scholarly journals Comparative analysis of the assortment structure of poplar clones I-214 and Pannonia

2020 ◽  
pp. 31-46
Author(s):  
Milorad Danilovic ◽  
Dragan Rakovic ◽  
Dusan Isajev ◽  
Slavica Antonic
Keyword(s):  
Data Collection ◽  
Second Phase ◽  
Detailed Measurement ◽  
Populus Euramericana ◽  
Poplar Clone ◽  
Poplar Clones ◽  
Poplar Trees ◽  
Significant Difference ◽  
Two Phases

Poplars occupy about 31.4 million ha in the world, while in Serbia poplars spread over the area of 48.000 ha. The subject of this research are artificially raised poplar plantations, consisting of poplar clone I-214 (Populus?euramericana (Dode) Guinier cl. I-214) and poplar clone Pannonia (Populus?euramericana (Dode) Guinier cl. Pannonia). Field activities of collecting data required for this research were conducted in two phases. The first phase of data collection included measurement of tree diameter. Also, the numbering, marking and recording of poplar rows, as well as each poplar in the row, was conducted. The second phase of data collection was conducted after the felling of trees that were selected for detailed measurement of the elements required for theoretical cross cutting. In accordance with the general principles of cross cutting, as well as the principles of maximum financial effect, the qualitative partition of trunks into several variants was performed. The classification of wood assortments was performed on the basis of SRPS wood standards. The share of technical wood for veneer (F and L class) in the analyzed poplar trees clone I-214 is 47.54% of the total volume of wood assortments. When it comes to the clone Pannonia, logs for cutting (quality class II), have the greatest share in total volume of wood assortments with 44.08. There is no statistically significant difference between the total volume and the value of the assortments of the two analyzed poplar clones, except when it comes to assortments for chemical exploitation where statistical differences exist.

scholarly journals Estudio de la enseñanza de ecosistemas amazónicos a través de la metodología de aprendizaje basada en la resolución de problemas (ABRP)

2018 ◽  
Vol 22 ◽  
pp. 9
Author(s):  
Carlos Henrique Nascimento ◽  
Ires Paula de Andrade Miranda
Keyword(s):  
Problem Solving ◽  
Data Collection ◽  
Primary School ◽  
School Environment ◽  
Problem Based Learning ◽  
Problem Formulation ◽  
Teaching Methodology ◽  
Second Phase ◽  
Two Phases ◽  
Selection Of

The purpose was to analyze the Problem-based learning (PBL) as a methodological alternative for primary school that favor learning about Amazonian ecosystems. This research is descriptive with a qualitative-quantitative approach. The study was carried out with students from the 9th year of primary school. The teaching methodology based on the PBL was applied in two phases: In the first phase, a test of previous conceptions was carried out in order to know the perception of the students on topics related to some units of landscapes of the Amazonian ecosystems. The second phase consisted of the implementation of the learning methodology in the school environment. Four different phases were established in the application: i) selection of topics; ii) problem formulation; iii) problem solving; iv) synthesis and evaluation. The data collection instruments used were: preconceptions test and skills chart. The results showed that after the application of the ABRP methodology, the cognitive recognition of the Amazonian ecosystems can be perceived in the students, reaching additional goals that the PCN establish.

scholarly journals CITYGML MODELLING FOR SINGAPORE 3D NATIONAL MAPPING

2017 ◽  
Vol XLII-4/W7 ◽  
pp. 37-42 ◽  
Author(s):  
K. H. Soon ◽  
V. H. S. Khoo
Keyword(s):  
Data Collection ◽  
3D Models ◽  
Second Phase ◽  
3D Mapping ◽  
Scanning Technique ◽  
Land Survey ◽  
Open Geospatial Consortium ◽  
Mobile Imaging ◽  
Two Phases

