic water scarcity crisis are lifestyle changes, over-exploitation of groundwater, water over usage, rapid urbanization, and uncontrolled population growth. Furthermore, salty water seeps into deeper aquifers, causing land subsidence. The purpose of this study on artificial groundwater recharge is to address the water shortage in Gomti Nagar, Lucknow. Submersibles are the most common methods of collecting freshwater from groundwater in Gomti Nagar neighbourhood of Lucknow. Gomti Nagar area has a groundwater depletion rate of 1968 m3/day/km2 and is categorized as Zone-A (very high levels) based on the existing groundwater abstraction pattern - A to D. Harvesting rainwater using roof top rainwater harvesting systems (RTRWHs) is an effective method for reducing aquifer depletion in a sustainable water management system. Rainwater collecting using roof top rainwater harvesting systems (RTRWHs) is an effective method for reducing aquifer depletion in a sustainable water conservation system. Due to a water imbalance of 24519 ML/yr, the Gomti Nagar region is facing severe groundwater depletion. According to the Lucknow Development Authority (LDA), the impact of installed RTRWHs (plot area 300 sq. m.) is 0.04 percent of rainfall collected through RTRWHs in Gomti Nagar region of Lucknow. When RTRWHs are deployed in all buildings, their influence will be greater. Bye-laws in India have mandated the installation of RTRWHs on plots greater than 300 sq.m. A better India without any water problem is a pipe dream that may be realized by installing residential and commercial rooftop rainwater collecting systems in every structure. According to the current study, RTRWHs should be used as an alternate source of water to bridge the gap between groundwater recharge and extraction in smart city viz. Gomti Nagar, Lucknow, India.

Keywords: groundwater recharge, RTRWHs, harvested rainwater, rainfall, water extraction

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Authors: Mohammed Bakhit Kashoob, Mohammed Salim Al-Maashani, Ahmed Abdullah Al-Marhoon

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Fatalities of Road Traffic Crashes (RTCs) on rural roads are usually higher than that on urban roads. The likelihood of traffic accidents may increase with the presence of factors that are associated with the rural type of community such as long-distance, road type, road geometry (e.g., curves and steepens), poor lighting, terrain, obstacles (e.g., animals crossing, boulders or tree branches), heavy truck traffic, weather conditions, and road flaws. Most of these factors are present on the rural roads of Oman. As many cities in Oman are surrounded by mountains and connected by rural roads, this is of great concern. In this paper, the causes of traffic crashes on rural roads in Oman are analyzed. The fatality rate of traffic deaths on rural roads is compared with the fatality rate on urban roads for different regions in Oman. Statistical data and police reports show that the leading cause of RTCs and deaths on rural roads is vehicle speeding, especially on long-distance roads. It is shown that crashes on rural roads result in higher fatalities than crashes on urban roads. In comparison to speed, the numbers of RTCs and deaths that resulted from other causes are small.

Keywords: causes of traffic crashes, road safety, road traffic crash, rural roads

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Authors: Hamza A. Pantami, Khozizah Shaari, Intan S. Ismail, Chong C. Min

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Introduction: Tilapia is the second-highest harvested freshwater fish species in Malaysia, available in almost all fish farms and markets. Unfortunately, tilapia culture in Malaysia is highly affected by Aeromonas hydrophila and Streptococcus agalactiae, which affect the production rate and consequently pose a direct negative economic impact. Reliance on drugs to control or reduce bacterial infections has been led to contamination of water bodies and development of drug resistance, as well as gave rise to toxicity issues in downstream fish products. Resorting to vaccines have helped curb the problem to a certain extent, but a more effective solution is still required. Using microalgae-based feed to enhance the fish immunity against bacterial infection offers a promising alternative. Objectives: This study aims to evaluate the efficacy of Chlorella vulgaris at lower percentage incorporation in feeds for an immune boost of tilapia in a shorter time. Methods: The study was in two phases. The safety concentration studies at 500 mg/kg-1 and the administration of cultured C. vulgaris biomass via incorporation into fish feed for five different groups in three weeks. Group 1 was the control (0% incorporation), whereas group 2, 3, 4 and 5 received 0.625%, 1.25%, 2.5% and 5% incorporation respectively. The parameters evaluated were the blood profile, serum lysozyme activity (SLA), serum bactericidal activity (SBA), phagocytosis activity (PA), respiratory burst activity (RBA), and lymphoproliferation activity (LPA). The data were analyzed via ANOVA using SPSS (version 16). Further testing was done using Tukey’s test. All tests were performed at the 95% confidence interval (p < 0.05). Results: There were no toxic signs in tilapia fish at 500 mg/kg-1. Treated groups showed significantly better immune parameters compared to the control group (p < 0.05). Conclusions: C. vulgaris crude biomass in a fish meal at a lower incorporation level of 5% can increase specific and non-specific immunity in tilapia fish in a shorter time duration.

Keywords: Chlorella vulgaris, hematology profile, immune boost, lymphoproliferation

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Authors: Polona Dobnik Dubrovski, Abhijit Majumdar

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The increasing awareness of negative effects of ultraviolet radiation and regular, effective protection are actual themes in many countries. Woven fabrics as clothing items can provide convenient personal protection however not all fabrics offer sufficient UV protection. Porous structure of the material has a great effect on UPF. The paper is focused on an overview of porosity in woven fabrics, including the determination of porosity parameters on the basis of an ideal geometrical model of porous structure. Our experiment was focused on 100% cotton woven fabrics in a grey state with the same yarn fineness (14 tex) and different thread densities (to achieve relative fabric density between 59 % and 87 %) and different type of weaves (plain, 4-end twill, 5-end satin). The results of the research dealing with the modelling of UPF and the influence of volume and open porosity of tested samples on UPF are exposed. The results show that open porosity should be lower than 12 % to achieve good UV protection according to AS/NZ standard of tested samples. The results also indicate that there is no direct correlation between volume porosity and UPF, moreover, volume porosity namely depends on the type of weave and affects UPF as well. Plain fabrics did not offer any UV protection, while twill and satin fabrics offered good UV protection when volume porosity was less than 64 % and 66 %, respectively.

Keywords: fabric engineering, UV radiation, porous materials, woven fabric construction, modelling

Procedia PDF Downloads 265