Since 2014, the Land Survey Division of Singapore Land Authority (SLA) has spearheaded a Whole-of-Government (WOG) 3D mapping project to create and maintain a 3D national map for Singapore. The implementation of the project is divided into two phases. The first phase of the project, which was based on airborne data collection, has produced 3D models for Relief, Building, Vegetation and Waterbody. This part of the work was completed in 2016. To complement the first phase, the second phase used mobile imaging and scanning technique. This phase is targeted to be completed by the mid of 2017 and is creating 3D models for Transportation, CityFurniture, Bridge and Tunnel. The project has extensively adopted the Open Geospatial Consortium (OGC)'s CityGML standard. Out of 10 currently supported thematic modules in CityGML 2.0, the project has implemented 8. The paper describes the adoption of CityGML in the project, and discusses challenges, data validations and management of the models.

scholarly journals Trajectory Design for UAV-Based Data Collection Using Clustering Model in Smart Farming

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 37
Author(s):  
Tariq Qayyum ◽  
Zouheir Trabelsi ◽  
Asad Malik ◽  
Kadhim Hayawi
Keyword(s):  
Data Collection ◽  
Base Station ◽  
Second Phase ◽  
Trajectory Design ◽  
Delay Sensitive ◽  
Clustering Model ◽  
Proposed Model ◽  
Smart Farming ◽  
Two Phases ◽  
Collection Phase

Unmanned aerial vehicles (UAVs) play an important role in facilitating data collection in remote areas due to their remote mobility. The collected data require processing close to the end-user to support delay-sensitive applications. In this paper, we proposed a data collection scheme and scheduling framework for smart farms. We categorized the proposed model into two phases: data collection and data scheduling. In the data collection phase, the IoT sensors are deployed randomly to form a cluster based on their RSSI. The UAV calculates an optimum trajectory in order to gather data from all clusters. The UAV offloads the data to the nearest base station. In the second phase, the BS finds the optimally available fog node based on efficiency, response rate, and availability to send workload for processing. The proposed framework is implemented in OMNeT++ and compared with existing work in terms of energy and network delay.

scholarly journals A new model for automatic text classification

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Hekmat Moumivand ◽  
Rasool Seidi Piri ◽  
Fatemeh Kheiraei
Keyword(s):  
Text Categorization ◽  
Learning Algorithm ◽  
Text Processing ◽  
Second Phase ◽  
Text Documents ◽  
Automatic Text Classification ◽  
Tree Algorithms ◽  
Standard Text ◽  
Two Phases

AbstractIn this paper, a new method for automatic classification of texts is presented. This system includes two phases; text processing and text categorization. In the first phase, various indexing criteria such as bigram, trigram and quad-gram are presented to extract the properties. Then, in the second phase, the W-SMO machine learning algorithm is used to train the system. In order to evaluate and compare the results of the two criteria of accuracy and readability, Macro-F1 and Micro-F1 have been calculated for different indexing methods. The results of experiments performed on 7676 standard text documents of Reuters showed that our proposed method has the best performance compared to the W-j48, Naïve Bayes, K-NN and Decision Tree algorithms.

scholarly journals COVID-19 Pneumonia Level Detection using Deep Learning Algorithm

2020 ◽  
Author(s):  
Kayhan Ghafoor
Keyword(s):  
Lung Inflammation ◽  
Nearest Neighbor ◽  
Learning Algorithm ◽  
Second Phase ◽  
K Nearest Neighbor ◽  
Deep Learning Algorithm ◽  
Severe Stage ◽  
Efficient Detection ◽  
Two Phases

The first COVID-19 confirmed case is reported in Wuhan, China and spread across the globe with unprecedented impact on humanity. Since this pandemic requires pervasive diagnosis, it is significant to develop smart, fast and efficient detection technique. To this end, we developed an Artificial Intelligence (AI) engine to classify the lung inflammation level (mild, progressive, severe stage) of the COVID-19 confirmed patient. In particular, the developed model consists of two phases; in the first phase, we calculate the volume and density of lesions and opacities of the CT images of the confirmed COVID-19 patient using Morphological approaches. In the second phase, the second phase classifies the pneumonia level of the confirmed COVID-19 patient. To achieve precise classification of lung inflammation, we use modified Convolution Neural Network (CNN) and k-Nearest Neighbor (kNN). The result of the experiments show that the utilized models can provide the accuracy up to 95.65\% and 91.304 \% of CNN and kNN respectively.<br>

scholarly journals Emergence of Multi-Drug Resistant Clinical Strains of Staphylococcus aureus

2016 ◽  
pp. 1-6
Author(s):  
B Saha ◽  
M Bal
Keyword(s):  
Staphylococcus Aureus ◽  
Comparative Analysis ◽  
Methicillin Resistance ◽  
Aureus Strain ◽  
Resistance Pattern ◽  
Second Phase ◽  
Drug Resistant ◽  
Clinical Strains ◽  
Significant Difference ◽  
Two Phases

Indiscriminate and wide spread use of antibiotics has lead to the development of multi-drug-resistant strains of pathogenic Staphylococcus aureus. Information regarding increase (in percentage) of existing resistance as well as emergence of new resistance to different antibiotics used for staphylococcal infections are insufficient. This study explores a comparative analysis of growing resistance to different antibiotics mainly ampicillin, methicillin, erythromycin, gentamicin, clindamycin and vancomycin against S. aureus isolated from Kolkata hospitals during two phases. During first phase (126) and second phase (67) non-repeat clinical strains of S. aureus obtained from different hospitals of Kolkata were identified by standard biochemical methods. However, PCR amplification of nuc gene and rDNA was also performed for identification of S. aureus. Antibiotic susceptibility pattern was determined by Disc Agar Diffusion tests and mecA was identified by PCR. Comparative analysis of antibiotic resistance pattern of the strains isolated during two phases showed significant difference (p=0.05) with 75% increase of resistance to erythromycin followed by 30% increase to ampicillin, chloramphenicol and streptomycin with the appearance of vancomycin resistance. Gentamicin and methicillin resistance have increased by 22% and 7% respectively. On the other hand, mecA was obtained by PCR from vancomycin resistant S. aureus strain, which was also resistant to methicillin, erythromycin and clindamycin. This study reveals tremendous increase of resistance to erythromycin and a remarkable increase to other antibiotics with emergence of multidrug- resistant clinical strains of S. aureus. This trend in increasing resistance to the commonly used antibiotics against S. aureus cannot be controlled until and unless antibiotics are used more prudently.International Journal of Natural Sciences (2013), 3(1-4) 1-6

Long Term Consistency of Handwriting Grip Kinetics in Adults

2014 ◽  
Vol 136 (4) ◽  
Author(s):  
Bassma Ghali ◽  
Khondaker A. Mamun ◽  
Tom Chau
Keyword(s):  
Data Collection ◽  
Grip Force ◽  
Statistical Tests ◽  
Relative Magnitude ◽  
Clinical Applications ◽  
Misclassification Rate ◽  
Second Phase ◽  
Two Phases ◽  
Over Time

While there is growing interest in clinical applications of handwriting grip kinetics, the consistency of these forces over time is not well-understood at present. In this study, we investigated the short- and long-term intra-participant consistency and inter-participant differences in grip kinetics associated with adult signature writing. Grip data were collected from 20 adult participants using a digitizing tablet and an instrumented pen. The first phase of data collection occurred over 10 separate days within a three week period. To ascertain long-term consistency, a second phase of data collection followed, one day per month over several months. In both phases, data were collected three times a day. After pre-processing and feature extraction, nonparametric statistical tests were used to compare the within-participant grip force variation between the two phases. Participant classification based on grip force features was used to determine the relative magnitude of inter-participant versus intra-participant differences. The misclassification rate for the longitudinal data were used as an indication of long term kinetic consistency. Intra-participant analysis revealed significant changes in grip kinetic features between the two phases for many participants. However, the misclassification rate, on average, remained stable, despite different demarcations of training, and testing data. This finding suggests that while signature writing grip forces may evolve over time, inter-participant kinetic differences consistently exceeds within-participant force changes in the long-term. These results bear implications on the collection, modeling and interpretation of grip kinetics in clinical applications.

Efficacy of amitriptyline as a pharmacological adjunct to behavioral modification in the management of aggressive behaviors in dogs

2001 ◽  
Vol 37 (4) ◽  
pp. 325-330 ◽  
Author(s):  
V Virga ◽  
KA Houpt ◽  
JM Scarlett
Keyword(s):  
Behavior Modification ◽  
Controlled Trial ◽  
Response To Treatment ◽  
Second Phase ◽  
Behavioral Modification ◽  
Aggressive Behaviors ◽  
Double Blind ◽  
Significant Difference ◽  
Placebo Phase ◽  
Two Phases

The efficacy of amitriptyline as a pharmacological adjunct to behavioral modification in the clinical management of aggressive behaviors in dogs was evaluated in two phases. Twelve dogs presenting for aggressive behaviors were treated sequentially with amitriptyline (2 mg/kg body weight, per os [PO] bid) and a placebo for 4 weeks in a prospective, randomized, double-blind, placebo-controlled trial. Standardized protocols for behavior modification were implemented throughout the trial. Owners maintained behavioral records and reported on the number of aggressive incidents as well as the dog's overall improvement at the end of each 4-week period. In the second phase, 27 cases of dogs presenting for aggressive behaviors and treated with amitriptyline were reviewed, and clients were contacted to record each dog's response to treatment. Reports were compared to those for dogs receiving behavior modification alone (i.e., placebo phase of prospective study). No significant difference was observed in the patients' responses to adjunctive amitriptyline versus behavior modification alone.

scholarly journals COVID-19 Pneumonia Level Detection using Deep Learning Algorithm

2020 ◽  
Author(s):  
Kayhan Ghafoor
Keyword(s):  
Lung Inflammation ◽  
Nearest Neighbor ◽  
Learning Algorithm ◽  
Second Phase ◽  
K Nearest Neighbor ◽  
Deep Learning Algorithm ◽  
Severe Stage ◽  
Efficient Detection ◽  
Two Phases

The first COVID-19 confirmed case is reported in Wuhan, China and spread across the globe with unprecedented impact on humanity. Since this pandemic requires pervasive diagnosis, it is significant to develop smart, fast and efficient detection technique. To this end, we developed an Artificial Intelligence (AI) engine to classify the lung inflammation level (mild, progressive, severe stage) of the COVID-19 confirmed patient. In particular, the developed model consists of two phases; in the first phase, we calculate the volume and density of lesions and opacities of the CT images of the confirmed COVID-19 patient using Morphological approaches. In the second phase, the second phase classifies the pneumonia level of the confirmed COVID-19 patient. To achieve precise classification of lung inflammation, we use modified Convolution Neural Network (CNN) and k-Nearest Neighbor (kNN). The result of the experiments show that the utilized models can provide the accuracy up to 95.65\% and 91.304 \% of CNN and kNN respectively.<br>

DATA COLLECTION AND CONSTRUCTIVE CLASSIFICATION OF SUPERADOBE BUILDINGS

2016 ◽  
Vol 2 (2) ◽  
pp. 208 ◽  
Author(s):  
Deborah Macêdo Santos ◽  
José Nuno Dinis Cabral Beirão
Keyword(s):  
Data Collection

<p>As construções em terra são soluções reconhecidas de baixo impacto ambiental. São construções duráveis, fortes, climaticamente eficientes, formalmente flexíveis e são compostas por recursos renováveis e reaproveitáveis favorecendo o desenvolvimento sustentável. Este artigo classifica as variações construtivas de aplicação da técnica de construção em terra superadobe. Também conhecido como “adobe ensacado”, “saco contínuo de terra estabilizada”, “<em>earthbag building</em>” ou “<em>Earth-filled bags</em>”, o superadobe consiste na técnica construtiva onde as paredes são construídas basicamente por sacos preenchidos com terra e areia empilhados, com arame farpado entre eles. A técnica foi desenvolvida como possível solução de construção na lua, depois foi aplicada pare resolver a problemática de habitação popular, atualmente é possível encontrar construções em superadobe robustas, com diferentes usos e com associações de outras técnicas construtivas. Este artigo tem por objetivo tabular as variações construtivas de aplicação da técnica de construção em terra superadobe já executadas, a fim de auxiliar pesquisas futuras no reconhecimento e superação dos limites e variações da técnica construtiva. O método é descritivo qualitativo, com investigação de cunho exploratório interdisciplinar, por meio de levantamento técnico em revistas especializadas em arquitetura, engenharia e sustentabilidade.</p><p><strong>Palavras-Chave:</strong> Superadobe, sustentabilidade, arquitetura, construção em terra.</p>

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